Grouping Genetic Algorithm for Solving the Server Consolidation ...

Grouping Genetic Algorithm for Solving the Server Consolidation Problem with Conflicts Shubham Agrawal Operations Research and Industrial Engg., Dept. of Mechanical Engg. University of Texas at Austin Austin, Texas, USA [email protected]

Sumit Kumar Bose Distributed Computing Lab Software Engg. & Technology Labs Infosys Technologies Ltd, Bangalore, India [email protected]

Srikanth Sundarrajan Distributed Computing Lab Software Engg. & Technology Labs Infosys Technologies Ltd, Bangalore, India [email protected]

ABSTRACT The advent of virtualization technologies encourages organizations to undertake server consolidation exercises for improving the overall server utilization and for minimizing the capacity redundancy within data-centers. Identifying complimentary workload patterns is a key to the success of server consolidation exercises and for enabling multi-tenancy within data-centers. Existing works either do not consider incompatibility constraints or performs poorly on the disjointed conflict graphs. The algorithm proposed in the current work overcomes the limitations posed by the existing solutions. The current work models the server consolidation problem as a vector packing problem with conflicts (VPC) and tries to minimize the number of servers used for hosting applications within datacenters and maximizes the packing efficiency of the servers utilized. This paper solves the problem using techniques inspired from grouping genetic algorithm (GGA) - a variant of the traditional Genetic Algorithm (GA). The algorithm is tested over varying scenarios which show encouraging results

Evolved Finite State Controller for Hybrid System Jean-François Dupuis Technical University of Denmark Nils Koppels Alle, Building 424 2800 Kgs. Lyngby, Denmark

[email protected] Zhun Fan Technical University of Denmark Nils Koppels Alle, Building 424 2800 Kgs. Lyngby, Denmark

[email protected] Erik Goodman Michigan State University 2120 Engineering Building

East Lansing, MI, USA 48824

[email protected] ABSTRACT This paper presents an evolutionary methodology to automatically generate _nite state automata (FSA) controllers to control hybrid systems. FSA controllers for a case study of two-tank system have been successfully obtained using the proposed evolutionary approach. Experimental results show that these controllers have good performance on the set of training targets as well as on a randomly generated set of validation targets.

An improved Simulated Annealing Algorithm for Vector Quantizer Design Mengyu Zhu Department of Electronic Engineering, Beijing Institute of Technology 5 South Zhongguancun Street, Beijing, 100081, China

[email protected] Yuliang Yang Department of Communication Engineering, School of Information Engineering, University of Science and Technology Bejing 30 Xueyuan Road, Beijing 100081, China Beijing, China

[email protected] ABSTRACT An improved Simulated Annealing algorithm in conjunction with GLA algorithm has been proposed in this paper. Using SA algorithm and new distortion measure, our new algorithm can avoid the GLA algorithm's defect in that is sensitive to the original codebook and is easy to fall into the locally optimal solution during the searching. The experiment results indicate that the improved algorithm can efficiently eliminate the sensibility to the original codebook, and improve performance for searching ability and subjective quality of decoding image.

Optimizing Constrained Non-convex NLP Problems in Chemical Engineering Field by a Novel Modified Goal Programming Genetic Algorithm Cuiwen Cao East China Univ. of Sci. & Tech. 130 Meilong Road Xuhui District, Shanghai, China 86-21-64252576 [email protected]

Jinwei Gu East China Univ. of Sci. & Tech. 130 Meilong Road Xuhui District, Shanghai, China 86-21-64252576 [email protected]

Bin Jiao Shanghai Dianji Univ.

690 Jiangchuang Road Minhang District, Shanghai, China 86-21-54758615 [email protected]

Zhong Xin East China Univ. of Sci. & Tech. 130 Meilong Road Xuhui District, Shanghai, China 86-21-64252576 [email protected]

Xingsheng Gu* East China Univ. of Sci. & Tech. 130 Meilong Road Xuhui District, Shanghai, China 86-21-64252576 [email protected]

ABSTRACT A novel modified goal programming genetic algorithm (MGPGA) is presented in this paper to solve constrained non-convex nonlinear programming (NLP) problems. This new method eliminates the complex equality constraints from original model and transforms them as parts of goal functions with higher priority weighting factors. At the same time, the original objective function has the lowest priority weighting factor. After all the absolute deviations of these equality constraints objectives are minimized, the final optimized solutions can be gained. Some applications in chemical engineering field are tested by this MGPGA. The proposed MGPGA demonstrates its advantages in better performances and abilities of solving non-convex NLP problems especially for those with equality constraints

Search-based Multi-paths Test Data Generation for Structure-oriented Testing Yang Cao Tsinghua University School of Aerospace Beijing, China

[email protected] Chunhua Hu Tsinghua University School of Aerospace Beijing, China

[email protected] Luming Li Tsinghua University School of Aerospace Beijing, China

[email protected] ABSTRACT This paper presents a new fitness function to generate test data for a specific single path, which is different from the predicate distance applied by most test data generators based on genetic algorithms (GAs). We define a similarity between the target path and execution path to evaluate the quality of the populations. The problem of the most existing generators is to search only one target data a time, wasting plenty of available interim data. We construct another fitness function combined with the single path

function, which can drive GA to complete covering multi-paths to avoid the reduplicate searching and utilize the interim populations for different paths. Several experiments are taken to examine the effectiveness of both the single path and multi-path fitness functions, which evaluate the functions’ performance with the convergence ability and consumed time. Results show that the two functions perform well compared with other two typical path-oriented functions and the multi-paths approach retrenches the searching actually

A Hybrid Neural-genetic Approach for Reconfigurable Scheduling of Networked Control System Hui Chen Key Laboratory of Ministry of Education for Image Processing & Intelligent Control Dept. of Control Science & Engineering, Huazhong University of Science and Technology Wuhan, Hubei, China, 430074 Phone: +86 027 87558001

[email protected] Chunjie Zhou Key Laboratory of Ministry of Education for Image Processing & Intelligent Control Dept. of Control Science & Engineering, Huazhong University of Science and Technology Wuhan, Hubei, China, 430074 Phone: +86 027 87558001

[email protected] Weifeng Zhu Key Laboratory of Ministry of Education for Image Processing & Intelligent Control Dept. of Control Science & Engineering, Huazhong University of Science and Technology Wuhan, Hubei, China, 430074 Phone: +86 027 87558001

[email protected] ABSTRACT In this paper, a novel approach for networked control system (NCS) task scheduling is proposed. The proposed neural-genetic method utilizes the information about the quality of service (QoS) over the communication network and enables online reconfigurable scheduling on distributed environment. In this way the NCS’s bandwidth can be shared properly among different parallel control tasks. For NCS, two significant factors of QoS that affect validity of scheduling results are the packet loss and delay, which occurred in the communication among tasks. By adopting a Elman neural network based prediction model, the one-step ahead packet loss

and time delay are obtained. The knowledge about the predict QoS factors, combined with the task execution features and the resources available in the system, are used as an entry to improve the decisions of the proposed scheduling algorithm. Such algorithm uses genetic algorithm techniques to find out the appropriate task scheduling scheme to adapt changes of application and communication circumstance. The proposed neural-genetic approach is evaluated through simulation by using a model parameterized with the features obtained from a real scenario of Ethernet based control system. The simulation results clearly show the effectiveness of the proposed approach in solving the task scheduling problems in NCS.

A Genetic Approach to Channel Assignment for Multiradio Multi-channel Wireless Mesh Networks Jian Chen, Jie Jia School of Information Science and Engineering Northeastern University Shenyang 110004, China

[email protected] Yingyou Wen School of Information Science and Engineering Northeastern University Shenyang 110004, China

[email protected] Dazhe Zhao, Jiren Liu National Engineering Research Center for Computer Software Northeastern University Shenyang 110004, China

[email protected] ABSTRACT Multi-channel communication in a Wireless Mesh Network with routers having multiple radio interfaces significantly enhances the network capacity. Efficient channel assignment is critical for realization of optimal throughput in such networks. In this paper, we investigate the problem of finding the largest number of links that can be connected with the overall network interference is minimized. Since the number of radios on any node can be less than the number of available channels, the channel assignment must obey the constraint that the number of different channels assigned to the links incident on any node is at most the number of radio interfaces on that node. The above optimization problem is known to be NP-hard. By presenting the theoretical model, the above task is formulated as a multi-objective problem, and then a novel channel assignment based on improved NSGA-II is proposed. Extensive empirical evaluations represent that the novel algorithm proposed in this paper can implement network connectivity with little interference rapidly and efficiently. To meet the actual demand in wireless mesh network, ns-2 simulations are used to demonstrate the performance potential of our channel assignment algorithms in 802.11-based multi-radio mesh networks.

Modeling and Extending Lifetime of Wireless Sensor Networks Using Genetic Algorithm Jian Chen, Jie Jia School of Information Science and Engineering Northeastern University Shenyang 110004, China

[email protected] Yingyou Wen School of Information Science and Engineering Northeastern University Shenyang 110004, China

[email protected] Dazhe Zhao, Jiren Liu National Engineering Research Center for Computer Software Northeastern University Shenyang 110004, China

[email protected] ABSTRACT To extend the lifetime of the sensor networks as far as possible while maintaining the quality of network coverage is a major concern in the research of coverage control. A systematical analysis on the relationship between the network lifetime and cover sets alternation is given, and by introducing the concept of time weight factor, the network lifetime maximization model is presented. Through the introduction of the solution granularity ΔT, the network lifetime optimization problem is transformed into the maximization of cover sets. A solution based on NSGA-II is proposed. Compared with the previous method, which has the additional requirement that the cover sets being disjoint and results in a large number of unused nodes, our algorithm allows the sensors to participate in multiple cover sets, and thus makes fuller use of the whole sensor nodes to further increase the network lifetime. Simulation results are presented to verify these approaches.

Particle Swarm Optimization Algorithm Based on Dynamic Memory Strategy Qiong Chen School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China

[email protected] Shengwu Xiong ¤

School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China

[email protected] Hongbing Liu

School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China

[email protected] ABSTRACT This paper mainly studies the in°uence of memory on individual performance in particle swarm system. Based on the observation of social phenomenon from the perspective of social psychology, the concept of individual memory contribution is de¯ned and several measurement methods to determine the level of e®ect of individual memory on its behavior are discussed. A dynamic memory particle swarm optimization algorithm is implemented by dynamically assigning appropriate weight to each individual's memory according to the selected metrics values. Numerical experiment results on benchmark optimization function set show that the proposed scheme can e®ectively adjust the weight of individual memory according to di®erent optimization problems adaptively. Numerical results also demonstrate that dynamic memory is an e®ective improvement strategy for preventing premature convergence in particle swarm optimization algorithm.

The Non-Clique Particle Swarm Optimizer Ziyu Chen College of Computer Science Chongqing University Chongqing 400044, China

[email protected] Zhongshi He College of Computer Science Chongqing University Chongqing 400044, China

[email protected] Cheng Zhang College of Computer Science Chongqing University Chongqing 400044, China

[email protected] ABSTRACT Neighborhood topology of particle swarm affects the performance of PSO. Through analyzing the graph properties of typical neighborhood topologies, this paper presents a non-clique static neighborhood topology which has lower clustering coefficient and smaller average path length. Compared to other topologies with the same neighborhood size and population size, the proposed topology has more uniform neighbor distribution. The experiment results demonstrate that the PSO based on the non-clique topology has great superiority both in robustness and efficiency. 、

A Co-Evolutionary Approach for Military Operational Analysis Choo, Chwee Seng DSO National Laboratories

20, Science Park Drive Singapore 118230 65-67727125

[email protected] Chua, Ching Lian DSO National Laboratories 20, Science Park Drive Singapore 118230 65-67727376

[email protected] Low, Kin Ming Spencer DSO National Laboratories 20, Science Park Drive Singapore 118230 65-67727376

[email protected] Ong, Wee Sze Darren Defence Science and Technology Agency 71, Science Park Drive Singapore 118253 65-68795077

[email protected] ABSTRACT In this paper, we describe Automated Co-Evolution (ACE), a framework that uses Competitive Co-Evolutionary Algorithm (CCEA) and High Performance Computing (HPC), to study the dynamics of competition in a military context through simulations. The overall goal is to complement the manually intensive actions-reactions process in developing (automatically) a Blue plan that performs well and is relatively robust even in the face of an adaptive Red adversary. The design of key components and techniques that are required to develop the ACE framework are described and discussed. An academic study using a military scenario - Maritime Anchorage Protection, was conducted and the results analyzed to demonstrate the capability of the ACE framework. It also illustrated how the ACE process could be used to complement Operational Analysis (OA).

Evolving Common LISP Programs in a Linear-Genotype Evolutionary Computation System Jamie S. Cullen Artificial Intelligence Laboratory University of New South Wales, Sydney NSW

[email protected] ABSTRACT Evolutionary Meta Programming (EMP) is an approach to Evolutionary Computation, which allows freedom of programming language choice in the evolved programs, as well as the ready use of external tools and testbenches, with which to perform fitness evaluation. The current implementation of EMP uses a linear genotype in a manner similar to Grammatical Evolution (GE). In contrast, traditional Genetic Programming (GP) typically uses a subset of the LISP programming language to represent target programs in a tree-based structure. The ability of EMP to leverage external tools and arbitrary languages enables the rapid pro-

totyping of possibly novel approaches to Evolutionary Computation. One such experiment is presented herein: The evolution of Common LISP language constructs using a linear genotype and associated grammar, and evaluation using a real external LISP interpreter. An exploratory study is performed with three classic problems: Symbolic Regression, Ant Trail, and Towers of Hanoi. Solutions to these problems were evolved in both Common LISP and ANSI C versions, and runtime and performance results collected. Present results are relatively unintuitive, when compared to conventional programming wisdom, with some problems apparently favoring a paradigm not traditionally suited to them in a non-evolutionary programming setting.

Ant Colony Optimization for Precedence-Constrained Heterogeneous Multiprocessor Assignment Problem Rong Deng Department of Computer Science and Engineering, Tongji University, 4800 Caoan Road, ShangHai,China,201804 86+21+69589864

[email protected] Changjun Jiang Department of Computer Science and Engineering, Tongji University, 4800 Caoan Road, ShangHai,China,201804 86+21+69589864

[email protected] Fei Yin Department of Computer Science and Engineering, Tongji University, 4800 Caoan Road, ShangHai,China,201804 86+21+69589864

[email protected] ABSTRACT An ant colony optimization approach, named MPAACO, for the Precedence-Constrained Heterogeneous Multiprocessor Assignment Problem (PCHMAP) is presented. The main characteristics of MPAACO are novel pheromone matrix and solution construction scheme. Separating processor selection steps from task selection steps, ant colony has full flexibility to construct new solution. Three-dimensional pheromone matrix can record each solution construction step precisely. When combined with heuristic information, they endow MPAACO the ability to find high quality schedules of PCHMAP quickly. We tested the algorithm on a set of benchmark problems from the [18]. The result shows that for 77% of all benchmark for PrecedenceConstrained Homogeneous Multiprocessor Assignment Problem, a special case of PCHMAP, the algorithm can get the optimal in just one try. For PCHMAP problems, MPAACO outperforms other algorithms significantly.

Genetic Programming for Quantitative Stock Selection

Ying L. Becker US/Global Active Equity Research State Street Global Advisors Boston, MA 02111 (617) 664 - 2907

[email protected] Una-May O’Reilly CSAIL Massachusetts Institute of Technology Cambridge, MA, USA (617) 253 - 6437

[email protected] ABSTRACT We provide an overview of using genetic programming (GP) to model stock returns. Our models employ GP terminals (model decision variables) that are financial factors identified by experts. We describe the multi-stage training, testing and validation process that we have integrated with GP selection to be appropriate for financial panel data and how the GP solutions are situated within a portfolio selection strategy. We share our experience with the pros and cons of evolved linear and nonlinear models, and outline how we have used GP extensions to balance different objectives of portfolio managers and control the complexity of evolved models.

Multi-Strategy Grouping Genetic Algorithm for the Pickup and Delivery Problem with Time Windows Ding Genhong Hohai University Nanjing, 210098 People’s Republic of China 86-25-83786626

[email protected] Li Linye Hohai University Nanjing, 210098 People’s Republic of China 86-15850589547

[email protected] Ju Yao Hohai University Nanjing, 210098 People’s Republic of China 86-15950523806

[email protected] ABSTRACT The Pickup and Delivery Problem with Time Windows (PDPTW) is a generalization of the well studied Vehicle Routing Problem with Time Windows (VRPTW). This paper studies a Grouping Genetic Algorithm for solving the PDPTW. The insertionsearching heuristics (in GGA) which can generate feasible solutions was improved, new data structures were built, and then three routing adjustment strategies were added to come up with the Multi-Strategy Grouping Genetic Algorithm (MSGGA). The PDPTW benchmark problems with 100 customers are calculated with MSGGA, and the comparison between the result and that of the reference shows that the new algorithm shortens the calculating time with its astringency, better solutions of four cases

are obtained and stability is improved.

Self-Fertilization Based Genetic Algorithm for University Timetabling Problem Zan Wang School of Management of Tianjin University, Information and Network Center of Tianjin University Room 208, Information and Network Center of Tianjin University, Tianjin, China (+8622)27401113 [email protected]

Jin-lan Liu School of Management of Tianjin University Room 220, Building 9th of Tianjin University, Tianjin, China (+8622)87401927 [email protected]

Xue Yu School of Management of Tianjin University Room A-1108, Building 25th of Tianjin University, Tianjin, China (+8622)27401021 [email protected]

ABSTRACT In this paper, a new algorithm inspired from the self-fertilization of some plants is proposed for the university timetabling problem (UTP). The main idea of the algorithm is to modify the fitness function, the selection and crossover operators of GA to obtain a further fit for UTP. Fitness function of this algorithm will neglect hard constraints because no infeasible individual can pass the check of advisor to survive. The advisor based on heuristic methods can also simplify the computation once there are changes on constraints. Distinguished from traditional crossover, a new exchange in one chromosome rather than between chromosomes will be issued to keep the integrity of the schedule. During some processes, simulated annealing was introduced as a select strategy for diversity of the population. This algorithm was implemented and tested with the real data of Tianjin University, China. The algorithm produces good timetable for the students and teachers and improve the usage rate of classroom. The experiment results indicate that our new hybrid genetic algorithm that addressing timetabling problem is promising and converge rapidly.

Self-Fertilization Based Genetic Algorithm for University Timetabling Problem Zan Wang School of Management of Tianjin University, Information and Network Center of Tianjin University Room 208, Information and Network Center of Tianjin University, Tianjin,

China (+8622)27401113 [email protected]

Jin-lan Liu School of Management of Tianjin University Room 220, Building 9th of Tianjin University, Tianjin, China (+8622)87401927 [email protected]

Xue Yu School of Management of Tianjin University Room A-1108, Building 25th of Tianjin University, Tianjin, China (+8622)27401021 [email protected]

ABSTRACT In this paper, a new algorithm inspired from the self-fertilization of some plants is proposed for the university timetabling problem (UTP). The main idea of the algorithm is to modify the fitness function, the selection and crossover operators of GA to obtain a further fit for UTP. Fitness function of this algorithm will neglect hard constraints because no infeasible individual can pass the check of advisor to survive. The advisor based on heuristic methods can also simplify the computation once there are changes on constraints. Distinguished from traditional crossover, a new exchange in one chromosome rather than between chromosomes will be issued to keep the integrity of the schedule. During some processes, simulated annealing was introduced as a select strategy for diversity of the population. This algorithm was implemented and tested with the real data of Tianjin University, China. The algorithm produces good timetable for the students and teachers and improve the usage rate of classroom. The experiment results indicate that our new hybrid genetic algorithm that addressing timetabling problem is promising and converge rapidly.

Research on Stronger Convergence in Probability of Immune Genetic Algorithm Luo Xiaoping Zhejiang University City College Department of Electrical Engineering Hangzhou,Zhejiang,310015, P.R.China

[email protected] Peng Yonggang* College of Electrical Engineering Zhejiang University Hangzhou,Zhejiang,310027, P.R.China [email protected]

Wei Wei College of Electrical Engineering Zhejiang University Hangzhou,Zhejiang,310027, P.R.China [email protected]

ABSTRACT

Immune Genetic Algorithm (IGA) is a new optimization strategy by simulating the behavior of biological immune system. Aiming at the relatively scarce work on the discussion of convergence on IGA, strong convergence in probability of IGA was proved on the condition that the time tended to infinity comparing to the previous conclusion that IGA was weak convergence in probability by (1)modeling the immune operators and optimization process and (2)introducing a lemma with 2 immune parameters to analyze some characteristics of the complement set of global optima set. This conclusion will be helpful to understand the performance of IGA and set better immune parameters.

To Create Neuro-Controlled Game Opponent from UCTCreated Data Fan Xie, Suoju He, Xiao Liu, Xingguo Li, Junping Du, Jiajian Yang, Yiwen Fu, Yang Chen, Junping Wang, Zhiqing Liu, Qiliang Zhu Beijing University of Posts and Telecommunications, Beijing, China, 100876 [email protected], [email protected]

ABSTRACT Adaptive Game AI improves adaptability of opponent AI as well as the challenge level of the gameplay, as a result the entertainment of game is augmented. Opponent game AI is usually implemented by scripted rules in video games, but the most updated algorithm of UCT (Upper Confidence bound for Trees) which perform well in computer go can also be used to achieve excellent result to control non-player characters (NPCs) in video games. However, due to computational intensiveness of UCT, it is actually not suitable for Online Games. As it is already known that UCT can create near optimal control, so it is possible to create Neuro-Controlled Game Opponent by off-line learning from the UCT created sample data; finally Neuro-Controlled Game Opponent for Online Games from UCT-Created Data without worry about computational intensiveness is generated. And also if the optimization approach of Neuro-Evolution is applied to the above generated Neuro-Controller, the performance of the opponent AI is enhanced even further.

Efficient Annealing -Inspired Genetic Algorithm for Information Retrieval from Web-Document Yuan Xu Software School Dalian University of Technology, Dalian, Liaoning Province, 116023 China +086-138-4084-6152

[email protected] Yang Deli Software School, Dalian University of Technology Dalian, Liaoning Province, 116023 China +086-138-4084-6152

[email protected] Liu Yu Software School Dalian University of Technology

Dalian, Liaoning Province, 116023 China +086-138-4084-6152

[email protected] ABSTRACT With the huge amount of information available online, the World Wide Web is a fertile area for data mining research. The Web mining research is at the cross road of research from several research is at the cross road of research from several research communities. In this paper, a new adaptive method of mining web documents is proposed. We give an algorithm which combines genetic algorithm and simulated annealing based on vector space model. This algorithm avoids the disadvantage of web documents by using pure genetic algorithm which can not be utilized accurately .Experimental results indicate that this adaptive method significantly improves the performance in retrieval accuracy.

Controlling Swarm Robots with Kinematic Constraints for Target Search Songdong Xue † Complex Syst. & Computational Int. Lab Taiyuan University of Science and Technology 66 Waliu Rd., Taiyuan 030024, China ‡ Col. of Elect. & Informat. Engn. Lanzhou University of Technology 85 Langongping, Lanzhou 730050, China

[email protected] Jianchao Zeng Complex Syst. & Computational Int. Lab Taiyuan University of Science and Technology 66 Waliu Rd., Taiyuan, Shanxi 030024, China

[email protected] ABSTRACT An approach to control artificial swarm whose members are autonomous wheeled mobile robots is proposed, by applying Particle Swarm Optimization (PSO) to target search. First, swarm search is mapped to PSO based on similarities between the two cases. Then a distributed PSO-style algorithm is given, in which decision making on real inputs of linear and angular velocity of robot controller being explored. We obtain the required command sequences by constraining the computational expected velocities and positions with robot’s non-holonomic properties in kinematics. In this way, swarm robots can work together cooperatively.

Frame-layer Rate Control Algorithm for Multi-view Video Coding Tao Yan1,2 Liquan Shen1,2 Ping An1,2 He Wang1 Zhaoyang Zhang1,2 1School

of Communication and Information Engineering Laboratory of Advanced Displays and System Application, Ministry of Education Shanghai University Shanghai, China 021-56332183

2Key

[email protected] ABSTRACT Rate control has not been well studied for multi-view video coding (MVC). We propose a rate control algorithm for MVC

based on the quadratic rate-distortion model. We remodel the quadratic rate-distortion model for multi-view videos based on the type of each frame. In the frame level, the quantization parameters are set according to the parameters of the various kinds of image model which is set up through the analysis of the coded information. The experimental results show that the proposed scheme can allocate the bits and control the rate efficiently.

Research of Fuzzy Control Strategy on Artificial Climate Chest Yang Yang School of Automation, Hangzhou Dianzi University Hangzhou,Zhejiang,310018,P.R.China

[email protected] Luo Xiaoping* Zhejiang University City College Hangzhou Zhejiang,310015,P.R.China [email protected]

Peng Yonggang, Wei Wei College of Electrical Engineering Zhejiang University Hangzhou,Zhejiang,310027,P.R.China

{pengyg,wwei}@zju.edu.cn ABSTRACT Aiming at the lack of effective control strategies about a nonlinear, strong coupling and long time delay object---artificial climate chest, a new adaptive control method is proposed based on fuzzy theory. An improved fuzzy controller which can selfadjust parameters on-line is designed. Furthermore, it is proved that the control strategy in this paper is effective and superior with fuzzy set theory, multi-variable Fourier Transform and approximate theory by analyzing the essential model of fuzzy controller. Last, the results of experiments show that the method proposed in this paper can control temperature and humidity in artificial climate chest better. The results of this paper can be helpful in understanding fuzzy control more deeply and directing how to design fuzzy controller for complicated systems.

Optimal Multi-objective Design of Power System Damping Controller Using Synergy of Bacterial Forging and Particle Swarm Optimization Sun Yong Harbin Institute of Technology Harbin, China

[email protected] Li Zhimin Harbin Institute of Technology Harbin, China

[email protected]

Zhang Dongsheng Harbin Institute of Technology Harbin, China

[email protected] ABSTRACT In order to solve the parameter optimization problem of traditional power system stabilizer, a novel power system stabilizer (PSS) design method is proposed based on synergy of bacterial forging and particle swarm optimization algorithm. Bacterial foraging algorithm may lead to delay in reaching global solution. Particle swarm optimization may lead to entrapment in local minimum solution and obtain imprecise search results. The new algorithm is proposed to combines both algorithms’ advantages in order to get better optimization values. A coordinate optimization index based on multi-object and multiple operation conditions is presented so as to improve the damping ratios of electromechanical modes and increase the robustness of power system. In this paper, PSS design for single machine infinite bus is formulated as multi-objective and multi-operating conditions, and the hybrid approach involving bacterial foraging and particle swarm optimization algorithm is employed to solve this problem. The results of both eigenvalue analysis and nonlinear simulation show that the proposed PSS can damp the low-frequency oscillations effectively and work well with high

control performance under different operating conditions. Compared with PSS which is design by genetic algorithm, the proposed PSS in this paper has better damping characteristics.

Hyperchaotic Genetic Algorithm Theory and Functions Optimization You-Ming Yu Dept. of Computer Beijing Institute of Petrochemical Technology, Beijing 102617 China 81292148,8610 [email protected]

Guo-Qing Zhao Dept. of Computer Beijing Institute of Petrochemical Technology, Beijing 102617 China 81292148,8610 [email protected]

Jian-Dong Liu Dept. of InfoTechn. Beijing Institute of Petrochemical Technology, Beijing 102617 China 81292295,8610 [email protected]

ABSTRACT Genetic algorithm (GA) has premature limitation,so the hyperchaotic genetic algorithm (HCGA) was proposed. Applied a new chaos-genetic evolution mechanism for avoiding the repetition operation among the currently common chaos optimization and crossover operator and mutation operator during evolution process. Adopting hyperchaotic model based on coupled map lattices loading hyperchaotic variables on variables population of genetic algorithm, fulfilled no collision evolution and fast convergence by the small disturbance from hyperchaotic variable to subpopulation and adjusted adaptively disturbance

range during search process. The function optimization results show HCGA improved the convergence and reduce the computing time greatly than GA or chaos genetic algorithm (CGA).Using the average truncated generation and the distribution entropy of truncated generations as evaluation criterion of optimization efficiency, compared quantificationally HCGA with CGA and GA, the optimization computation results show that the HCGA has higher optimization efficiency than CGA and GA.

Cloud Service and Service Selection Algorithm Research Wenying Zeng School of Computer Science and Engineering, South China University of Technology; Guangdong Institute of Science and Technology Guangzhou 510640, China

[email protected] Yuelong Zhao School of Computer Science and Engineering, South China University of Technology Guangzhou 510640, China

[email protected] Junwei Zeng Chongqing University of Posts and Telecommunications Chongqing 400065, China

[email protected] ABSTRACT This paper describes the cloud service architecture and key technologies for service selection algorithm. Cloud computing is a hot topic on software and distributed computing based on Internet, which means users can access storages and applications from remote servers by web browsers or other fixed or mobile terminals. Because the constrained resources of fixed or mobile terminals, cloud computing will provide terminals with powerful complementation resources to acquire complicated services. The paper discusses the cloud service architecture and key algorithms about service selection with adaptive performances and minimum cost. The cloud service architecture is reasonable and the proposed service selection algorithms are available, scalable, and adaptive to different types of environments of services and clients.

Hybrid Differential Evolution and the Simplified Quadratic Interpolation for Global Optimization Li Zhang National Key Laboratory of Antennas and Microwave Technology Xidian University Xi’an, Shaanxi, 710071,China

[email protected] Yong-Chang Jiao National Key Laboratory of Antennas and

Microwave Technology Xidian University Xi’an, Shaanxi, 710071,China

[email protected] Hong Li National Key Laboratory of Antennas and Microwave Technology Xidian University Xi’an, Shaanxi, 710071,China

[email protected] Fu-Shun Zhang National Key Laboratory of Antennas and Microwave Technology Xidian University Xi’an, Shaanxi, 710071,China

[email protected] ABSTRACT To improve the searching ability and convergence speed of di®erential evolution (DE), we combined a search operation for enhancing the performance of the original DE. The simpli¯ed quadratic interpolation (SQI) is employed to improve the local search ability and the accuracy of the minimum function value, and to reduce greatly the computational overhead of the algorithm. The classic benchmark test functions are employed to evaluate the performance of the proposed method. We also provide a comparison of the proposed method to fuzzy adaptive di®erential evolution (FADE). Experimental results con¯rm that the proposed method outperforms the original DE and FADE in terms of convergence speed, solution quality, and solution stability.

Object Segmentation Based on Disparity Estimation Qian Zhang1 Suxing Liu1 Ping An1,2 Zhaoyang Zhang1,2 1School

of Communication and Information Engineering Laboratory of Advanced Displays and System Application, Ministry of Education Shanghai University Shanghai,China 021-56332183

2Key

[email protected] ABSTRACT Object segmentation plays an important role in multi-view video analysis. In this paper, we present a new object segmentation method for multi-view video in which only the disparity is used for segmentation and the motion estimation is neglected. Firstly, a modified locally adaptive support-weight approach is proposed for disparity estimation. Then, segmentation is realized by meanshift algorithm. The experimental results show that proposed method could segment the semantically meaningful objects from complex background with high precision.

A Weight Based Compact Genetic Algorithm Qing-bin Zhang

Shijiazhuang Institute of Railway Technology Shijiazhuang 050041, China

[email protected] Ti-hua Wu Hebei Academy of Sciences 46 South Youyi street Shijiazhuang 050081, China

[email protected] Bo Liu Hebei Academy of Sciences 46 South Youyi street Shijiazhuang 050081, China

[email protected]

ABSTRACT In order to improve the performance of the compact Genetic Algorithm（cGA） to solve difficult optimization problems, an improved cGA which named as the weight based compact Genetic Algorithm (wcGA) is proposed. In the wcGA, S individuals are generated from the probability vector in each generation, when the winner competing with the other S-1 individuals to update the probability vector, different weights are multiplied to each solution according to the sequence of the solution ranked in the S-1 individuals. Experimental results on three kinds of Benchmark functions show that the proposed algorithm has higher optimal precision than that of the standard cGA and the cGA simulating higher selection pressures.

An Improved Differential Evolution to Continuous Domains and Its Convergence Yuntao Zhao National Engineer Research Center of Advanced Rolling, University of Science and Technology Beijing, Beijing, China

[email protected] Jing Wang National Engineer Research Center of Advanced Rolling, University of Science and Technology Beijing, Beijing, China

[email protected] Yong Song National Engineer Research Center of Advanced Rolling, University of Science and Technology Beijing, Beijing, China

[email protected] ABSTRACT When differential evolution algorithm is applied in complicated optimization problems, it has the shortages of prematurity and stagnation. An improved differential evolution to obtain solutions quickly is proposed in this paper. The algorithm takes into account the information of problem solving and objective function. Firstly, a hybrid optimization strategy that parallelly executes uniform crossover and Binomial crossover is designed. So individuals can fully represent the solution space. Secondly, a transform function is constructed. This method is utilized to simplify the objective function .It eliminates local minimum and keeps the value of optimized function unchanged under the local minimum. Then its convergence is analyzed theoretically, and is proved to converge to the best solution. This algorithm is also tested by several benchmark functions. The simulation results show that it has perfect property in efficacy and converges faster

Study to Short-term Flow Estimation at Intersection Base on Genetic Neural Networks Zhou ZhiNa Air Traffic Management Institute, Northwestern Polytechnical University, Xi’an,710072,China [email protected]

Shi ZhongKe Air Traffic Management Institute, Northwestern Polytechnical University, Xi’an,710072,China

Li YingFeng Air Traffic Management Institute, Northwestern Polytechnical University, Xi’an,710072,China

ABSTRACT The traffic flow data is the foundation of the transportation management and control. Inevitably there is data loss in traffic parameters acquisitions, so it needs traffic flow estimation to complete the traffic flow information when the data loss is serious. Proper estimation of traffic flow is an essential component of advanced management of dynamic traffic networks. The genetic nerve-network is developed, combined the nerve network and the genetic algorithm together, to estimate the short-term traffic volume. According to the experiment result, the method is effective to estimate traffic flow in the short term at intersection

Study to Short-term Flow Estimation at Intersection Base on Genetic Neural Networks Zhou ZhiNa Air Traffic Management Institute, Northwestern Polytechnical University, Xi’an,710072,China [email protected]

Shi ZhongKe Air Traffic Management Institute, Northwestern Polytechnical University, Xi’an,710072,China

Li YingFeng Air Traffic Management Institute, Northwestern Polytechnical University, Xi’an,710072,China

ABSTRACT The traffic flow data is the foundation of the transportation management and control. Inevitably there is data loss in traffic parameters acquisitions, so it needs traffic flow estimation to complete the traffic flow information when the data loss is serious. Proper estimation of traffic flow is an essential component of advanced management of dynamic traffic networks. The genetic nerve-network is developed, combined the nerve network and the genetic algorithm together, to estimate the short-term traffic volume. According to the experiment result, the method is effective to estimate traffic flow in the short term at intersection

Independent Global Constraints for Web Service Composition Based on GA and APN Xianwen Fang1,2,3 1Key

Lab of Embedded System & Service Computing Ministry of Education, Tongji University, Shanghai, 201804, China +86-021-69589864

[email protected] Changjun Jiang1,2 2Electronics

and Information Engineering School, Tongji University, Shanghai, 201804, China +86-021-69589864

[email protected] Xiaoqin Fan1,2 3Information

Science Department, Anhui University of Science and Technology, Huainan, Anhui Province, 232001, China +86-021-69589864

[email protected] ABSTRACT The Service composition has been a popular research presently. Service Composition by manual cannot meet the expectations in reality, but the wholly intellectualized automatic service composition is a very complicated process. So, many applications and research about service composition are oriented to semiautomatic service composition, for obtaining optimal performance by some compositing policies. A global constraint is independent if the values that should be assigned to all the remaining restricted attributes can not be uniquely determined once a value is assigned to one. Based on the Web service ontology, the paper presents an independent global constrains-aware Web service composition approach based on semantic. Associate Petri net (APN) modeling methods which can describe multi-attribute multi-constraint relations and associate relationships between component services are proposed. Then, using the properties and reasoning rules of APN, a constraintaware service composition optimization algorithm is presented in order to locate legal firing sequences in APN model, and those corresponding to the legal firing sequences with the biggest trust value are the optimal solutions. Lots of experiments show that this semantic-based method has both lower time consuming and higher success ratio of service composition.

Tabu-search for Single Machine Scheduling With Controllable Processing Times Zuren Feng Xi’an Jiaotong University 28 Xianning Lane Xi’an, China

[email protected] Kailiang Xu Xi’an Jiaotong University 28 Xianning Lane

Xi’an, China

[email protected] ABSTRACT In this paper, we consider scheduling n jobs with arbitrary release dates and due dates on a single machine, where jobs’ processing times can be controlled by the allocation of a common resource, and the operation is modeled by a nonlinear convex resource consumption function. The objective is to obtain an optimal processing sequence as well as optimal resource allocation, such that all the jobs could be finished no later than their due dates, and the resource consumption could be minimized. Since the problem is strongly NP-hard, a two-layer-structured algorithm based on tabusearch is presented. The computational result compared with a branch-and-bound algorithm showed the algorithm is capable for producing optimal and near optimal solution for large sized problems in acceptable computational time.

Particle Swarm Optimization Algorithm for Emergency Resource Allocation on Expressway Chai Gan Transportation College Southeast University, Nanjing, 210096, China (+86)13851446229

[email protected] Ying-ying Transportation College Southeast University, Nanjing, 210096, China (+86)13951613471

[email protected] Zhu Cang-hui Transportation College Southeast University, Nanjing, 210096, China (+86)13851900541

[email protected]

ABSTRACT In order to allocate traffic emergency rescue resources on expressway, considering rescue time and resources costs as the objective, stochastic variables are introduced into constraints and a corresponding stochastic programming model is established, due to the stochastic resource requirements of accidents. Because of large numbers of rescue depots and black-spots, a stochastic simulation of particle swarm optimization (PSO) algorithm is put forward, and a particle presentation of Indirect Particle Position (IPP) is developed. By using the algorithm, the model need not to be converted into certain programming and is easy to solve. The model and the algorithm are used in the case of rescue resource allocation problem on expressway networks in Nanjing area, and the results show that the method is more effective and efficient than a traditional algorithm. In addition, the results can provide a reference for resource allocation of other expressway networks.

Orthogonal Immune Algorithm with Diversity-based Selection for Numerical Optimization Maoguo Gong Xidian University Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of

Intelligent Information Processing, PO Box 224, Xidian University, Xi'an, 710071, China +86-029-88202661

[email protected] Licheng Jiao Xidian University Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, PO Box 224, Xidian University, Xi'an, 710071, China +86-029-88201023

[email protected] Wenping Ma Xidian University Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, PO Box 224, Xidian University, Xi'an, 710071, China +86-029-88202661

[email protected] ABSTRACT In this study, we design an Orthogonal Immune Algorithm (OIA) for numerical optimization by incorporating orthogonal initialization, a novel neighborhood orthogonal cloning operator, a static hypermutation operator, and a novel diversity-based selection operator. The OIA is unique in three respects: Firstly, a new selection method based on orthogonal arrays is provided in order to maintain diversity in the population. Secondly, the orthogonal design with quantization technique is introduced to generate initial population. Thirdly, the orthogonal design with the modified quantization technique is introduced into the cloning operator. In order to identify any improvement due to orthogonal initialization, diversity-based selection and neighborhood orthogonal cloning, we modify the OIA via replacing its orthogonal initialization by random initialization; replacing its diversity-based selection by a standard evolutionary operator (μ+λ)-selection operator; and replacing its neighborhood orthogonal cloning by proportional cloning, and compare the four version algorithms in solving eight benchmark functions and six composition functions.

Large-scale Optimization Using Immune Algorithm Maoguo Gong Xidian University Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, PO Box 224, Xidian University, Xi'an, 710071, China +86-029-88202661

[email protected] Licheng Jiao

Xidian University Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, PO Box 224, Xidian University, Xi'an, 710071, China +86-029-88201023

[email protected] Wenping Ma Xidian University Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, PO Box 224, Xidian University, Xi'an, 710071, China +86-029-88202661

[email protected] ABSTRACT Immune-inspired optimization algorithms encoded the parameters into individuals where each individual represents a search point in the space of potential solutions. A large number of parameters would result in a large search space. Nowadays, there is little report about immune algorithms effectively solving numerical optimization problems with more than 100 parameters. In this paper, we introduce an improved immune algorithm, termed as Dual-Population Immune Algorithm (DPIA), to solve large-scale optimization problems. DPIA adopts two side-by-side populations, antibody population and memory population. The antibody population employs the cloning, affinity maturation, and selection operators, which emphasizes the global search. The memory population stores current representative antibodies and the update of the memory population pay more attention to maintain the population diversity. Normalized decimal-string representation makes DPIA more suitable for solving large-scale optimization problems. Special mutation and recombination methods are adopted to simulate the somatic mutation and receptor editing process. Experimental results on eight benchmark problems show that DPIA is effective to solve large-scale numerical optimization problems.

A Bounded Diameter Minimum Spanning Tree Evolutionary Algorithm Based on Double Chromosome Fangqing Gu Faculty of Applied Mathematics Guangdong University of teachnology Guangdong Province,China

[email protected] Hai-lin Liu Faculty of Applied Mathematics Guangdong University of teachnology

Guangdong Province,China

[email protected] Wei Liu Faculty of Applied Mathematics Guangdong University of teachnology Guangdong Province,China

[email protected] ABSTRACT The Bounded Diameter Minimum Spanning Tree problem (BDMST) is a classical combinatorial optimization problem. In this paper,we propose a double chromosome evolutionary algorithm based on level coding and permutation coding for the BDMST problem. Double chromosome coding achieves the correspondences of the code and the solution of BDMST problem, so that the local search can be implemented more efficiently. A new crossover operator is design based on the double chromosome coding. The proposed algorithm keeps diversity and preferable convergence, because The offspring not only inherit the parent’s some sub-tree, but also generate some new edges. Designed a novel decoding strategy to the level code chromosome, might find the predecessor that associated with smaller costs. The proposed algorithm is empirically compared to edge-set coded genetic algorithm and a variable neighborhood search implementation on Euclidean instances based on complete graphs with up to 1000 nodes considering either solution quality as well as computation time. It turns out that the evolutionary algorithm used double chromosome performs best the edge-set EA and the variable neighborhood search implementation concerning computation time.

An Improved Quantum Genetic Algorithm for Stochastic Flexible Scheduling Problem with Breakdown Jinwei Gu East China Univ. of Sci. & Tech. 130 Meilong Road Xuhui District, Shanghai, China 86-21-64252576

Cuiwen Cao East China Univ. of Sci. & Tech. 130 Meilong Road Xuhui District, Shanghai, China 86-21-64252576 [email protected]

Bin Jiao Shanghai Dianji Univ. 690 Jiangchuan Road Minhang District, Shanghai, China 86-21-54758615 [email protected]

Xingsheng Gu* East China Univ. of Sci. & Tech. 130 Meilong Road Xuhui District, Shanghai, China 86-21-64252576 [email protected]

ABSTRACT A stochastic flexible scheduling problem subject to random breakdowns is studied in this paper, which objective is to minimize the expected value of makespan. We consider a preemptive-resume model of breakdown. The processing times, breakdown intervals and repair times are random variables subjected to independent normal distributions. An expanding method inspired by paper [1] is devised through predicting expected breakdown time of machines. Based on some concepts of quantum evolution, an Improved Quantum Genetic Algorithm (IQGA) is proposed, which is tested on a sampling problem compared with Cooperative Co-evolutionary Genetic Algorithm (CCGA) and Genetic Algorithm (GA). Experiment results show IQGA has better feasibility and effectiveness. Categories and Subject Descriptors G.1.6 [Numerical Analysis]: Optimization – constrained optimization, global optimization, stochastic programming. General Terms: Algorithms. Keywords quantum algorithm; machine breakdown; stochastic scheduling

Binary Particle Swarm Optimization Based Prediction of G-Protein-Coupled Receptor Families with Feature Selection Quan Gu College of Information Sciences and Technology, Donghua University Shanghai 201620, China

Yongsheng Ding* College of Information Sciences and Technology, Donghua University Engineering Research Center of Digitized Textile & Fashion Technology, Ministry of Education Shanghai 201620, China +86 21 67792329 *[email protected]

ABSTRACT G-protein-coupled receptors (GPCRs), the largest family of membrane protein, play an important role in production of therapeutic drugs. The functions of GPCRs are closely correlated with their families. It is crucial to develop powerful tools to predict GPCRs families. In this study, Binary particle swarm optimization (BPSO) algorithm, which has a better optimization performance on discrete binary variables than particle swarm optimization (PSO), is applied to extract effective feature for amino acids pair compositions of GPCRs protein sequence. Ensemble classifier is used as prediction engine, of which the basic classifier is the fuzzy K-nearest neighbor (FKNN). Each basic classifier is trained with different feature sets. The results obtained by jackknife test are quite encouraging, indicating that the proposed method might become a potentially useful tool for GPCR prediction, or play a complimentary

Classification of EEG Signals Using Relative Wavelet Energy and Artificial Neural Networks Ling Guo ∗

Department of Information Technologies and Communications University of A Coruña, A Coruña, 15071, Spain

[email protected] Daniel Rivero Department of Information Technologies and Communications University of A Coruña, A Coruña, 15071, Spain

[email protected] Jose A.Seoane Department of Information Technologies and Communications University of A Coruña, A Coruña, 15071, Spain

[email protected] Alejandro Pazos Department of Information Technologies and Communications University of A Coruña, A Coruña, 15071, Spain

[email protected] ABSTRACT Electroencephalographms (EEGs) are records of brain electrical activity. It is an indispensable tool for diagnosing neurological diseases, such as epilepsy. Wavelet transform (WT) is an effective tool for analysis of non-stationary signal, such as EEGs. Relative wavelet energy (RWE) provides information about the relative energy associated with different frequency bands present in EEG signals and their corresponding degree of importance. This paper deals with a novel method of analysis of EEG signals using relative wavelet energy, and classification using Artificial Neural Networks (ANNs). The obtained classification accuracy confirms that the proposed scheme has potential in classifying EEG signals.

Path Planning Method for Robots in Complex Ground Environment Based on Cultural Algorithm Yi-nan Guo School of Information and Electronic Engineering, China University of Mining and Technology, Xuzhou,China 86-516-83884749, 221116

[email protected] Mei Yang School of Information and Electronic Engineering, China University of Mining and Technology, Xuzhou,China 221116

[email protected]

Jian Cheng School of Information and Electronic Engineering, China University of Mining and Technology, Xuzhou,China 221116

[email protected] ABSTRACT In complex ground environment, different regions have different road conditions. Path planning for robots in such environment is an open problem, which lacks effective methods. A novel global path planning method based on common sense and evolution knowledge is proposed by adopting dual evolution structure in culture algorithms. Common sense describes ground information and feasibility of environment, which is used to evaluate and select the paths. Evolution knowledge describes the angle relationship between the path and the obstacles, or the common segments of paths, which is used to judge and repair infeasible individuals. Taken two types of environments with different obstacles and road conditions as examples, simulation results indicate that the algorithm can effectively solve path planning problem in complex ground environment and decrease the computation complexity for judgment and repair of infeasible individuals. It also can improve the convergence speed and have better computation stability.

Cooperative Interactive Cultural Algorithms Adopting Knowledge Migration Yi-nan Guo School of Information and Electronic Engineering, China University of Mining and Technology, Xuzhou,China 86-516-83884749, 221116

[email protected] Jian Cheng School of Information and Electronic Engineering, China University of Mining and Technology, Xuzhou,China 221116

[email protected] Yong Lin School of Information and Electronic Engineering, China University of Mining and Technology, Xuzhou,China 221116

[email protected] ABSTRACT In many optimization problems with implicit indexes, human need to participate in the evaluation process synchronously in different computer nodes. And human is easy to feel tired. In order to alleviate human fatigue, implicit knowledge embodied in the evolution process, which reflect human cognition and preference, is extracted and utilized. However, how to effectively exchange information among nodes is not taken into account. Aiming at systemic analysis and effective application about implicit knowledge, cooperative interactive cultural algorithm adopting knowledge migration strategy is proposed. A novel knowledge model based on characteristic-vector is adopted to describe implicit knowledge embodied in the evolution process, including human cognitive tendency, the degree of human preference, the degree of human fatigue and human cognition schema. According to the

evolution status of population and human fatigue in each computer node, human cognition schemas are migrated between nodes. And common knowledge is obtained by coordination strategy and utilized to induce the evolution process of ICA in each computer node. Taking cooperative fashion design system as a testing platform, the rationality of knowledge migration strategy is proved. Simulation results indicate this algorithm can alleviate human fatigue and improve the speed of convergence effectively.

Intrinsic Evolution of Digital Circuits Using Evolutionary Algorithms Guoliang He1,2, Yuanxiang Li1, Zhongzhi Shi2，Ting Hu3 1State

Key Laboratory of Software Engineering, Wuhan University, Wuhan, China Laboratory of Intelligent Information Process, Institute of Computing Technology, The Chinese Academy of Sciences, Beijing, China 3Department of Computer Science, Memorial University of Newfoundland, St. John’s, Canada 2Key

[email protected], [email protected], [email protected], [email protected] ABSTRACT Currently, the auto-design of electronic and analog circuits is an intensively studied topic in the field of evolvable hardware. In order to improve evolutionary design of logic circuits in efficiency, capability of optimization and safety of on-line evolution, an elitist pool evolutionary algorithm (EPEA) based on novel approaches is proposed. First, an extended matrix encoding method is devised, which can be able to reflect the potential performance of a circuit and reduce the risk of deleting a circuit with a good developing potential during evolution. Then, a novel sub-circuit crossover operator and an adaptive mutation strategy are introduced to improve the local optimization and maintain the diversity of a population in the evolution. At last, a framework of on-line evolution is used to implement EPEA on a fieldprogrammable gate array. Experiments show that our proposed method is able to design valid and novel circuits efficiently.

Large Scale Function Optimization or High-Dimension Function Optimization in Large Using Simplex-based Genetic Algorithm Xiao Hongfeng School of Information Science and Engineering, Central South University, China 8872564, 0731, 086

[email protected] Tan Guanzheng School of Information Science and Engineering, Central South University, China 8876128, 0731, 086

[email protected] Huang Jingui Department of Computer Education, Hunan Normal University, China 8872564, 0731, 086

[email protected]

ABSTRACT

Simplex genetic algorithm (Simplex-GA) is the fusion between the simplex multi-direction searches consisting in Nelder-Mead Simplex Method (NMSM), i.e., MDS-NMSM, and the evolutionary mechanism of genetic algorithm, i.e., selecting the superior and eliminating the inferior. One of important differences in evolution algorithms is that each evolution algorithm has its own especial reproduce operators. The reproduce operator of simplex-GA consists of an extremum mutation operator and directional reproduce operators. The extremum mutation operator is designed for the best individual, while the directional reproduce operators are devised for all individuals except the best individual and based on the multi-direction search of NMSM. The direction reproduce operators have four main features. (1)The first is that the directional reproduce operators are the combination of deterministic search and random search. (2)The second is that the directional reproduce operators search for new individuals according to a new mode from point-search, line-search to plane-search or solid-search; the point-search is a deterministic search, while line-search, plane-search and solid-search are random searches; deterministic search is prior to random search. (3)The third is that directional reproduce operators are embedded into multi-direction search of Nelder-Mead Simplex Method. Based on above three points, simplex is a primary element of simplex-GA. In this paper, we only discuss two extreme cases: low dimension simplex-GA (LD-Simplex-GA), where the dimensionality of simplex is small, and high dimension simplex-GA (HD-Simplex-GA), where the dimensionality of simplex is big. The elaborately selected eight test functions with 500-1500 dimensions are used to verify the performances of LD-simplex-GA and HD-Simplex-GA, and experiment results confirm that both LD-Simplex-GA and HD-Simplex-GA have the excellent capacity of optimizing the functions with large scale variants

Model-based Compromise Control of Greenhouse Climate using Pareto Optimization ∗ Haigen Hu †

Department of Control Science and Engineering Tongji University, Shanghai, China, 200092

[email protected] Lihong Xu ‡

Member,ACM Department of Control Science and Engineering Tongji University, Shanghai, China, 200092

[email protected] Qingsong Hu Department of Control Science and Engineering Tongji University, Shanghai, China, 200092

[email protected] ABSTRACT Energy-saving is always in conflict with the control Errorminimizing for real-world engineering application in greenhouse. Moreover, the efficiency of plant production and energy consumption depends largely on the adjustment of greenhouse environment. In order to achieve less energy consumption and higher control precision, this paper presents a kind of compromise control algorithm for Pareto solutions of greenhouse environment control. The models of greenhouse and weather forecast used are described and derived. A series of optimization experiments are presented at any time of a day using Non-dominated Sorting Genetic AlgorithmII(NSGA-II). The results show the feasibility of the proposed method, and it may be valuable and helpful to formulate environmental control strategies, and to achieve high control precision and low energy cost.

Non-even Spread NSGA-II and Its Application to Con_icting Multi-Objective Compatible Control ¤

Qingsong Huy Department of Control Science and Engineering, Tongji University 65# Chifeng Road Shanghai 200092, China

[email protected] Lihong Xuz Department of Control Science and Engineering, Tongji University 65# Chifeng Road Shanghai 200092, China

[email protected] Erik Goodman Department of Electrical and Computer, Michigan State University 2340 Engineering College Building, MSU East Lansing, MI48824,USA

[email protected] ABSTRACT Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is a sound method to deal with the multi-objective optimization problem, and even spread Pareto front preserving strategy is one of its two key principles. However, especially for some dynamic problems, the most interested area is certain special area among the Pareto front. To meet this requirement, the non-even Pareto front spread preserving principle is proposed and is taken as the optimization tool for the multi-objective compatible control problem (MOCCP). To decrease the real-time computation load at every control step, based on the tight relation between the system states of the neighboring sampling instants, an iterative control algorithm is presented. The stability preference selection strategy in the algorithm tends to produce a stable controller in face of the Pareto front with the divergent or oscillating segment. To further decrease the computation time, adaptable population corresponding with the control process is adopted. Comparative simulation example illustrates the validity.

Guided Variable Neighborhood Harmony Search for Integrated Charge Planning in Primary Steelmaking Processes 1, 2

Min Huang 1

, Hong-yu Dong

1, 2

, Xing-wei

1

2

Wang , Bing-lin Zheng , W.H.Ip

3

College of Information Science and Engineering, Northeastern University Shenyang, 110004, China

2

Key Laboratory of Integrated Automation of Process Industry (Northeastern University), Ministry of Education, Shenyang, 110004, China 3

Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong +86-24-83671469

[email protected] [email protected]

ABSTRACT The planning for rectangular plate products (slabs) in an integrated steel plant is extremely hard and important. Due to the large scale and complex integrated operational constraints, the planning problem is quite difficult to achieve an optimal solution even a feasible solution. From the practical point of view, this paper discusses an integrated charge planning (ICP) problem, with flexible product specifications. The purpose is to improve the efficiency and feasibility of planning, the customer satisfaction levels and the production costs, considering the integrated operational constraints. An integer programming model is formulated, and the problem is NP-hard. A new heuristics based on a variable neighborhood search (VNS), named the guided VNS embedded in harmony search, is designed. The computational results demonstrate that the proposed model and algorithm are feasible and effective for ICP.

University Course Timetable Planning using Hybrid Particle Swarm Optimization Ho Sheau Fen @ Irene Faculty of Comp. Sc. & Info. Sys. Universiti Teknologi Malaysia 81310. Johor, Malaysia +6019-8331338

[email protected] Deris Safaai Faculty of Comp. Sc. & Info. Sys. Universiti Teknologi Malaysia 81310. Johor, Malaysia +6019-7569202

[email protected] Mohd Hashim, Siti Zaiton Faculty of Comp. Sc. & Info. Sys. Universiti Teknologi Malysia 81310. Johor, Malaysia +6019-7726248

[email protected] ABSTRACT University Course Timetabling (UCT) is a complex problem and cannot be dealt with using only a few general principles. The complicated relationships between time periods, subjects and classrooms make it difficult to obtain feasible solution. Thus, finding feasible solution for UCT is a continually challenging problem. This paper presents a hybrid particle swarm optimization algorithm to solve University Course Timetabling Problem (UCTP). The proposed approach (hybrid particle swarm optimization with constraint-based reasoning) uses particle swarm optimization to find the position of room and timeslot using suitable objective function and the constraints-based reasoning has been used to search for the best preference value based on the student capacity for each lesson in a reasonable computing time. The proposed algorithm has been validated with other hybrid algorithms (hybrid particle swarm optimization with local search and hybrid genetic algorithm with constraint-based reasoning) using a real world data from Faculty of Science at Ibb University – Yemen and results show that the proposed algorithm can provide more promising solution.

The Impact of Network Topology on Self-Organizing Maps Fei J iang1, 2 , Hugues Berr y 1 , Marc Sc hoenauer2

Pr ojec t- Team Alc hemy, INRIA Saclay – Île-de-France, Par c O r s ay Univer s ité 28, r ue J ean Ros tand 91893 O r s ay Cedex , Franc e 2Pr ojec t- Team TAO INRIA Sac lay – Île- de- Franc e & LRI ( UMR CNRS 8623) Bât 490, Univer s ité Par is - Sud 91405 O r s ay Cedex , Franc e 1

Fei. J iang@inr ia. f r, Hugues. Berr y @inr ia. f r, Marc . Sc hoenauer@inr ia. f r ABSTRACT In this paper, we study instances of complex neural networks, i.e. neural networks with complex topologies. We use Self-Organizing Map neural networks whose neighborhood relationships are defined by a complex network, to classify handwritten digits. We show that topology has a small impact on performance and robustness to neuron failures, at least at long learning times. Performance may however be increased (by almost 10%) by evolutionary optimization of the network topology. In our experimental conditions, the evolved networks are more random than their parents, but display a more heterogeneous degree distribution.

Comprehensive Analysis for Modified Particle Swarm Optimization with PD Controllers Jing Jie College of Software, Zhejiang University of Technology1, Hangzhou City, 310014; Taiyuan University of Science & Technology2, Taiyuan City, 030024 13068085117, China, CN0086 [email protected]

Jianchao Zeng Division of System Simulation & Computer Application, Taiyuan University of Science & Technology2, WaLiu Road 66#, Taiyuan City, 030024 0351-6998016, China, CN0086 [email protected]

Wanliang Wang College of Software, Zhejiang University of Technology1, Hangzhou City, 310014; 0571-85290667,China, CN0086 [email protected]

ABSTRACT Inspired by the information prediction existing in the nature intelligent agents, the authors have developed a modified particle swarm optimization (PSO) with a forward PD controller (PSOFWPD) earlier. Comprehensive analysis for the model is provided in the paper, including its stabilization, convergence and dynamic behavior. Later, another modified PSO with a feedback PD

controller (PSO-FBPD) is presented companying some analysis. The introductions of different PD controllers develop the standard PSO(SPSO) with information prediction cabality, which can guide the particle to respond to the change of their exemplars correctly and rapidly, and greatly contributes to a successful global search. The proposed methods provide some new ideas for the improvement of SPSO, and are compared with SPSO based on some complex numerical optimization simulations. The relative experimental results show SPSO with different PD controller performs better than SPSO on the complex optimization problems.

Combinatorial Effects of Local Structures and Scoring Metrics in Bayesian Optimization Algorithm Hossein Karshenas Iran University of Science and Technology Narmak, Tehran, Iran

ho_karshenas @comp.iust.ac.ir Amin Nikanjam Iran University of Science and Technology Narmak, Tehran, Iran

[email protected] B. Hoda Helmi Iran University of Science and Technology Narmak, Tehran, Iran

[email protected] Adel T. Rahmani Iran University of Science and Technology Narmak, Tehran, Iran

[email protected] ABSTRACT Bayesian Optimization Algorithm (BOA) has been used with different local structures to represent more complex models and a variety of scoring metrics to evaluate Bayesian network. But the combinatorial effects of these elements on the performance of BOA have not been investigated yet. In this paper the performance of BOA is studied using two criteria: Number of fitness evaluations and structural accuracy of the model. It is shown that simple exact local structures like CPT in conjunction with complexity penalizing BIC metric outperforms others in terms of model accuracy. But considering number of fitness evaluations (efficiency) of the algorithm, CPT with other complexity penalizing metric K2P performs better

Hybrid Algorithms Based on Harmony Search and Differential Evolution for Global Optimization Ling-po Li Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation,

Tsinghua University, Beijing, 100084, P.R. China

[email protected] Ling Wang Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing, 100084, P.R. China

[email protected] ABSTRACT In this paper, two hybrid algorithms are proposed for global optimization by merging the mechanisms of Harmony Search (HS) and Differential Evolution (DE). First, the learning mechanism of a variant of HS named Global-best Harmony Search (GHS) is embedded into the framework of DE to develop an algorithm called Global Harmony Differential Evolution (GHDE). Besides, the differential operator of DE is introduced into the framework of GHS to develop another new algorithm called Differential Harmony Search (DHS). Numerical simulations are carried out based a set of benchmarks. And simulation results and comparisons show that the hybrid algorithms are superior to the GHS and DE in terms of searching efficiency and searching quality. Meanwhile, the effect of some key parameters on the performances of DHS is investigated

The Cloud-based Framework for Ant Colony Optimization Zhiyong Li School of Computer and Communication Hunan University, 410082 Changsha, China

[email protected] Yong Wang School of Computer and Communication Hunan University, 410082 Changsha, China

[email protected] Kouassi K. S. Olivier School of Computer and Communication Hunan University, 410082 Changsha, China

[email protected] Jun Chen Office Of Student Admission Hunan University, 410082 Changsha, China

[email protected] Kenli Li School of Computer and Communication Hunan University, 410082 Changsha, China

[email protected] ABSTRACT How to keep the balance between exploration in search space regions and exploitation of the search experience gathered so far is one of the most important issues in Ant Colony Optimization

(ACO). By using a variety of effective exploitation mechanisms and elite strategies, researchers proposed many sophisticated ACO algorithms, and obtains better results in experiments. In this paper, a new framework for implementing ACO algorithms called the cloud-based framework for ACO is proposed, which uses cloud model as the fuzzy membership function and constructs a self-adaptive mechanism with cloud model. By using the self-adaptive mechanism and the pheromone updating rule of suboptimal solutions which is determined by the membership function uncertainly, the cloud-based framework can make ACO algorithm explorer search space more effectively. Theoretical analysis on the cloud-based framework for ACO indicate that the framework is convergent, and the simulation results show that the framework can improve the ACO algorithms evidently.

Multi-Objective Particle Swarm Optimization Algorithm Based on Game Strategies Zhiyong Li School of Computer and Communication, Hunan University Changsha, 10082, P.R. China. [email protected]

Songbing Liu Jun Chen Office of Student Admission Hunan University Changsha, 410082, P.R. China [email protected]

Kenli Li School of Computer and Communication, Hunan University Changsha, 410082, P.R. China [email protected]

School of Computer and Communication, Hunan University Changsha,410082, P.R. China. [email protected]

Degui Xiao School of Computer

andCommunication, Hunan University Changsha, 410082, P.R. China [email protected]

ABSTRACT Particle Swarm Optimization (PSO) is easier to realize and has a better performance than evolutionary algorithm in many fields. This paper proposes a novel multi-objective particle swarm optimization algorithm inspired from Game Strategies (GMOPSO), where those optimized objectives are looked as some independent agents which tend to optimize own objective function. Therefore, a multiplayer game model is adopted into the multi-objective particle swarm algorithm, where appropriate game strategies could bring better multi-objective optimization performance. In the algorithm, novel bargain strategy among multiple agents and nondominated solutions archive method are designed for improving optimization performance. Moreover, the algorithm is validated by several simulation experiments and its performance is tested by different benchmark functions.

Quantum Evolutionary Algorithm for Multi-Robot Coalition Formation Zhiyong Li School of Computer and Communication, Hunan University 410082 Changsha, Hunan, China [email protected]

Bo Xu School of Computer and Communication, Hunan University 410082 Changsha, Hunan, China [email protected]

Lei Yang School of Computer and Communication, Hunan University 410082 Changsha, Hunan, China [email protected]

Jun Chen Office Of Student Admission Of Hunan University Hunan University 410082 Changsha, Hunan, China [email protected]

Kenli Li School of Computer and Communication, Hunan University 410082 Changsha, Hunan, China [email protected]

ABSTRACT Coalition formation is an important cooperative method in Multi-Robot System, which has been paid more and more attention. However, efficient algorithm for multi-robot coalition is lack of various real-world applications in dynamic unknown environment. In such cases, the optimization algorithm has to track the changing optimum as close as possible, rather than just finding a static appropriate solution. In this paper, The Quantum Evolutionary Algorithm is proposed for solving this problem, where a skillful Quantum probability representation of chromosome coding strategy is designed to adapt to the complexity of the multi-robot coalition formation problem. Furthermore, a strategy for updating quantum

gate using the evolutionary equation is employed to avoid the premature convergence. Experiments results show that the proposed algorithm could solve the multi-robot coalition formation problem effectively and efficiently, and the proposed algorithm is valid and superior to other related methods as far as the stability and speed of convergence are concerned.

Global Path Planning for Mobile Robot Based Genetic Algorithm and Modified Simulated Annealing Algorithm Yuming Liang, Lihong Xu School of Electronics and Information Engineering, Tongji University Room 304,ongji University science and technology garden 2nd building, No. 67 Chifeng road, Yangpu District, Shanghai 201804, China 086-021-65980590

{nc21.lym, xulhk}@163.com ABSTRACT Global path planning for mobile robot using genetic algorithm and simulated annealing algorithm is investigated in this paper. In view of the slow convergence speed of the conventional simulated annealing algorithm, a modified simulated annealing algorithm is presented, and a hybrid algorithm based on the modified simulated annealing algorithm and genetic algorithm is proposed. The proposed algorithm includes three steps: the MAKLINK graph theory is adopted to establish the free space model of mobile robots firstly, then Dijkstra algorithm is utilized for finding a feasible collision-free path and fixing on the sub-searchspace where the global optimal path inside, finally the global optimal path of mobile robots is obtained based on the hybrid algorithm of modified simulated annealing algorithm and genetic algorithm. Experimental results indicate that the proposed algorithm has better performance than simulated annealing algorithm and ant system algorithm in term of both solution quality and computational time, and thus it is a viable approach to mobile robot global path planning.

Mobile Robot Global Path Planning Using Hybrid Modified Simulated Annealing Optimization Algorithm Yuming Liang, Lihong Xu School of Electronics and Information Engineering, Tongji University Room 304,o= Ongji University Science and Technology Garden 2nd building, No. 67 Chifeng road, Yangpu District, Shanghai 201804, China 086-021-65980590 {nc21.lym, xulhk}@163.com

ABSTRACT Global path planning for mobile robot using simulated annealing algorithm is investigated in this paper. In view of the slow convergence speed of the conventional simulated annealing algorithm, a modified simulated annealing algorithm is presented, and a hybrid algorithm based on the modified simulated annealing algorithm and conjugate direction method is proposed. On each temperature, conjugate direction method is utilized for searching local optimal solution firstly, then the modified simulated annealing algorithm is employed to move off local optimal solution, and then the temperature is updated; these operations are repeated until a termination criterion is satisfied. Experimental results indicate that the proposed algorithm has better performance than simulated annealing algorithm and conjugate direction method in term of both solution quality and

computational time, and thus it is a viable approach to mobile robot global path planning.

An Immune Algorithm for Complex Fuzzy Cognitive Map Partitioning Lin Chunmei Department of Computer Shaoxing College of Arts and Sciences Shaoxing Zhejiang 312000 CHINA 086-0575-88321046

[email protected] ABSTRACT Fuzzy cognitive map is an approach to knowledge representation and inference that are essential to any intelligent system; it emphasizes the connections of concepts as basic units for storing knowledge, and the structure represents the significance of system. It can be used for designing knowledge base, modeling and controlling complex systems. However, modern systems are characterized as complex systems with high dimension and a variety of variables and factors, when a large of nodes is included and the cause relation among concept-nodes is complex in the system, the inference, verification and maintenance of knowledge are very difficult. In this paper, we first analyze the knowledge representation and the inference mechanism of fuzzy cognitive map. Further, we present to partition the complex fuzzy cognitive map base into smaller chunks based on immune algorithm. In the methodology, we utilize the feature of fuzzy cognitive map to construct partition rules and criticize rules. Finally, an illustrative example is provided, and its results suggest that the method is capable of partitioning fuzzy cognitive map.

Parameters Optimization on Dent around Fuel Filler of Auto Rear Fender Based on Intelligent Algorithm Jianping Lin School of Mechanical Engineering, Tongji University NO.4800, Caoan Road, Shanghai, China +8613901719457

[email protected] Shuisheng Chen School of Mechanical Engineering, Tongji University NO.4800, Caoan Road, Shanghai, China +8613501941311

[email protected] Ying Cao School of Mechanical Engineering, Tongji University NO.4800, Caoan Road, Shanghai, China +8613601905310

[email protected] Huajun Guan Press Center, Shanghai Volkswagen Automotive Co., Ltd. NO.5288 CaoAn Road, Anting, Shanghai, China +862169564609

[email protected] ABSTRACT Dent is one of crucial surface defects in sheet metal forming. To improve the cosmetic surface qualities, it is important to optimize the process parameters to avoid dent defects in forming parts and to minimize production cost. The relationships between defects magnitude and forming parameters like die radius, punch radius, fuel filler radius, blank holder force (BHF) and friction coefficient can be established through finite element analysis (FEA). A reduced set of finite element simulations are carried out as per the orthogonal design array. Take the depth z and the width L of the surface dent as optimization objectives, an optimization methodology based on the use of orthogonal design method and the response surface technique based on Feedforward Neural Networks (FNN) is proposed to obtain the optimum values of above forming parameters, which can reduce the dent without cracking and damage of product, and z and L is gained depending on the optimized parameters by FEA. The optimization results of parameters are compared with the ones achieved by Trial and Error approach in industry. The result indicates that the proposed method is efficient for surface dents controlling.

Parameters Optimization of Support Vector Regression Based on Immune Particle Swarm Optimization Algorithm Yan Wang, Juexin Wang, Wei Du, Chen Zhang, Yu Zhang, Chunguang Zhou College of Computer Science and Technology, Jilin University Changchun 130012, China

[email protected]; [email protected] ABSTRACT A novel Immune Particle Swarm Optimization (IPSO) for parameters optimization of Support Vector Regression (SVR) is proposed in this article. After introduced clonal copy and mutation process of Immune Algorithm (IA), the particle of PSO is considered as antibodies. Therefore, evaluated the fitness of particles by the Leave-One-Out Cross-Validation (LOOCV) standard, the best individual mutated particle for each cloned group will be selected to compose the next generation to get better parameters of SVR. It can construct high accuracy and generalization performance regression model rapidly by optimizing the combination of three SVR parameters at the same time. Under the datasets generated from sinx function with additive noise and spectra dataset, simulation results show that the new method can determine the parameters of SVR quickly and the gotten models have superior learning accuracy and generalization performance.

Estimation of Distribution Algorithm Based

on Archimedean Copulas Li-Fang Wang1,2 [email protected] Jian-Chao Zeng2 [email protected] Yi Hong1 [email protected] 1. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, China 2. Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, 030024, China

ABSTRACT Both Estimation of Distribution Algorithms (EDAs) and Copula Theory are hot topics in different research domains. The key of EDAs is modeling and sampling the probability distribution function which need much time in the available algorithms. Moreover, the modeled probability distribution function can not reflect the correct relationship between variables of the optimization target. Copula Theory provides a correlation between univariable marginal distribution functions and the joint probability distribution function. Therefore, Copula Theory could be used in EDAs. Because Archimedean copulas possess many nice properties, an EDA based on Archimedean copulas is presented in this paper. The experimental results show the effectiveness of the proposed algorithm.

Categories and Subject Descriptors

Case Study of Finite Resource Optimization in FPGA Using Genetic Algorithm JingXia Wang Department of Electrical Engineering Shenzhen Polytechnic, XiLi Lake, NanShan District, ShenZhen, GuangDong, 518055, P.R.C 86-755-26731243 [email protected]

Sin Ming Loo Department of Electrical and Computer Engineering Boise State University, 1910 University Drive Boise, ID 83725, USA 1-208-426-5679 [email protected]

ABSTRACT Modern Field-Programmable Gate Arrays (FPGAs) are becoming very popular in embedded systems and high-performance applications. FPGA has benefited from the shrinking of transistor feature size, which allows more on-chip reconfigurable (e.g. memories and look-up tables) and routing resources. Unfortunately, the amount of reconfigurable resources in a FPGA is fixed and limited. This paper investigates an applicationmapping scheme in FPGA by utilizing sequential processing units and task specific hardware. Genetic Algorithm is used in this study. We found that placing sequential processor cores into FPGA can improve the resource utilization efficiency and

achieved acceptable system performance. In this paper, two cases were studied to determine the trade-off between resource optimization and system performance.

DynamicTrust: Three-Dimensional Dynamic Computing Model of Trust in Peer-to-Peer Networks Fengming Liu School of Management and Economics, Shandong Normal University No.88 Wenhua Road(E.), Jinnan Shandong Province, P.R. China 86+531+86180509

[email protected] Wenyin Zhang Information School, Linyi Normal University Linyi, Shandong, P.R.China 86+539-2060257

[email protected] Yongsheng Ding College of Information Sciences and Technology, Donghua University No. 2999, Renmin Road (N.) Songjiang, Shanghai P.R. China 86+21+67792323

[email protected] Xiyu Liu School of Management and Economics, Shandong Normal University No.88 Wenhua Road(E.), Jinnan Shandong Province, P.R. China 86+531+86180509

[email protected] Mingchun Zheng School of Management and Economics, Shandong Normal University No.88 Wenhua Road(E.), Jinnan Shandong Province, P.R. China 86+531+86180509

[email protected] Yu Liu Department of commerce, Jinan Technology College No.48 Maanshan Road(E.), Jinnan Shandong Province, P.R. China 86+531+86301137

[email protected] ABSTRACT With the application of peer-to-peer network, how to promote cooperation between peers has gotten more and more important. Most of traditional security technologies can not be applied in P2P network very well to promote the cooperation because of the

special characteristics of P2P network such as openness and anonymity, etc. Trust has been proven to be essential to enforcing cooperative behavior in peer-to-peer networks. Trust relationship depends on trustee’s trustworthiness. So, in this paper, we present a three-dimensional computing model of dynamic trust to try to find a way to address the problem. Firstly, we give a threedimensional computing model of trust and make a dynamics analysis to trust of peer. Next, considered the new peer without trustworthiness can not do anything almost, we propose an algorithm of initial trustworthiness based on the new peer’s abilities. To compute the direct trustworthiness and the recommended trustworthiness, we colligate the time as a dynamic factor. Finally, based on the trustworthiness computed by trust fusion algorithm, we present a mechanism of making trust decision to promote cooperation. The simulation results have showed that our model can enhance the cooperation between peers and avoid the malicious peers from destroying behaviors.

SRDE: An Improved Differential Evolution Based on Stochastic Ranking Jinchao Liu Technical University of Denmark Nils Koppels Alle Kgs. Lyngby 2800, Denmark +45 4525 5602

[email protected] Zhun Fan Technical University of Denmark Nils Koppels Alle Kgs. Lyngby 2800, Denmark +45 4525 6271

[email protected] Erik Goodman 2120 Engineering Building, MSU East Lansing, MI, 48824, USA +01 517 355 6453

[email protected]

ABSTRACT In this paper, we propose a methodology to improve the performance of the standard Differential Evolution (DE) in constraint optimization applications, in terms of accelerating its search speed, and improving the success rate. One critical mechanism embedded in the approach is applying Stochastic Ranking (SR) to rank the whole population of individuals with both objective value and constraint violation to be compared. The ranked population is then in a better shape to provide useful information e.g. direction to guide the search process. The performance of the proposed approach, which we call SRDE (Stochastic Ranking based Differential Evolution) is investigated and compared with standard DE with two variants of mutation strategies. The experimental results show that SRDE outperforms, or at least is comparable with standard DE in both variants in all the tested benchmark functions.

An Exploratory Study on Dominance Resistant Solutions in Many Objectives Optimization Liu Liu School of Management

Minqiang Li School of Management Tianjin University, Tianjin, 300072

{liuliu, mqli, dlin}@tju.edu.cn Lin Dan School of Science

ABSTRACT In spite of many approaches have been proposed to improve the performance of evolutionary multiobjective algorithms on many objectives optimization, little work was to explore the essential reason that the algorithms, such as the NSGA-II and SPEA2, deteriorates distinctly with increased number of objectives. One of the popular explanations is that the high proportion of nondominated solutions in the population breaks down the search of the algorithms. However, this paper attempts to explain that some dominance resistant solutions (DRSs) (except the extreme individuals which are located on coordinate axis greater than the extreme points of the true Pareto front), which are hard to be dominated and far away from the true Pareto front, constitute the essential handicap for the population evolution. It is observed this kind of solution is generated at the beginning of the evolution, and further delays the convergence of the algorithms due to diversity promoting methods. We make an analytical explanation of the four representative approaches that were originally proposed to address many objectives problems. At last, under a new framework of MOEA evolved with only nondominated solutions, our experimental results verify their performance on DTLZ1 and DTLZ2 problems with 4, 5, 6 objectives respectively.

Designing Fair Flow Fuzzy Controller Using Genetic Algorithm for Computer Networks Weirong Liu, Min Wu, Jun Peng and Guojun Wang School of Information Science and Engineering Central South University Changsha 410083, China [email protected]

ABSTRACT To utilize the link bandwidth efficiently in network, F.P.Kelly proposed the classic optimal model using utility function, which can converge to proportional fair point with asymptotic stability. However, the primal algorithm of Kelly model leads to the packet accumulation in the queue of the bottleneck link. By using heuristic fuzzy rules, this paper designs a fuzzy controller to adjust the additive increase parameter of the primal algorithm dynamically. Then genetic algorithm is used to optimize the scaling gains of the fuzzy controller, which is called GA-based fuzzy controller in this paper. The primal algorithm with the GAbased fuzzy controller can avoid the packet accumulation and keep the fairness and asymptotical stability. Thus it improves the performance of the primal algorithm.

Decision of Optimal Scheduling Scheme for Gas Field Pipeline Network Based on Hybrid Genetic Algorithm Wu Liu, Min Li School of Petroleum Engineering Southwest Petroleum University Chengdu, China +86-28-83033834

[email protected] Yi Liu Xi’an Changqing Technology Engineering Co. LTD. Xi’an, China +86-29-86599299

[email protected] Yuan Xu, Xinglan Yang Graduate School Southwest Petroleum University Chengdu, China +86-28-83032146

[email protected] ABSTRACT A mathematical model of optimal scheduling scheme for natural gas pipeline network is established, which takes minimal annual operating cost of compressor stations as objective function after comprehensively considering the resources of gas field, operating parameters of compressor stations and work conditions of pipeline system. In the light of the characteristics of the objective function such as nonliner, more optimal variables and complicated constraint conditions, based on the thought of modern heuristic evolutionary-algorithm, this paper presented a new hybrid genetic algorithm, which is featured by global search, fast convergence and strong robustness. It combined the reproduction strategy of differential evolution algorithm with the crossover and mutation of genetic algorithm. With the dynamic calibration of fitness and the elitism strategy of the optimal individual, this algorithm can improve the ability of searching and avoid the premature convergence effectively. The case study of a certain pipeline network system with 11 nodes, 11 pipelines,2 compressor stations demonstrates the effectiveness and application of the established model and algorithm. The optimal scheduling scheme could be adapted to daily operation and future retrofit of gas pipeline network.

The Design of Three-motor Intelligent Synchronous Decoupling Control System Xingqiao Liu School of Electrical and Information Engineering Jiangsu University, Zhenjiang, China 051188780895+86

[email protected] Jianqun Hu School of Electrical and Information Engineering

Jiangsu University, Zhenjiang, China 051188972520+86

[email protected] Shaoqing Teng School of Electrical and Information Engineering Jiangsu University, Zhenjiang, China 051188972520+86

[email protected] Liang Zhao School of Electrical and Information Engineering Jiangsu University, Zhenjiang, China 051188972041+86

[email protected] Guohai Liu School of Electrical and Information Engineering Jiangsu University, Zhenjiang, China 051188780821+86

[email protected] ABSTRACT Aiming at the characteristics of multi-input and multi-output, nonlinearity, time-variation and strong coupling in the threemotor synchronous control system, and on the basis of mathematic model analysis of three-motor synchronous control system, the neural network control system is designed. It is composed of three intelligent PID controllers based on BP neural network arithmetic which adjusts the parameters of PID controllers on-line and neuron decoupling compensator. The control of speed and tension of system is realized by three intelligent PID controllers based on BP neural network, and the decoupling control of coupled variables is achieved by neuron decoupling compensator. Experiment is combined with PLC, and the results indicate that the control system can get some optimal parameters of the PID controllers according to different running state of system. The method is designed to realize better decoupling control between the speed and tension in the system, and it has better dynamic and static characteristics.

A Simulated Annealing Algorithm with a new Neighborhood Structure for the Timetabling Problem Yongkai Liu Department of Computer Science, Xiamen University Xiamen, 361005, China +86

[email protected] Defu Zhang Department of Computer Science, Xiamen University Xiamen, 361005, China +86 592 5918207

[email protected] Stephen C.H. Leung Department of Management Sciences, City University of Hong Kong, 83 Tat

Chee Avenue, Kowloon, Hong Kong

[email protected] ABSTRACT In this paper, a new neighborhood structure is presented. The new neighborhood is obtained by performing a sequence of swaps between two timeslots, instead of only one move in the standard neighborhood structure. Based on new neighborhood, simulated annealing algorithm can solve the timetabling problem well. The computation results on two open benchmarks coming from two real-world high schools timetabling problems prove that the simulated annealing algorithm based on new neighborhood can compete with other effective approaches.

Multi-swarm Particle Swarm Optimization Based Risk Management Model for Virtual Enterprise 1,2

1,2

3

1,2

Fu-Qiang Lu , Min Huang , Wai-Ki Ching , Xing-Wei Wang , Xian-li Sun

1,2

1 College of Information Science and Engineering, Northeastern University 2 Key Laboratory of Integrated Automation of Process Industry (Northeastern University), Ministry of Education 3 Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong +86-2483671469, Shenyang, China, 110004

[email protected]

ABSTRACT Virtual Enterprise (VE) is a main scheme of enterprises in the 21st century. There are various risks for VE, due to VE’s agility and diversity of its members and its distributed characteristics. This paper presents a novel risk management model for VE, a Constructional Distributed Decision Making (CDDM) model. The model has two levels, namely, the top-model and the base-model, which describe the decision processes of the owner and the partners respectively. In this model, the situation of information symmetry between owner and partners is considered. The size of the search space will be very huge, when the number of members in VE, the number of risk factors and the number of actions increase. In addition, there are multiple members in VE. Considering the biological and computational motivations, a Multi-swarm Particle Swarm Optimization (MPSO) is then designed to solve the resulting optimization problem. Simulation results show the effectiveness of the proposed algorithm.

A Collaborative Optimized Genetic Algorithm Based on Regulation Mechanism of Neuroendocrine-Immune System Bao Liu Information and Control Engineering College, China University of Petroleum Dongying, 257061, P.R. [email protected]

Yongsheng Ding1,2 1 College of Information Sciences and Technology, Donghua University 2 Engineering Research Center of Digitized Textile & Fashion Technology, Ministry of Education,

Shanghai 201620, P.R. China [email protected]

Jun-Hong Wang Information and Control Engineering College, China University of Petroleum Dongying, 257061, P.R. China [email protected]

ABSTRACT In this paper, an improved collaborative optimized genetic algorithm (CGA) inspired from the modulation mechanism of neuroendocrine-immune system is presented. The CGA has several features as follows. The first is that the parent individuals are not involved in the copy process. The second is that more excellent individuals may be produced due to the adaptive crossover and variation probability based on the hormone modulation. In order to examine its performance, firstly, two typical test functions are selected as the simulation models. Then CGA is applied to an intelligent controller based on the modulation of epinephrine (EIC). The simulation results show that the CGA has quicker convergence rate and higher searching precision than that of immune genetic algorithm and normal genetic algorithm,

A Discrete Particle Swarm Optimization Algorithm with Fully Communicated Information Lu Qiang School of Automation Hangzhou Dianzi University Hangzhou, China

[email protected] Qiu Xue-na School of Telecommunication Ningbo University of Technology Ningbo, China

[email protected] Liu Shi-rong School of Automation Hangzhou Dianzi University Hangzhou,China

[email protected] ABSTRACT In this paper, a novel discrete particle swarm optimization (DPSO) algorithm is presented for solving the combinational optimization problems such as knapsack and clustering. The proposed algorithm mainly employs the idea of the information stored and exchanged among particles through Information-Shared Matrix (ISM). There are two reasons for using the idea. To begin with, the mechanism, storing and exchanging information, makes it possible to construct a discrete algorithm to solve combinational problems. Furthermore, the positions of particles in the space are adjusted according to not only historical information and global information current particles left, but also the information the other particles left. Therefore, information can be more sufficiently shared by each particle. The performance of DPSO algorithm is evaluated in comparison with well-known ACO algorithm, TS algorithm and other discrete PSO algorithms. Our computational simulations reveal very encouraging results in terms of the quality of solution

found.

Face Recognition Using Immune Network Based on Principal Component Analysis Guan-Chun Luh Tatung University No. 40, Sec. 3, Jhongshan N. Rd., Taipei City, Taiwan, ROC 886-2-25925252 Ext. 3410 Re-Ext. 806

[email protected] Ching-Chou Hsieh Tatung University No. 40, Sec. 3, Jhongshan N. Rd., Taipei City, Taiwan, ROC 886-2-25925252 Ext. 3410 Re-Ext. 804

[email protected] ABSTRACT This paper proposes a face recognition method using artificial immune network classifiers based on Principal Component Analysis (PCA). The PCA abstracts principal eigenvectors of the image in order to get best feature description, hence to reduce the number of inputs of immune networks. After this, these image data of reduced dimensions are input into an immune network to be trained. Subsequently the antibodies of the immune networks are optimized using genetic algorithms. The performance of the present method was evaluated using the AT&T Laboratories Cambridge database (formerly called ORL face database). The results show that this method gains higher recognition rate in contrast with some other methods.

Kernel-based Immunity Synergetic Network for Image Classification Xiuli Ma School of Communication and Information Engineering, Shanghai University, No.149 Yanchang Road, Shanghai 200072, China +86 21 5633 1619

[email protected] Guoqiang Mu Delphi China Technical Research Center, No.118 Delin Road, Shanghai 200131, China +86 21 2896 7503

[email protected] Xiaoqing Yu School of Communication and Information Engineering, Shanghai University, No.149 Yanchang Road, Shanghai 200072, China +86 21 5633 1619

[email protected] ABSTRACT In order to reduce the relativity among prototype pattern vectors and to enhance the separability among different patterns, a novel kernel-based learning algorithm of Synergetic Neural Network (SNN) is proposed. The algorithm first maps the data from original space into a new feature space and then classifies them by a two-layered SNN. An optimization method of weighted factors in the two-layered SNN is also presented. It gives different patterns to different weights and makes full use of the global and local searching ability of Immunity Clonal Algorithm (ICA). Experiments on Iris dataset, textural images and Synthetic Aperture Radar (SAR) images show that the new algorithm does not only improve the classification rate but also has shorter training and testing time.

Spectral Clustering Ensemble for Image Segmentation Xiuli Ma School of Communication and Information Engineering, Shanghai University, No.149 Yanchang Road, Shanghai 200072, China +86 21 5633 1619

[email protected] Wanggen Wan School of Communication and Information Engineering, Shanghai University, No.149 Yanchang Road, Shanghai 200072, China +86 21 5633 4945

[email protected] Licheng Jiao Institute of Intelligent Information Processing, Xidian University, No.2 South Taibai Road, Xi’an 710071, China +86 29 8820 2234

[email protected] ABSTRACT To make full use of information included in a dataset, a multiway spectral clustering algorithm with joint model is applied to image segmentation. To overcome the sensitivity of the joint modelbased multiway spectral clustering to kernel parameter and to produce the robust and stable segmentation results, spectral clustering ensemble algorithm is proposed in this paper, which can make full use of the built-in randomness of spectral clustering and the inaccuracy of Nystrom approximation to produce diversity. Experiments on UCI dataset, textural and SAR images show that, after cluster ensemble, the new algorithm is not only more robust but also better quality. Therefore, the new algorithm is effective

Fuzzy CMAC with Automatic State Partition for Reinforcement Learning

Huaqing Min South China University of Technology

[email protected] Jiaan Zeng South China University of Technology

[email protected] Ronghua Luo South China University of Technology

[email protected] ABSTRACT Most of reinforcement learning (RL) algorithms use value function to seek the optimal policy. In large or even continuous states, function approximation approaches must be used to represent value function. The structures of function approximators influence the learning performance greatly. However, the design of structures relies too much on human designer and inappropriate design can lead to poor performance. In this paper, we propose a novel function approximator called Fuzzy CMAC (FCMAC) with automatic state partition (ASP-FCMAC) to automate the structure design for FCMAC. Based on CMAC (also known as tile coding), ASP-FCMAC employs fuzzy membership function to lower the computation load, and analyzes Bellman error as well as learning weights to partition the state automatically so as to generate the structure of FCMAC. Empirical results in both mountain car and RoboCup Keepaway domains demonstrate that ASP-FCMAC can automatically generate the structure of FCMAC and agent using it can learn efficiently.

SO-Antnet for Improving Load Sharing in MANET Joseph C. Mushi Guanzheng Tan Central South University Central South University Changsha 410083, P. R. China Changsha 410083, P. R. China

[email protected] [email protected] ABSTRACT SO-Antnet introduces new idea of load balancing over mobile adhoc networks based on intelligent agents inspired by organic metaphor of ants’ food foraging behavior. With inspiration from Antnet approach, this study improves theoretical derivation of objective function by consider contribution of all four characteristics of ants’ foraging behavior to achieve SelfOrganization of a system. The study uses this objective function to optimize operation of intelligent agents, which collect information in mobile ad-hoc networks, to help the node to optimize route-cache contents and means of finding optimal path to particular destination. The study implements operational behavior of SO-Antnet by customizes DSR routing protocol modules in network simulator NS2. One major difference with other related work is that SO-Antnet simulation considers really cache implementation. Simulation results are compared with DSR performance, which show improvement in load balancing.

Virus-Evolutionary Genetic Algorithm Based Selective Ensemble Classifier for Pedestrian Detection B. Ning1,2, X.B. Cao1,2 , Y.W. Xu1,2, J. Zhang3 1Department

of Computer Science and Technology, University of Science and Technology of China, Hefei, 230026, P.R.China 2Anhui Province Key Laboratory of Software in Computing and Communication, Hefei, 230026, P.R.China 3School of Electronic and Information Engineering, Beihang University, Beijing, 100083, P.R.China [email protected],[email protected],[email protected],[email protected]

ABSTRACT In pedestrian detection system, it is critical to determine whether a candidate region contains a pedestrian both quickly and reliably. Therefore, an efficient classifier must be designed. In general, a well-organized assembly classifier outperforms than single classifiers. For pedestrian detection, due to the complexity of scene and vast number of candidate regions, an efficient ensemble method is needed. In this paper, we propose a virus evolutionary genetic algorithm (VEGA) based selective ensemble classifier for pedestrian detection system, in which only part of the trained learners are selected and participate the majority voting for the detection. Component learners are trained with diversity and then VEGA is employed to optimize the selection of component learners. Moreover, a time-spending factor is added to the fitness function so as to balance the detection rate and detection speed. Experiments show that, comparing with typical non-selective Bagging and GA-based selective ensemble method, the VEGAbased selective ensemble gets better performance not only in detecting accuracy but also in detection speed.

Hierarchical Oriented Genetic Algorithms for Coverage Path Planning of Multi-robot Teams with Load Balancing Metin Ozkan1 Ahmet Yazici1 Muzaffer Kapanoglu2 Osman Parlaktuna3 [email protected], [email protected], [email protected], [email protected] 1,2,3Eskisehir

Osmangazi University +90 222 2393750 1Department of Computer Eng., 2Department of Industrial Eng., 3Department of Electrical Eng., Batimeselik, 26480, Eskisehir, Turkey

ABSTRACT Multi-robot coverage path planning problems require every point in a given area to be covered by at least one member of the robot team using their sensors. For a time-efficient coverage, the environment needs to be partitioned among robots in a balanced manner. So the problem can be modeled as task assignment problem with load balancing. In this study, we propose two oriented genetic algorithms working in a hierarchical manner to deal with this problem. In the first phase, a previously proposed oriented genetic algorithm is used to find a single route with minimum repeated coverage. In the following phase, a directed genetic algorithm is used to partition the route among robots considering load balancing. The algorithm is coded in C++ and simulations are conducted using P3-DX mobile robots in the MobileSim environment. The hierarchical oriented genetic algorithm (HOGA) is also compared to the multi-robot spanning tree coverage (STC) approach in terms of load balancing. The comparison indicates competitive results over multi-robot STC.

A Multi-Objective-Based Non-Stationary UAV Assignment

Model for Constraints Handling using PSO Feng Pan Department of Automatic Control Beijing Institute of Technology Beijing, 100081, P.R.China +86-10-68948971

[email protected] Guanghui Wang Department of Automatic Control Beijing Institute of Technology Beijing, 100081, P.R.China +86-10-68948971

[email protected] Yang Liu Department of Automatic Control Beijing Institute of Technology Beijing, 100081, P.R.China +86-10-68948971

[email protected] ABSTRACT An unmanned aerial vehicle (UAV) assignment requires allocating vehicles to destinations to complete various jobs. It is a complex assignment problem with hard constraints, and potential dimensional explosion when the scenarios become more complicated and the size of problems increases. Moreover, the non-stationary UAV assignment problem, studied in the paper, is more difficult, since dynamic scenarios are introduced, e.g. change of the number, or different task requirements of targets and vehicle, etc. In this paper, a "Constraint-First-Objective- Next" model is proposed for the non-stationary problem. The proposed model can effectively handle constraints as an additional objective, including constraints expressed by nature language, and is flexible enough to be combined with kinds of intelligent computation algorithms. A local version of PSO is cooperated with the proposed model to solve non-stationary UAV assignment problems. Numerical experimental results illustrate that it can efficiently achieve the optima and demonstrate the effectiveness.

Cooperative Micro–Particle Swarm Optimization Konstantinos E. Parsopoulos Department of Mathematics University of Patras GR–26110 Patras, Greece

[email protected] ABSTRACT Cooperative approaches have proved to be very useful in evolutionary computation due to their ability to solve efficiently high-dimensional complex problems through the cooperation of low–dimensional subpopulations. On the other hand, Micro–evolutionary approaches employ very small populations of just a few individuals to provide solutions rapidly. However, the small population size renders them prone to search stagnation. This paper introduces Cooperative Micro– Particle Swarm Optimization, which employs cooperative low–dimensional and low–cardinality subswarms to concurrently adapt different subcomponents of high–dimensional optimization problems. The algorithm is applied on highdimensional instances of five widely used test problems with very promising results. Comparisons with the standard Particle

Swarm Optimization algorithm are also reported and Discussed

A Population Based Hybrid Meta-heuristic for the Uncapacitated Facility Location Problem Wayne Pullan School of Information and Communication Technology Griffith University, Gold Coast, QLD, Australia

[email protected] ABSTRACT The uncapacitated facility location problem is one of _nding the minimum cost subset of m facilities, where each facility has an associated establishment cost, to satisfy the demands of n users where the cost of satisfying each user from all possible facilities is known. In this paper, PBS, a population based metaheuristic for the uncapacitated facility location problem is introduced. PBS uses a genetic algorithm based meta-heuristic, primarily based on cut and paste crossover and directed mutation operators, to generate new starting points for a local search. For larger uncapacitated facility location instances, PBS is able to e_ectively utilise a number of computer processors. It is shown empirically that PBS achieves state-of-the-art performance for a wide range of uncapacitated facility location benchmark instances.

Target Tracking and Localization of Binocular Mobile Robot using Camshift and SIFT Qiu Xuena Institute of Automation East China University of Science and Technology, Shanghai, China

[email protected] Lu Qiang School of Automation Hangzhou Dianzi University Hangzhou,China

[email protected] ABSTRACT A real time dynamic target recognition and tracking method is presented for mobile robot in this paper. Firstly, the inter-frame difference method is applied to detect the moving target. And the proposed method computes the color histogram and extracts SIFT features in the target region. Then from the following frame, it extracts SIFT features, matches with SIFT features extracted from target, and calculates the center location of the matched features. Finally the Camshift algorithm, starting from the center location, is used to track the target. Experiments demonstrate that the proposed method can effectively recognize and track the moving target, and its performance is better than the classic Camshift algorithm.

Hierarchical Oriented Genetic Algorithms for Coverage

Path Planning of Multi-robot Teams with Load Balancing Metin Ozkan1 Ahmet Yazici1 Muzaffer Kapanoglu2 Osman Parlaktuna3 [email protected], [email protected], [email protected], [email protected] 1,2,3Eskisehir

Osmangazi University +90 222 2393750 1Department of Computer Eng., 2Department of Industrial Eng., 3Department of Electrical Eng., Batimeselik, 26480, Eskisehir, Turkey

ABSTRACT Multi-robot coverage path planning problems require every point in a given area to be covered by at least one member of the robot team using their sensors. For a time-efficient coverage, the environment needs to be partitioned among robots in a balanced manner. So the problem can be modeled as task assignment problem with load balancing. In this study, we propose two oriented genetic algorithms working in a hierarchical manner to deal with this problem. In the first phase, a previously proposed oriented genetic algorithm is used to find a single route with minimum repeated coverage. In the following phase, a directed genetic algorithm is used to partition the route among robots considering load balancing. The algorithm is coded in C++ and simulations are conducted using P3-DX mobile robots in the MobileSim environment. The hierarchical oriented genetic algorithm (HOGA) is also compared to the multi-robot spanning tree coverage (STC) approach in terms of load balancing. The comparison indicates competitive results over multi-robot STC.

A Multi-Objective-Based Non-Stationary UAV Assignment Model for Constraints Handling using PSO Feng Pan Department of Automatic Control Beijing Institute of Technology Beijing, 100081, P.R.China +86-10-68948971

[email protected] Guanghui Wang Department of Automatic Control Beijing Institute of Technology Beijing, 100081, P.R.China +86-10-68948971

[email protected] Yang Liu Department of Automatic Control Beijing Institute of Technology Beijing, 100081, P.R.China +86-10-68948971

[email protected] ABSTRACT An unmanned aerial vehicle (UAV) assignment requires allocating vehicles to destinations to complete various jobs. It is a complex assignment problem with hard constraints, and potential dimensional explosion when the scenarios become more complicated and the size of problems increases. Moreover, the non-stationary UAV assignment problem, studied in the paper, is more difficult, since dynamic scenarios are introduced, e.g. change of the number, or different task requirements of targets and vehicle, etc. In this paper, a "Constraint-First-Objective- Next" model is proposed for the non-stationary problem. The proposed

model can effectively handle constraints as an additional objective, including constraints expressed by nature language, and is flexible enough to be combined with kinds of intelligent computation algorithms. A local version of PSO is cooperated with the proposed model to solve non-stationary UAV assignment problems. Numerical experimental results illustrate that it can efficiently achieve the optima and demonstrate the effectiveness.

Cooperative Micro–Particle Swarm Optimization Konstantinos E. Parsopoulos Department of Mathematics University of Patras GR–26110 Patras, Greece

[email protected] ABSTRACT Cooperative approaches have proved to be very useful in evolutionary computation due to their ability to solve efficiently high-dimensional complex problems through the cooperation of low–dimensional subpopulations. On the other hand, Micro–evolutionary approaches employ very small populations of just a few individuals to provide solutions rapidly. However, the small population size renders them prone to search stagnation. This paper introduces Cooperative Micro– Particle Swarm Optimization, which employs cooperative low–dimensional and low–cardinality subswarms to concurrently adapt different subcomponents of high–dimensional optimization problems. The algorithm is applied on highdimensional instances of five widely used test problems with very promising results. Comparisons with the standard Particle Swarm Optimization algorithm are also reported and discussed.

A Population Based Hybrid Meta-heuristic for the Uncapacitated Facility Location Problem Wayne Pullan School of Information and Communication Technology Griffith University, Gold Coast, QLD, Australia

[email protected] ABSTRACT The uncapacitated facility location problem is one of _nding the minimum cost subset of m facilities, where each facility has an associated establishment cost, to satisfy the demands of n users where the cost of satisfying each user from all possible facilities is known. In this paper, PBS, a population based metaheuristic for the uncapacitated facility location problem is introduced. PBS uses a genetic algorithm based meta-heuristic, primarily based on cut and paste crossover and directed mutation operators, to generate new starting points for a local search. For larger uncapacitated facility location instances, PBS is able to e_ectively utilise a number of computer processors. It is shown empirically that PBS achieves state-of-the-art performance for a wide range of uncapacitated facility location benchmark instances.

Target Tracking and Localization of Binocular Mobile Robot using Camshift and SIFT

Qiu Xuena Institute of Automation East China University of Science and Technology, Shanghai, China

[email protected] Lu Qiang School of Automation Hangzhou Dianzi University Hangzhou,China

[email protected] ABSTRACT A real time dynamic target recognition and tracking method is presented for mobile robot in this paper. Firstly, the inter-frame difference method is applied to detect the moving target. And the proposed method computes the color histogram and extracts SIFT features in the target region. Then from the following frame, it extracts SIFT features, matches with SIFT features extracted from target, and calculates the center location of the matched features. Finally the Camshift algorithm, starting from the center location, is used to track the target. Experiments demonstrate that the proposed method can effectively recognize and track the moving target, and its performance is better than the classic Camshift algorithm.

Embedded Self-Adaptation to Escape from Local Optima Oleg Rokhlenko IBM Research Lab., Haifa, Israel

[email protected] Ydo Wexler Microsoft Research, Redmond, USA

[email protected] ABSTRACT Self-adaptation in genetic algorithms has been suggested as a strategy to enhance evolutionary algorithms for optimization tasks. We consider continuous self-adaptation schemes called strategies that are governed by evolutionary rules, and suggest to incorporate multiple strategies to improve the performance of genetic algorithms. We show that employing multiple strategies, and letting evolutionary pressure steer adaptation, can overcome the problem of premature convergence. To demonstrate the power of our method we apply it to optimization problems of uncapacitated facility location. The method outperforms both methods with a single strategy and previously reported methods on several benchmarks. In these runs, algorithms that incorporate multiple strategies avoid getting stuck in local optimum, and converge to better solutions.

Bacterial Foraging Optimization Algorithm with Particle Swarm Optimization Strategy for Global Numerical Optimization Hai Shen _

Key Laboratory of Industrial

Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, China Graduate School of the Chinese Academy of Sciences, China College of Physics Science and Technology, Shenyang Normal University, China

[email protected] Yunlong Zhu Key Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, China

[email protected] Xiaoming Zhou Key Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, China Graduate School of the Chinese Academy of Sciences, China

[email protected] Haifeng Guo Key Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, China

[email protected] Chunguang Chang Key Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, China

[email protected] ABSTRACT In 2002, K. M. Passino proposed Bacterial Foraging Optimization Algorithm (BFOA) for distributed optimization and control. One of the major driving forces of BFOA is the chemotactic movement of a virtual bacterium that models a trial solution of the optimization problem. However, during the process of chemotaxis, the BFOA depends on random search directions which may lead to delay in reaching the global solution. Recently, a new algorithm BFOA oriented by PSO termed BF-PSO has shown superior in proportional integral derivative controller tuning application. In order to examine the global search capability of BF-PSO, we evaluate the performance of BFOA and BF-PSO on 23 numerical benchmark functions. In BF-PSO, the search directions of tumble behavior for each bacterium oriented by the individual’s best location and the global best location. The experimental results show that BFPSO performs much better than BFOA for almost all test functions. That’s approved that the BFOA oriented by PSO strategy improve its global optimization capability.

The Study of the Knowledge Optimization Tool Akira Otsuki Science and Engineering, Keio University Tokyo, Japan, [email protected]

Kenichi Okada Science and Engineering, Keio University Tokyo, Japan [email protected]

ABSTRACT In this study, a tool was constructed that supports the process of tying to organize knowledge newly created after tacit knowledge has been optimized by applying knowledge management strategies, 3C(“Customer”, “Company” and “Competitor” Analysis), a marketing mix, and various enumeration methods. The tool was verified by quantitative methods, user feedback studies, and evaluation through comparison with similar tools. Until now, though some theories regarding the organization of newly created knowledge have been advocated based on user feedback studies, the method of concretely applying such theories to real-world business circumstances has not been presented. In the current study, the tool was tested through use in an actual administrative project and proved to be more effective than an already-existing tool used for the organization of newly-created knowledge.

Hierarchical Oriented Genetic Algorithms for Coverage Path Planning of Multi-robot Teams with Load Balancing Metin Ozkan1 Ahmet Yazici1 Muzaffer Kapanoglu2 Osman Parlaktuna3 [email protected], [email protected], [email protected], [email protected] 1,2,3Eskisehir

Osmangazi University +90 222 2393750 1Department of Computer Eng., 2Department of Industrial Eng., 3Department of Electrical Eng., Batimeselik, 26480, Eskisehir, Turkey

ABSTRACT Multi-robot coverage path planning problems require every point in a given area to be covered by at least one member of the robot team using their sensors. For a time-efficient coverage, the environment needs to be partitioned among robots in a balanced manner. So the problem can be modeled as task assignment problem with load balancing. In this study, we propose two oriented genetic algorithms working in a hierarchical manner to deal with this problem. In the first phase, a previously proposed oriented genetic algorithm is used to find a single route with minimum repeated coverage. In the following phase, a directed genetic algorithm is used to partition the route among robots considering load balancing. The algorithm is coded in C++ and simulations are conducted using P3-DX mobile robots in the MobileSim environment. The hierarchical oriented genetic algorithm (HOGA) is also compared to the multi-robot spanning tree coverage (STC) approach in terms of load balancing. The comparison indicates competitive results over multi-robot STC.

A Multi-Objective-Based Non-Stationary UAV Assignment Model for Constraints Handling using PSO

Feng Pan Department of Automatic Control Beijing Institute of Technology Beijing, 100081, P.R.China +86-10-68948971

[email protected] Guanghui Wang Department of Automatic Control Beijing Institute of Technology Beijing, 100081, P.R.China +86-10-68948971

[email protected] Yang Liu Department of Automatic Control Beijing Institute of Technology Beijing, 100081, P.R.China +86-10-68948971

[email protected] ABSTRACT An unmanned aerial vehicle (UAV) assignment requires allocating vehicles to destinations to complete various jobs. It is a complex assignment problem with hard constraints, and potential dimensional explosion when the scenarios become more complicated and the size of problems increases. Moreover, the non-stationary UAV assignment problem, studied in the paper, is more difficult, since dynamic scenarios are introduced, e.g. change of the number, or different task requirements of targets and vehicle, etc. In this paper, a "Constraint-First-Objective- Next" model is proposed for the non-stationary problem. The proposed model can effectively handle constraints as an additional objective, including constraints expressed by nature language, and is flexible enough to be combined with kinds of intelligent computation algorithms. A local version of PSO is cooperated with the proposed model to solve non-stationary UAV assignment problems. Numerical experimental results illustrate that it can efficiently achieve the optima and demonstrate the effectiveness.

Cooperative Micro–Particle Swarm Optimization Konstantinos E. Parsopoulos Department of Mathematics University of Patras GR–26110 Patras, Greece

[email protected] ABSTRACT Cooperative approaches have proved to be very useful in evolutionary computation due to their ability to solve efficiently high-dimensional complex problems through the cooperation of low–dimensional subpopulations. On the other hand, Micro–evolutionary approaches employ very small populations of just a few individuals to provide solutions rapidly. However, the small population size renders them prone to search stagnation. This paper introduces Cooperative Micro– Particle Swarm Optimization, which employs cooperative low–dimensional and low–cardinality subswarms to concurrently adapt different subcomponents of high–dimensional optimization problems. The algorithm is applied on highdimensional instances of five widely used test problems with very promising results. Comparisons with the standard Particle Swarm Optimization algorithm are also reported and discussed.

A Population Based Hybrid Meta-heuristic for the Uncapacitated Facility Location Problem Wayne Pullan School of Information and Communication Technology Griffith University, Gold Coast, QLD, Australia

[email protected] ABSTRACT The uncapacitated facility location problem is one of _nding the minimum cost subset of m facilities, where each facility has an associated establishment cost, to satisfy the demands of n users where the cost of satisfying each user from all possible facilities is known. In this paper, PBS, a population based metaheuristic for the uncapacitated facility location problem is introduced. PBS uses a genetic algorithm based meta-heuristic, primarily based on cut and paste crossover and directed mutation operators, to generate new starting points for a local search. For larger uncapacitated facility location instances, PBS is able to e_ectively utilise a number of computer processors. It is shown empirically that PBS achieves state-of-the-art performance for a wide range of uncapacitated facility location benchmark instances.

Target Tracking and Localization of Binocular Mobile Robot using Camshift and SIFT Qiu Xuena Institute of Automation East China University of Science and Technology, Shanghai, China

[email protected] Lu Qiang School of Automation Hangzhou Dianzi University Hangzhou,China

[email protected] ABSTRACT A real time dynamic target recognition and tracking method is presented for mobile robot in this paper. Firstly, the inter-frame difference method is applied to detect the moving target. And the proposed method computes the color histogram and extracts SIFT features in the target region. Then from the following frame, it extracts SIFT features, matches with SIFT features extracted from target, and calculates the center location of the matched features. Finally the Camshift algorithm, starting from the center location, is used to track the target. Experiments demonstrate that the proposed method can effectively recognize and track the moving target, and its performance is better than the classic Camshift algorithm.

Embedded Self-Adaptation to Escape from Local Optima Oleg Rokhlenko IBM Research Lab., Haifa, Israel

[email protected] Ydo Wexler

Microsoft Research, Redmond, USA

[email protected] ABSTRACT Self-adaptation in genetic algorithms has been suggested as a strategy to enhance evolutionary algorithms for optimization tasks. We consider continuous self-adaptation schemes called strategies that are governed by evolutionary rules, and suggest to incorporate multiple strategies to improve the performance of genetic algorithms. We show that employing multiple strategies, and letting evolutionary pressure steer adaptation, can overcome the problem of premature convergence. To demonstrate the power of our method we apply it to optimization problems of uncapacitated facility location. The method outperforms both methods with a single strategy and previously reported methods on several benchmarks. In these runs, algorithms that incorporate multiple strategies avoid getting stuck in local optimum, and converge to better solutions.

Bacterial Foraging Optimization Algorithm with Particle Swarm Optimization Strategy for Global Numerical Optimization Hai Shen _

Key Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, China Graduate School of the Chinese Academy of Sciences, China College of Physics Science and Technology, Shenyang Normal University, China

[email protected] Yunlong Zhu Key Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, China

[email protected] Xiaoming Zhou Key Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, China Graduate School of the Chinese Academy of Sciences, China

[email protected] Haifeng Guo Key Laboratory of Industrial Informatics, Shenyang

Institute of Automation, Chinese Academy of Sciences, China

[email protected] Chunguang Chang Key Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, China

[email protected] ABSTRACT In 2002, K. M. Passino proposed Bacterial Foraging Optimization Algorithm (BFOA) for distributed optimization and control. One of the major driving forces of BFOA is the chemotactic movement of a virtual bacterium that models a trial solution of the optimization problem. However, during the process of chemotaxis, the BFOA depends on random search directions which may lead to delay in reaching the global solution. Recently, a new algorithm BFOA oriented by PSO termed BF-PSO has shown superior in proportional integral derivative controller tuning application. In order to examine the global search capability of BF-PSO, we evaluate the performance of BFOA and BF-PSO on 23 numerical benchmark functions. In BF-PSO, the search directions of tumble behavior for each bacterium oriented by the individual’s best location and the global best location. The experimental results show that BFPSO performs much better than BFOA for almost all test functions. That’s approved that the BFOA oriented by PSO strategy improve its global optimization capability.

Nodes Localization in Sensor Networks based on Vectors and Particle Swarm Optimization Wang Yu-feng School of Automation Beihang University Beijing 100191, China

[email protected] Wang Yan School of Automation Beihang University Beijing 100191, China

[email protected] Mu Chao-yi Xi’an Research Institute of Applied Optics Xi’an 710065, China

[email protected] ABSTRACT This paper proposed a design of an integrated algorithm based on DV-Hop. A Location Correction Vector (LCV) was constructed by the difference between estimated distance and range measurement, nodes were clustered when anchors were heads of clusters, where object function expressing total distance error was constructed in a cluster. Particle Swarm Optimization (PSO) was used to solve the minimization problem, then correction steps of all member nodes had been done; the value of location correction equals the product of LCV and step; then extra location correction had been executed by using the relative positions among edge

nodes of neighbor clusters. Simulation results show that the localization error of DV-Hop has been reduced by 75% and indicate that the present algorithm is also applicable to lowdensity networks.

A Novel Robust Background Modeling Algorithm for Complex Natural Scenes Wang Zhi-Ling Department of Automation University of Science and Technology of China Hefei, 230027 P.R. China

[email protected] Chen Zong-Hai Department of Automation, University of Science and Technology of China; Hefei, 230027, P.R. China National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100080, P.R. China

[email protected] Chen Hui-Yong Department of Automation University of Science and Technology of China Hefei, 230027 P.R. China

[email protected] ABSTRACT Background modeling is fundamentally important in the computer vision tasks such as image understanding, object tracking and video surveillance. It is especially difficult in the complex natural scenes, mainly due to two matters: 1) gross errors resulted by random outliers that can not be described in a uniform distribution; 2) structural confusion cluttered by sample sets’ polymorphism, which is originated by multiple structures. For dealing with these problems, a novel robust background modeling algorithm is presented. The model is established by an improved Multi-RANSAC approach for dynamic background pixels and by one-tail trimmed sample mean estimator for static pixels. A threecomponentset is derived for the model so that it can be updated quickly in a unified framework for both types. It stands right even when there are more than 70 percent outliers and is fit for complex natural scenes. Quantitative evaluation and comparisons with traditional methods show that the proposed method has much improved results.

Dynamic Output Feedback Control of Uncertain Networked Control Systems Weihua Deng Shanghai Key Laboratory of Power Station Automation Technology, Shanghai University

[email protected] Minrui Fei Shanghai Key Laboratory of Power Station Automation Technology, Shanghai University

[email protected] ABSTRACT The paper focuses on the problem of output feedback control for uncertain networked control systems(NCSs) that possess random time-delay which is described by a Markov process. Based on Lyapunov-Razumikhin method a dynamic output feedback controller is proposed to stabilize the class of NCSs. A su±cient condition for existence of such controller is given in terms of bilinear matrix inequalities (BMIs). A modi¯ed algorithm is used to solve the BMIs. A numerical example illustrates the utility of the proposed approach.

Evolving Distributed Algorithms with Genetic Programming: Election Thomas Weise Distributed Systems Group University of Kassel 34121 Kassel, Germany

[email protected] Michael Zapf Distributed Systems Group University of Kassel 34121 Kassel, Germany

[email protected] ABSTRACT In this paper, we present a detailed analysis of the application of Genetic Programming to the evolution of distributed algorithms. This research field has many facets which make it especially difficult. These aspects are discussed and countermeasures are provided. Six different Genetic Programming approaches (SGP, eSGP, LGP, RBGP, eRBGP, and Fraglets) are applied to the election problem as case study utilizing these countermeasures. The results of the experiments are analyzed statistically and discussed thoroughly.

Why Evolution Is Not a Good Paradigm For Program Induction; A Critique of Genetic Programming John R. Woodward University of Nottingham 199, Taikang East Road, University Park Ningbo, 315100, People’s Republic of China

John.Woodward @Nottingham.edu.cn Ruibin Bai University of Nottingham 199, Taikang East Road, University Park Ningbo, 315100, People’s Republic of China

Ruibin.Bai @Nottingham.edu.cn ABSTRACT We revisit the roots of Genetic Programming (i.e. Natural

Evolution), and conclude that the mechanisms of the process of evolution (i.e. selection, inheritance and variation) are highly suited to the process; genetic code is an e_ective transmitter of information and crossover is an e_ective way to search through the viable combinations. Evolution is not without its limitations, which are pointed out, and it appears to be a highly e_ective problem solver; however we over-estimate the problem solving ability of evolution, as it is often trying to solve \self-imposed" survival problems. We are concerned with the evolution of Turing Equivalent programs (i.e. those with iteration and memory). Each of the mechanisms which make evolution work so well are examined from the perspective of program induction. Computer code is not as robust as genetic code, and therefore poorly suited to the process of evolution, resulting in a insurmountable landscape which cannot be navigated e_ectively with current syntax based genetic operators. Crossover, has problems being adopted in a computational setting, primarily due to a lack of context of exchanged code. A review of the literature reveals that evolved programs contain at most two nested loops, indicating that a glass ceiling to what can currently be accomplished.

Topology Optimization of Structures Using Ant Colony Optimization Chun-Yin Wu Department of Mechanical Engineering, Tatung University, Taipei, Taiwan, R.O.C.

[email protected] Ching-Bin Zhang Department of Mechanical Engineering, Tatung University, Taipei, Taiwan, R.O.C.

[email protected] Chi-Jer Wang Department of Mechanical Engineering, Tatung University, Taipei, Taiwan, R.O.C.

[email protected] Abstract A modified ACO algorithm that derives from specific definition of pheromone and cooperation mechanism between ants was applied for solving topology optimization problem of structure. Mesh topology of finite element model for structure was treated as possible paths for ant’s movement. A tour on mesh topology map for seeking food finished by ant is transformed into a structure and the finite element method was applied to analyze the structure for calculating pheromone deposited on the path. The amount of accumulated pheromone deposited on every element by different ants was used to find optimum structural design. From the results studied in this paper, the purposed ACO algorithm provides as alternate optimization method that has high potential in finding the best design for topology optimization of structure successfully and efficiently.

A Global Optimization Based on Physicomimetics

Framework Li-Ping Xie 1 College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050 2 Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, Shanxi, P.R. China, 030024

[email protected] Jian-Chao Zeng Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, Shanxi, P.R. China, 030024

[email protected] ABSTRACT Based on physicomimetics framework, this paper presents a global optimization algorithm inspired by physics, which is a stochastic population-based algorithm. In the approach, each physical individual has a position and velocity which move through the feasible region of global optimization problem under the influence of gravity. The virtual mass of each individual corresponds to a user-defined function of the value of an objective function to be optimized. An attraction-repulsion rule is constructed among individuals and utilized to move individuals towards the optimality. Experimental simulations show that the algorithm is effective.

Nodes Localization in Sensor Networks based on Vectors and Particle Swarm Optimization Wang Yu-feng School of Automation Beihang University Beijing 100191, China

[email protected] Wang Yan School of Automation Beihang University Beijing 100191, China

[email protected] Mu Chao-yi Xi’an Research Institute of Applied Optics Xi’an 710065, China

[email protected] ABSTRACT This paper proposed a design of an integrated algorithm based on DV-Hop. A Location Correction Vector (LCV) was constructed by the difference between estimated distance and range measurement, nodes were clustered when anchors were heads of clusters, where object function expressing total distance error was constructed in a cluster. Particle Swarm Optimization (PSO) was used to solve the minimization problem, then correction steps of all member nodes had been done; the value of location correction equals the product of LCV and step; then extra location correction had been executed by using the relative positions among edge nodes of neighbor clusters. Simulation results show that the localization error of DV-Hop has been reduced by 75% and indicate that the present algorithm is also applicable to lowdensity networks.

A Novel Robust Background Modeling Algorithm for Complex Natural Scenes Wang Zhi-Ling Department of Automation University of Science and Technology of China Hefei, 230027 P.R. China

[email protected] Chen Zong-Hai Department of Automation, University of Science and Technology of China; Hefei, 230027, P.R. China National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100080, P.R. China

[email protected] Chen Hui-Yong Department of Automation University of Science and Technology of China Hefei, 230027 P.R. China

[email protected] ABSTRACT Background modeling is fundamentally important in the computer vision tasks such as image understanding, object tracking and video surveillance. It is especially difficult in the complex natural scenes, mainly due to two matters: 1) gross errors resulted by random outliers that can not be described in a uniform distribution; 2) structural confusion cluttered by sample sets’ polymorphism, which is originated by multiple structures. For dealing with these problems, a novel robust background modeling algorithm is presented. The model is established by an improved Multi-RANSAC approach for dynamic background pixels and by one-tail trimmed sample mean estimator for static pixels. A threecomponentset is derived for the model so that it can be updated quickly in a unified framework for both types. It stands right even when there are more than 70 percent outliers and is fit for complex natural scenes. Quantitative evaluation and comparisons with traditional methods show that the proposed method has much improved results.

Dynamic Output Feedback Control of Uncertain Networked Control Systems Weihua Deng Shanghai Key Laboratory of Power Station Automation Technology, Shanghai University

[email protected] Minrui Fei Shanghai Key Laboratory of Power Station

Automation Technology, Shanghai University

[email protected] ABSTRACT The paper focuses on the problem of output feedback control for uncertain networked control systems(NCSs) that possess random time-delay which is described by a Markov process. Based on Lyapunov-Razumikhin method a dynamic output feedback controller is proposed to stabilize the class of NCSs. A su±cient condition for existence of such controller is given in terms of bilinear matrix inequalities (BMIs). A modi¯ed algorithm is used to solve the BMIs. A numerical example illustrates the utility of the proposed approach.

Evolving Distributed Algorithms with Genetic Programming: Election Thomas Weise Distributed Systems Group University of Kassel 34121 Kassel, Germany

[email protected] Michael Zapf Distributed Systems Group University of Kassel 34121 Kassel, Germany

[email protected] ABSTRACT In this paper, we present a detailed analysis of the application of Genetic Programming to the evolution of distributed algorithms. This research field has many facets which make it especially difficult. These aspects are discussed and countermeasures are provided. Six different Genetic Programming approaches (SGP, eSGP, LGP, RBGP, eRBGP, and Fraglets) are applied to the election problem as case study utilizing these countermeasures. The results of the experiments are analyzed statistically and discussed thoroughly.

Canonical Representation Genetic Programming John R. Woodward University of Nottingham 199, Taikang East Road, University Park Ningbo, 315100, People’s Republic of China

John.Woodward @Nottingham.edu.cn Ruibin Bai University of Nottingham 199, Taikang East Road, University Park Ningbo, 315100, People’s Republic of China

Ruibin.Bai @Nottingham.edu.cn ABSTRACT Search spaces sampled by the process of Genetic Programming often consist of programs which can represent a function in many di_erent ways. Thus, when the space is examined it is highly likely that di_erent programs may be tested which represent the same function, which is an undesirable waste of resources. It is argued that, if a search space can be constructed where only unique representations of a function are permitted, then this will be more successful than

employing multiple representations. When the search space consists of canonical representations it is called a canonical search space, and when Genetic Programming is applied to this search space, it is called Canonical Representation Genetic Programming. The challenge lies in constructing these search spaces. With some function sets this is a trivial task, and with some function sets this is impossible to achieve. With other function sets it is not clear how the goal can be achieved. In this paper, we speci_cally examine the search space de_ned by the function set f+; ; _; =g and the terminal set fx; 1g. Drawing inspiration from the fundamental theorem of arithmetic, and results regarding the fundamental theorem of algebra, we construct a representation where each function that can be constructed with this primitive set has a unique representation.

Why Evolution Is Not a Good Paradigm For Program Induction; A Critique of Genetic Programming John R. Woodward University of Nottingham 199, Taikang East Road, University Park Ningbo, 315100, People’s Republic of China

John.Woodward @Nottingham.edu.cn Ruibin Bai University of Nottingham 199, Taikang East Road, University Park Ningbo, 315100, People’s Republic of China

Ruibin.Bai @Nottingham.edu.cn ABSTRACT We revisit the roots of Genetic Programming (i.e. Natural Evolution), and conclude that the mechanisms of the process of evolution (i.e. selection, inheritance and variation) are highly suited to the process; genetic code is an e_ective transmitter of information and crossover is an e_ective way to search through the viable combinations. Evolution is not without its limitations, which are pointed out, and it appears to be a highly e_ective problem solver; however we over-estimate the problem solving ability of evolution, as it is often trying to solve \self-imposed" survival problems. We are concerned with the evolution of Turing Equivalent programs (i.e. those with iteration and memory). Each of the mechanisms which make evolution work so well are examined from the perspective of program induction. Computer code is not as robust as genetic code, and therefore poorly suited to the process of evolution, resulting in a insurmountable landscape which cannot be navigated e_ectively with current syntax based genetic operators. Crossover, has problems being adopted in a computational setting, primarily due to a lack of context of exchanged code. A review of the literature reveals that evolved programs contain at most two nested loops, indicating that a glass ceiling to what can currently be accomplished.

Topology Optimization of Structures Using Ant Colony Optimization Chun-Yin Wu Department of Mechanical Engineering, Tatung University,

Taipei, Taiwan, R.O.C.

[email protected] Ching-Bin Zhang Department of Mechanical Engineering, Tatung University, Taipei, Taiwan, R.O.C.

[email protected] Chi-Jer Wang Department of Mechanical Engineering, Tatung University, Taipei, Taiwan, R.O.C.

[email protected] Abstract A modified ACO algorithm that derives from specific definition of pheromone and cooperation mechanism between ants was applied for solving topology optimization problem of structure. Mesh topology of finite element model for structure was treated as possible paths for ant’s movement. A tour on mesh topology map for seeking food finished by ant is transformed into a structure and the finite element method was applied to analyze the structure for calculating pheromone deposited on the path. The amount of accumulated pheromone deposited on every element by different ants was used to find optimum structural design. From the results studied in this paper, the purposed ACO algorithm provides as alternate optimization method that has high potential in finding the best design for topology optimization of structure successfully and efficiently

A Global Optimization Based on Physicomimetics Framework Li-Ping Xie 1 College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050 2 Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, Shanxi, P.R. China, 030024

[email protected] Jian-Chao Zeng Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, Shanxi, P.R. China, 030024

[email protected] ABSTRACT Based on physicomimetics framework, this paper presents a global optimization algorithm inspired by physics, which is a stochastic population-based algorithm. In the approach, each physical individual has a position and velocity which move through the feasible region of global optimization problem under the influence of gravity. The virtual mass of each individual corresponds to a user-defined function of the value of an objective function to be optimized. An attraction-repulsion rule is constructed among individuals and utilized to move individuals towards the optimality. Experimental simulations show that the algorithm is effective.

The Stability Study of Biped Robot Based

on GA and Neural Network Lun Xie School of Information Engineering, University of Science and Technology Beijing, Beijing 100083, P.R. China 13681560734

[email protected] Zhiliang Wang School of Information Engineering, University of Science and Technology Beijing, Beijing 100083, P.R. China 13910727340

[email protected] Kun Wu School of Information Engineering, University of Science and Technology Beijing, Beijing 100083, P.R. China 15010272215

[email protected] ABSTRACT In recent years, the Biped Robot is more and more selfdetermining and time-sensitive, so the stability has become a very important question. But the traditional control methods can not meet it. To solve this question, Artificial Neural Network (ANN) has been brought up. Instead of most traditional control methods, Artificial Neural Network is applied widely to control the Biped Robot to walk accurately and stably. In this work, we design a control system of the Biped Robot with GA-ANN (Artificial Neural Network based on Genetic Algorithm). The GA-ANN control system adjusts the weights by the robot’s Zero Moment Point (ZMP), tracks the robot’s nonlinear kinetic system and keeps the robot step stably. Experiments show the stability improvement of robot using proposed algorithm.

Problem Difficulty Analysis for Particle Swarm Optimization: Deception and Modality Bin Xin Laboratory of Complex System Intelligent Control and Decision China Ministry of Education Department of Automatic Control Beijing Institute of Technology Beijing, 100081, China 86-10-68912463

[email protected] Jie Chen Laboratory of Complex System Intelligent Control and Decision China Ministry of Education Department of Automatic Control Beijing Institute of Technology Beijing, 100081, China 86-10-68913748

[email protected] Feng Pan Laboratory of Complex System

Intelligent Control and Decision China Ministry of Education Department of Automatic Control Beijing Institute of Technology Beijing, 100081, China 86-10-68912463

[email protected] ABSTRACT This paper studies the problem difficulty for a popular optimization method - particle swarm optimization (PSO), particularly for the PSO variant PSO-cf (PSO with constriction factor), and analyzes its predictive measures. Some previous measures and related issues about other optimizers, mainly including deception and modality, are checked for PSO. It is observed that deception is mainly the combination of three factors – the measure ratios of attraction basins, the relative distance of attractors and the relative difference of attractors’ altitudes. Multimodality and multi-funnel are proved not to be the essential factors contributing to the problem difficulty for PSO. The counterexamples and comparative experiments in this paper can be taken as a reference for further researches on novel comprehensive predictive measures of problem difficulty for PSO.

On Average Time Complexity of Evolutionary Negative Selection Algorithms for Anomaly Detection Baoliang Xu1,2, Wenjian Luo1,2, Xingxin Pei1,2, Min Zhang1,2, Xufa Wang1,2 1Nature

Inspired Computation and Applications Laboratory, Department of Computer Science and Technology, University of Science and Technology of China, Hefei, 230027, Anhui, China 2Anhui Key Laboratory of Software in Computing and Communication, University of Science and Technology of China, Hefei 230027, China

[email protected], [email protected], [email protected], [email protected], [email protected] ABSTRACT Evolutionary Negative Selection Algorithms have been proposed and used in artificial immune system community for years. However, there are no theoretical analyses about the average time complexity of such algorithms. In this paper, the average time complexity of Evolutionary Negative Selection Algorithms for anomaly detection is studied, and the results demonstrate that its average time complexity depends on the self set very much. Some simulation experiments are done, and it is demonstrated that the theoretical results approximately agree with the experimental results. The work in this paper not only gives the average time complexity of Evolutionary Negative Selection Algorithms for the first time, but also would be helpful to understand why different immune responses (i.e. primary/cross-reactive/secondary immune response) in biological immune system have different efficiencies.

Adaptive Immune Genetic Algorithm for Logic Circuit Design Hai-Qin Xu College of Information Sciences and Technology, Donghua University Shanghai 201620, P. R. China [email protected]

Yong-Sheng Ding* College of Information Sciences and Technology, Donghua University Engineering Research Center of Digitized Textile & Fashion Technology, Ministry of Education Shanghai 201620, P. R. China * [email protected]

Zhi-Hua Hu College of Information Sciences and Technology, Donghua University Shanghai 201620, P. R. China [email protected]

ABSTRACT Evolutionary design of circuits (EDC), an important branch of evolvable hardware which emphasizes circuit design, is a promising way to realize automated design of electronic circuits. In order to improve the evolutionary design of logic circuits in a more efficient, scalable and capable way, an Adaptive Immune Genetic Algorithm (AIGA) was designed. The AIGA draws into the mechanisms in biological immune systems such as clonal selection, hypermutation, and immune memory. Besides, the AIGA features an adaptation strategy that enables crossover probability and mutation probability to vary with genetic-search process. Our results are compared with those produced by the Multi-objective Evolutionary Algorithm (MOEA) and the Simple Immune Algorithm (SIA). The simulation results show that AIGA overcomes the disadvantages of premature convergence, and improves the global searching efficiency and capability.

Energy-saving Control of Greenhouse Climate Based on MOCC Strategy ¤ Lihong Xu y

Member,ACM Department of Control Science and Engineering Tongji University, Shanghai, China, 200092

[email protected] Haigen Hu z

Department of Control Science and Engineering Tongji University, Shanghai, China, 200092

[email protected] Bingkun Zhu Department of Control Science and Engineering Tongji University, Shanghai, China, 200092

ABSTRACT The adjustment of greenhouse environment has heavy in°uence on the plants growth, production yield, quality and energy consumption. Moreover, classical methods used for

solving greenhouse environment multi-objective control problems may be more reasonable by adopting "region" control objectives instead of "point" control objectives. In this paper, we propose a novel energy-saving control algorithm, and employ Multi-Objective Compatible Control(MOCC) strategy and an extant greenhouse model to optimize the control parameters of greenhouse environment for short timescale prediction(15 minutes). A series of optimization experiments using Multi-Objective Evolutionary Algorithms(MOEAs) are presented to minimize energy consumption under certain compatible control "region" conditions. The results are encouraging, and show that the proposed method may be valuable and helpful to formulate environmental control strategies, to pursue less energy cost, and to gradually realize the ultimate objectives of environmental optimal control.

An Improved MOCC with Feedback Control Structure Based on Preference Lihong Xu Member, ACM Department of Control Science and Engineering Tongji University, Shanghai,China, 200092

[email protected] Zhu Department of Control Science and Engineering Tongji University, Shanghai, China, 200092

[email protected] Erik D.Goodman Departement of Electrical and Computer Engineering Michigan State University East Lansing, MI, USA, 48824

[email protected]

ABSTRACT The optimal solution of multi-objective control problem (MOCP) isn’t unique, so it is hard for traditional method to obtain these optimal solutions in one simulation process. Based on this background, Multi-Objective Compatible Control (MOCC) algorithm was presented by Lihong Xu in [2]. MOCC is a compromise method, which hunts for suboptimal and feasible region as the control aim rather than precise optimal point. The controller of MOCC is optimized by GA in its interval, namely its controller lacks concrete controller structure. Due to the controller without concrete structure, the system model must be accurate and without input disturbance; however, it is impractical in practice. Besides, the control problem is different from the optimization. Different user has different preference and users’ preference information plays a key role in control performance. In this paper, the feedback control law uLand users’ preference information are incorporated into MOCC algorithm. An improved MOCC (IMOCC) algorithm is presented. The simulation result demonstrates its superiority and advantage over the MOCC algorithm.

Association Based Immune Network for Multimodal Function Optimization Qingzheng Xu1,2 [email protected] Jing Si1 [email protected] Lei Wang1 [email protected] 1School

of Computer Science and Engineering Xi’an University of Technology Xi’an, China 2Xi’an Communication Institute Xi’an, China

ABSTRACT For the problem of serious resources waste, indeterminate direction of local search and degeneration in the original optaiNet, a novel association based immune network is proposed for multimodal function optimization. The hexabasic model mimics natural phenomenon in immune system such as clonal selection, affinity maturation, immune network, immune memory and immune association. The antibody population scale is semi-fixed reducing the time and space required to execute it. The information of the antibody population and the memory cells population is effective utilized to point out the direction of local search, to regulate the ratio between local search and global search, and to enhance the affinity of new antibodies. The elitist selection mechanism is adopted to ensure the convergence and stability of our algorithm respectively. The experiments on 10 benchmark functions show that when compared with opt-aiNet method, the new algorithm is capable of improving the search performance significantly in global convergence, convergence speed, computational cost, search ability, solution quality and algorithm stability.

A Genetic Algorithm-based Feature Selection Method for Human Identification based on Ground Reaction Force Su Xu 1. The Key laboratory of Biomimetic Sensing and Advanced Robot Technology, Institute of Intelligence Machines, Chinese Academy of Science, Hefei, Anhui, 230031 2. Dept. of Automation University of Science and Technology of China, Hefei,

Anhui, 230027 +86-0551-3620494

[email protected] Xu Zhou The Key laboratory of Biomimetic Sensing and Advanced Robot Technology, Institute of Intelligence Machines, Chinese Academy of Science, Hefei, Anhui, 230031

[email protected] Yi-ning Sun The Key laboratory of Biomimetic Sensing and Advanced Robot Technology, Institute of Intelligence Machines, Chinese Academy of Science, Hefei, Anhui, 230031

[email protected] ABSTRACT Biometrics-based identification is a promising technology. Ground reaction force (GRF), with its characteristics of non-invasion, easily measurement and low environment-affection, shows a potential in this field. Feature selection is an important step in biometrics-based identification. In this paper, a genetic algorithm-based feature selection method was discussed. The proposed algorithm has the advantage of finding small subsets of features that perform well in identification. Two contrast experiments were conducted to show the effectiveness of the algorithm, which shows that with GA, higher identification accuracy and smaller feature size were reached.、

A Hybrid Particle Swarm Optimization Approach with Prior Crossover Differential Evolution Wei Xu East China Univ. of Sci. & Tech. 130 Meilong Road Xuhui District, Shanghai, China 86-21-64252576

[email protected] Xingsheng Gu* East China Univ. of Sci. & Tech. 130 Meilong Road Xuhui District, Shanghai, China 86-21-64252576

[email protected] ABSTRACT Particle swarm optimization (PSO) is population-based heuristic searching algorithm. PSO has excellent ability of global optimization. However, there are some shortcomings of prematurity, low convergence accuracy and speed, similarly to other evolutionary algorithms (EA). To improve its performance, a hybrid particle swarm optimization is proposed in the paper. Firstly, the average position and velocity of particles are incorporated into basic PSO for concerning with the effect of the evolution of the whole swarm. Then a differential evolution (DE) computation, which introduces an extra population for prior

crossover, is hybridized with the improved PSO to form a novel optimization algorithm, PSOPDE. The role of prior crossover is to appropriately diversify the population and increase the probability of reaching better solutions. DE component takes into account the stochastic differential variation, and enhances the exploitation in the neighborhoods of current solutions. PSOPDE is implemented on five typical benchmark functions, and compared with six other algorithms. The results indicate that PSOPDE behaves better, and greatly improve the searching efficiency and quality.

Parameter Estimation for Asymptotic Regression Model by Particle Swarm Optimization Xing Xu State Key Lab. of Software Engineering, Wuhan University Wuhan, China

[email protected] Yuanxiang Li State Key Lab. of Software Engineering, Wuhan University Wuhan, China

[email protected] Yu Wu State Key Lab. of Software Engineering, Wuhan University Wuhan, China

[email protected] Xin Du State Key Lab. of Software Engineering, Wuhan University Wuhan, China

[email protected] ABSTRACT Asymptotic regression model (ARM) has been widely used in the field of agriculture, biology and engineering, especially in agriculture. Parameter estimation for ARM is a significant, challenging and difficult issue. The modern heuristic algorithm has been proved to be a highly effective and successful technique in parameter estimation of nonlinear models. As a novel evolutionary computation paradigm based on social behavior of bird flocking or fish schooling, particle swarm optimization (PSO) has shown outstanding performance in many real-world applications, for it is conceptually simple and practically easy to be implemented. In the present work, parameters of ARM are estimated on the basis of PSO for the first time. Firstly, PSO is compared with evolutionary algorithm (EA) on seven groups of actual data; PSO, while using less number of function evaluations, can find a parameter set as well as EA. Secondly, we estimate one-dimensional, two-dimensional and threedimensional parameter by fixing two, one and zero of all parameters of ARM, respectively. Finally, how sampling range and data with Gaussian noise influence on the performance of PSO is considered. Experimental results show that PSO is a stable, reliable and effective method in parameter

estimation for ARM and it’s robust to noise.

Enhancing Automated Red Teaming with Evolvable Simulation YongLiang Xu School of Computer Engineering, Nanyang Technological University Nanyang Avenue Singapore 639798

[email protected] Malcolm Yoke Hean Low School of Computer Engineering, Nanyang Technological University Nanyang Avenue Singapore 639798

[email protected] Chwee Seng Choo DSO National Laboratories 20 Science Park Drive Singapore 118230

[email protected] ABSTRACT Automated Red Teaming (ART), an automated process for Manual Red Teaming, is a technique frequently utilised by the Military Operational Analysis (OA) community to uncover vulnerabilities in operational tactics. Currently, individual ART studies are limited to the parameter tuning of a simulation model with a fixed structure. The effects in the evolutions of structural features of a simulation model have not been investigated in any of the studies. This paper investigates the benefits of Evolvable Simulation, which involves evolution of the structure of a simulation model. The case study used for this purpose is a maritime based scenario which involves the defense of an anchorage. Simulation results obtained through Evolvable Simulation revealed that the quality of the solutions found given an appropriate amount of evaluations will improve when the simulation model is evolved. Additionally, experimental results also showed that it is likely to have negligible improvement in solutions for models with smaller search space when the amount of evaluations is more than required. The insights obtained in this work shows that evolvable simulation is an effective methodology which allows decision makers to enhance their understanding on military operational tactics.

Research on Job Shop Scheduling under Uncertainty Xu Zhenhao East China Univ. of Sci. & Tech. 130 Meilong Road Xuhui District, Shanghai, China 86-21-64252576

[email protected] Gu Xingsheng East China Univ. of Sci. & Tech. 130 Meilong Road Xuhui District, Shanghai, China 86-21-64253463

[email protected] Gu Jinwei East China Univ. of Sci. & Tech. 130 Meilong Road Xuhui District, Shanghai, China 86-21-64252576

[email protected] Jiao Bin Shanghai Dianji Univ. 690 Jiangchuan Road Minhang District, Shanghai, China 86-21-54758615

[email protected]

ABSTRACT In many real world applications, the processing time of products in Job Shop scheduling problems is not a fixed value, and may vary dynamically with the situation. In this study, the scheduling mathematical model of Job Shop problems with uncertain processing time has been established based on fuzzy programming theory. The uncertain processing time can be described by the triangular fuzzy numbers, and the Maximum Membership Functions of Mean Value method is applied to convert the fuzzy scheduling model to the general optimization model. Furthermore, a fuzzy immune scheduling algorithm combined with the feature of the Immune Algorithm is proposed, which can prevents the possibility of stagnation in the iteration process and achieves fast convergence for global optimization. The effectiveness and efficiency of the fuzzy scheduling model and the proposed algorithm are demonstrated by simulation results.

PSO Algorithm for a Scheduling Parallel Unit Batch Process with Batching Ping Yan The Key Laboratory of Integrated Automation of Process Industry, Northeastern University Shenyang 110004, PR China +86-13609877928

[email protected] Lixin Tang Liaoning Key Laboratory of Manufacturing System and Logistics, The Logistics Institute, Northeastern University Shenyang 110004, PR China +86-24-83680169

[email protected] ABSTRACT In this paper, a parallel unit batch process scheduling problem (PBPSP) integrating batching decision is investigated. The batch scheduling problem is to convert the demands for products into sets of batches and schedule these batches on the units such that makespan is minimized. We propose a Particle Swarm Optimization (PSO) algorithm to solve this problem where a novel particle solution representation is designed for representing a batching scheme for PBPSP and a scale-based repair procedure is introduced to make particles feasible. In addition, the proposed PSO is combined with a relatively current evolutionary algorithm known as Differential Evolution (DE) for enhance the performance of PSO. A mixed integer linear programming (MILP) formulation is also given and used to calculate a lower bound for comparison with the PSO solutions. Computational results indicated the validity and effectiveness of the proposed PSO.

Design and Analysis of Switching Full-order Current Observer and Separation Principle for T-S Fuzzy System

ShiYu Yan ∗

State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology, Tsinghua University Beijing, 100084, People’s Republic of China

[email protected] ZengQi Sun State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology, Tsinghua University Beijing, 100084, People’s Republic of China National Laboratory of Space Intelligent Control Beijing, 100080, People’s Republic of China

[email protected] ABSTRACT As the important issues in fuzzy control system, some studies on fuzzy observer and separation principle for total fuzzy system have been done up to now. However, these existing results are far from enough. In order to supplement such theoretical study, this paper gives the design and analysis of switching fuzzy full-order current observer and proves that corresponding separation principle does hold. At last, a numerical simulation and comparison with smooth fuzzy fullorder prediction observer is given to assess switching fuzzy full-order current observer and the truth of the separation principle.

A Real-time Schedule Method for Aircraft Landing Scheduling Problem Based on Cellular Automaton Shenpeng Yu1, Xianbin Cao1, Maobin Hu2, Wenbo Du1, Jun Zhang3 1 Department of Computer Science and Technology, University of Science and Technology of China, Hefei, 230026, P.R.China, Anhui Province Key Laboratory of Software in Computing and Communication, Hefei, 230026, P.R.China 86-551-3601545 [email protected], [email protected], [email protected] 2 School of Engineering Science, University of Science and Technology of China Hefei 230026 P.R.China 86-551-3600127 [email protected] 3 School of Electronic and Information Engineering, Beihang University, Beijing, 100083, P.R.China [email protected]

ABSTRACT The Aircraft Landing Scheduling (ALS) problem is a typical hard multi-constraint optimization problem. In real applications, it is not most important to find the best solution but to provide a feasible landing schedule in an acceptable time. We propose a novel approach which can effectively solve the ALS while satisfying the real-time need. It consists of two steps: (i) Use CA to simulate the landing process in the terminal airspace and to find a considerably good landing sequence; (ii) a simple Genetic Algorithm associated with a Relaxation Operator is used to obtain a better result based on the CA result. Experiments have shown that our method is much faster and suitable for real-time ALS problem compared with traditional optimization methods. For all the 13 data sets, the proposed approach can find satisfactory solutions in less than 2 seconds.

A Parallel Evolutionary Algorithm for Optimal Pulse-Width

Modulation Technique in Power Systems RongXiang Yuan School of Electrical Engineering Wuhan University Wuhan 430072, China

[email protected] Xiufen Zou* School of Mathematics & Statistics Wuhan University Wuhan 430072, China

[email protected] Chunlin Xu School of Mathematics & Statistics Wuhan University Wuhan 430072, China

[email protected] ABSTRACT Pulse-Width Modulation (PWM) based on the elimination of loworder harmonics needs to deal with a class of systems of nonlinear equations whose right-hand terms are changed with time, moreover, these systems of nonlinear equations often have multiple solutions which are difficult to handle with conventional techniques. In this paper, an effective asynchronous parallel evolutionary algorithm is proposed to solve these systems of nonlinear equations so that we can obtain the switching angles for eliminating the low-order harmonics, accordingly, pulse patterns are optimized. In the paper, we give the detailed description of parallel algorithm, and the numerical results show that we can obtain the switching angles for a set of amplitudes and phase angles of the low-order harmonics.

Mathematical Model and Hybrid Particle Swarm Optimization for Flexible Job-Shop Scheduling Problem Zeng Ling-li, Zou Feng-xing, Xu Xiao-hong College of Mechatronics and Automation, National University of Defense Technology 410073 Changsha, P.R.China 86 731 4573370

[email protected] ABSTRACT In this paper, A hybrid integer programming model is proposed for flexible job-shop scheduling problem(FJSP). Using crossover operator and mutation operator, the hybrid particle swarm optimization(HPSO) algorithm with simple particle swarm optimization(SPSO) algorithm and genetic algorithm(GA) is employed to solve this problem. Compared with SPSO algorithm, HPSO algorithm has a potential to reach a better optimum. The results of simulation indicate that, HPSO algorithm out performs SPSO algorithm on searching speed for global optimum and avoiding prematurity.

A Novel Sexual Adaptive Genetic Algorithm Based on Two-step Evolutionary Scenario of Baldwin Effect and Analysis of Global Convergence Mingming Zhang, Shuguang Zhao, Xu Wang College of Information Science and Technology Donghua University

Shanghai 201620, China

[email protected] ABSTRACT This work presents a novel sexual adaptive genetic algorithm (NSAGA) based on two-step evolutionary scenario of Baldwin effect to overcome the shortcomings of traditional genetic algorithms, such as premature convergence, stochastic roaming, and poor capabilities in local exploring. NSAGA simulates sexual reproduction in nature and utilizes an effective gender determination method to divide the evolutionary population into two different gender subgroups. Based on the competition, cooperation, and innate differences between two gender subgroups, NSAGA adaptively adjusts the sexual genetic operators. To guide the individuals’ evolution, NSAGA adopts a two-step evolutionary scenario: NSAGA guides individuals in niche to forward or reverse evolutionary learning inspired by the acquired reinforcement learning theory based on Baldwin effect, and enables the transmission of fitness information between parents and offspring to supervise the offspring’s evolution. Then, the global convergence analysis of NSAGA is presented in detail. It is theoretically proved that NSAGA can converge to the global optimum and the epsilon-optimal solution with probability one. Moreover, numerical simulations are conducted for a set of benchmark test functions, and the performance of NSAGA is compared with that of some evolutionary algorithms published recently. Experiments results show that the proposed algorithm is effective and advantageous.

An Immune Evolutionary Algorithm Based Pose Estimation Method for Parallel Manipulator Shu-Ping Zhang College of Information Sciences and Technology Donghua University Shanghai ,China 201620 [email protected]

Yong-Sheng Ding College of Information Sciences and Technology; Engineering Research Center of Digitized Textile & Fashion Technology, Ministry of Education Donghua University Shanghai ,China 201620 [email protected]

Kuang-Rong Hao College of Information Sciences and Technology Donghua University Shanghai ,China 201620 [email protected]

ABSTRACT Based on immune systems, a new immune evolutionary algorithm (IEA) is presented to develop a pose estimation method for a parallel manipulator in the paper. Four vertices of a parallelogram device on a parallel manipulator’s end-effector are used as the

object model. And the problem of pose identification is transformed to obtain the optimal depth estimations of the object model. In IEA, depth estimations of the object model are taken as an antigen. Then the optimal solutions are searched by clone selection and variation operator. In theory, this method enriches the pose estimation methods from four points correspondences. In addition, it provides guidance for practical applications of a parallel manipulator. Experiments results demonstrate that our algorithm works speedily and robustly.

An Immune Co-Evolutionary Algorithm Based Approach for Optimization Control of Gas Turbine Xiang-feng Zhang College of Electric Shanghai Dianji University Shanghai 200240, P.R. China 86-21-64300980-3071

[email protected] Jun Liu College of Electric Shanghai Dianji University Shanghai 200240, P.R. China 86-21-64300980-3159

[email protected] Yong-sheng Ding Information Sciences and Technology, Donghua University Shanghai 201620, P. R. China 86-21-67792329

[email protected] ABSTRACT Gas turbine is a complex non-linearity system and operates in variable conditions. Traditional control methods are usually adopted in the control loop of gas turbine. The methods may cause control error with the theoretically correct value. In this paper, an immune co-evolutionary algorithm (ICEA) is proposed inspired by immune mechanisms and co-evolutionary computation. And the control of gas turbine is optimized with the ICEA. The procedures of the ICEA mainly include clonal selection and proliferation, fitness evaluation, hyper-mutation, co-evolution and antibody population update. The fitness function is defined referencing to the control model of gas turbine considering some constraints, such as the compressor surge edge constraints and the highest initial gas temperature. Two cases are simulated using the ICEA when the system is accelerated to the partial load and the maximum load, respectively. The simulations show that the ICEA can optimize the quantity of oil to make the gas turbine system reach the terminal status within the shortest time. And the consumed time for the latter is longer than that for the former. The results demonstrate that the ICEA has good feasibility and practicability for the optimization control of gas turbine.

A Hybrid Optimization Algorithm for the Job-shop Scheduling Problem

Qiang Zhou Department of Computer Science and Technology Chuzhou University, Chuzhou, China, 239012 Tel: +86-0550-3047526

[email protected] Xunxue Cui New Star Research Institute of Applied Technology Hefei, China, 230031 Tel: +86-0551-5769700

[email protected] Zhengshan Wang Department of Computer Science and Technology Chuzhou University, Chuzhou, China, 239012 Tel: +86-0550-3510481

[email protected] Bin Yang Department of Computer Science and Technology Chuzhou University, Chuzhou, China, 239012 Tel: +86-0550-3510481

[email protected]

ABSTRACT The job-shop scheduling problem is a NP-hard combinational optimization and one of the best-known machine scheduling problems. Genetic algorithm is an effective search algorithm to solve this problem; however the quality of the best solution obtained by the algorithm has to improve due to its limitation. The paper proposes a novel hybrid optimization algorithm for the job-shop scheduling problem, which applies chaos theory on the basis of combining genetic programming and genetic algorithm. It improves the quality of the initial population by using chaos optimization method; it maintains the population diversity by chaotic disturbance and anti-equilibration in crossover of genetic programming. Three traversals are adopted to reduce the chance of reaching local optimal solution. Moreover, a scheme of changing weight is proposed during the process of evolution to increase the global exploration capability. The experimental results show that the effectiveness and good quality of the hybrid algorithm is obvious from some benchmarks.

A Study of Parallel Evolution Strategy – Pattern Search on a GPU Computing Platform Weihang Zhu Department of Industrial Engineering Lamar University P.O.Box 10032 Beaumont, Texas 77710, USA 1-409-880-8876

[email protected] ABSTRACT This paper presents a massively parallel Evolution Strategy – Pattern Search Optimization (ES-PS) algorithm with graphics hardware acceleration on bound constrained nonlinear continuous optimization functions. The algorithm is specifically designed for a graphic processing unit (GPU) hardware platform featuring ‘Single Instruction – Multiple Thread’ (SIMT). GPU computing is an emerging desktop parallel computing platform. The hybrid ES-PS optimization method is implemented in the GPU environment and compared to a similar implementation on CPU hardware. Computational results indicate that GPU-accelerated SIMT-ES-PS method is orders of magnitude faster than the

corresponding CPU implementation. The main contribution of this paper is the parallelization analysis and performance analysis of the hybrid ES-PS with GPU acceleration.

A Proposed Modularized DNA Computer, Based on Biochips Ying Zhu1 Donghua University 2999 Renmin R. (N.), Shanghai, China [email protected]

Yongsheng Ding Donghua University 2999 Renmin R. (N.), Shanghai, China [email protected]

Wanggen Li Donghua University 2999 Renmin R. (N.), Shanghai, China [email protected]

Gregory Kemp CSIRO Livestock Industry 306 Carmody Road, St Lucia, QLD, Australia [email protected]

ABSTRACT There are limits to miniaturization with current computer technologies. Information-processing capabilities of organic molecules such as DNA can be used in computers to replace digital switching modality. However, without the emergence of microfluidic devices, all operations in vitro would be user regulated. A more advanced model is where robotic and electronic regulation is combined with DNA computing allowing the majority of the operations within the test environment to be carried out automatically. Microfluidics offers the promise of a “lab on a chip” system. This can control pico liter scale volumes, with integrated support for operations such as mixing, storage, PCR, heating/cooling, cell lysis, electrophoresis, and others [1], [2]], [3]. Thus has emerged a vision for creating a hybrid DNA computer: that can use microfluidics for the control paths and biological primitives for computation (the Arithmetic Logical Units). This paper presents a proposed modularized DNA biochip computer that works in accordance with Von Neumann’s principles [4]. The biochips are divided into several modules, which have different functions. Thus, biochemical operations can be regulated in a step wise fusion. We then describe each module within the biochip and simulate how the classic Hamiltonian Path Problem would be solved in the proposed DNA computer.

Study of Cache Placement for Time-shifted TV Cluster Using Genetic Algorithm Juchao Zhuo Dept. of Automation University of Science and Technology of China, Hefei, Anhui, 230027 +86-0551-3620494

[email protected] Jun Li Dept. of Automation University of Science and Technology of China, Hefei, Anhui, 230027 +86-0551-3602459

[email protected] Gang Wu Dept. of Automation University of Science and Technology of China, Hefei, Anhui, 230027 +86-0551-3601053

[email protected] ABSTRACT The designing of a streaming media system, especially Timeshifted TV cluster faces an optimization cache problem of deciding how to cache channels to multiple servers so that the blocking probability is minimized subject to memory capacity constraints. In this paper, we investigate the crucial problem by evaluating the blocking performance for a feasible assignment. A popularity-based random placement (PRP) scheme together with the genetic algorithm (GA) is developed to find an optimal or approximate optimal solution of the problem. The experiment results reveal that our proposed algorithm is efficient on improving the performance of Time-shifted TV cluster in terms of minimizing blocking probability.

About the Dynamics of Essential Genetic Information: An Empirical Analysis for Selected GA-Variants Michael Affenzeller Department of Software Engineering Upper Austria University of Applied Sciences Softwarepark 11 4232 Hagenberg, Austria

[email protected] Andreas Beham Josef Ressel Centre for Heuristic Optimization Upper Austria University of Applied Sciences Softwarepark 11 4232 Hagenberg, Austria

[email protected] Stefan Wagner Department of Software Engineering Upper Austria University of Applied Sciences Softwarepark 11 4232 Hagenberg, Austria

[email protected] Stephan M. Winkler Department of Medical and Bioinformatics

Upper Austria University of Applied Sciences Softwarepark 11 4232 Hagenberg, Austria

[email protected] ABSTRACT This paper exemplarily points out how essential genetic information evolves during the runs of selected GA-variants. The algorithmic enhancements to a standard genetic algorithm certify the survival of essential genetic information by supporting the survival of relevant alleles rather than the survival of above average chromosomes. This is achieved by de¯ning the survival probability of a new child chromosome depending on the child's ¯tness in comparison to the ¯tness values of its own parents. The main aim of this paper is to explain important properties of the discussed algorithm variants in a rather intuitive way. Aspects for meaningful and practically more relevant generalizations as well as more sophisticated experimental analyses are indicated.

Analysis of Collision Probability in Vehicular Ad Hoc Networks Jianwei An ajw626 @ 126.com Xun Guo guoxun19 @ gmail.com Yang Yang yyang @ ustb.edu.cn Department of Communication Engineering, University of Science and Technology Beijing, Beijing, China

ABSTRACT Vehicular Ad Hoc Network (VANET) is a new type of ad hoc network with the characteristics of highly dynamic topology, variable vehicle velocity and density. In the performance evaluation of VANETs, traditional collision probability model is not suitable because it poorly reflects these characteristics. However, collision probability is a vital ingredient for any performance evaluation in IEEE 802.11 system. In order to get more accurate results of VANETs’ performance evaluation, a new model to estimate the collision probability in VANETs is proposed in this paper, which integrated the traditional model with the characteristics of VANETs. The model shows that the collision probability in VANETs is no longer a constant value as in traditional model, but a function of the factors reflecting the characteristics of VANETs. It increases along with the increasing of vehicle velocity or vehicle density. Simulation results using Network Simulator 2 (ns-2) show the validity and accuracy of the proposed model.

Pursuit Evasion Differential Game with Superior Evaders Ze-su Cai State Key Laboratory Robotics and System, Harbin Institute of Technology, Harbin 150001, China

[email protected] Li-ning Sun

State Key Laboratory Robotics and System, Harbin Institute of Technology Harbin 150001, China

[email protected] Hai-bo Gao School of Mechatronics Engeneering, Harbin Institute of Technology, Harbin 150001, China

ABSTRACT In this paper, we consider a novel Hierarchical decomposition approach for multi-player pursuit evasion game (MPPEG) where some evaders’ capability are higher than those of all pursuers. Differently from standards MPPEGs where the environment and the location of evaders is unknown and a probabilistic map is built based on the pursuer onboard sensor. In this paper, we study the number of pursuers which necessitates for the capture condition and the time of all evaders have been captured. A novel Cooperative in the coalition formation is used for pursuer in their pursuit strategies deriving to 1) Avoid collision among objects, 2) Reduce the distance between each pursuer and the evader over the evolution of game; 3) Keep the pursuers’ formation around the evader invariant during the pursuit process and enclose the superior evader. The validity of our method is illustrated by two simulation examples.

Optimal Feature Selection Algorithm Based on Quantum-Inspired Clone Genetic Strategy in Text Categorization Hao Chen School of Information Science and Engineering Central South University Hunan, China +86 731 8617575

[email protected] Beiji Zou School of Information Science and Engineering Central South University Hunan, China +86 731 8877701

[email protected] ABSTRACT Information overload is a serious issue in the modern society. As a powerful method to help people out of being “lost” in too much useless information, automatic text categorization is getting more and more important. Feature selection is the most important step in text categorization. To improve the performance of text categorization, we present a new text categorization method called quantum-inspired clone genetic algorithm (QCGA). The experimental results show that the QCGA algorithm is superior to other common methods.

Evolutionary Multi-objective Optimization Algorithm Based on Global Crowding Diversity Maintenance Strategy Qiong Chen

School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China

[email protected] Shengwu Xiong ¤

School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China

[email protected] Hongbing Liu School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China

[email protected] ABSTRACT This paper presents an improved multi-objective evolutionary algorithm based on global crowding diversity maintenance strategy and diversity initialization population strategy. In selection process, the global crowding strategy is applied to be a part of crowding operator which is used to select survival individuals. In the initialization process, one kind of diversity initialization population strategy is used to guarantee that the population can be widely spread at the beginning of evolutionary process. Numerical experiment results show that the proposed scheme improves diversity maintenance in evolutionary process. The results also demonstrate that the proposed algorithms can speed up the convergence and guide the solutions to be widely spread on the true Pareto optimal front.

Analysis of Micro-Behavior and Bounded Rationality in Double Auction Markets Using Co-evolutionary GP Shu-Heng Chen National Chengchi University Taipei, Taiwan

[email protected] Ren-Jie Zeng National Chengchi University Taipei, Taiwan

[email protected] Tina Yu Memorial University of Newfoundland, Canada

[email protected] ABSTRACT We investigate the dynamics of trader behaviors using a co-evolutionary genetic programming system to simulate a double-auction market. The objective of this study is twofold. First, we seek to evaluate how, if any, the di_erence in trader rationality/intelligence inuences trading behavior. Second, besides rationality, we also analyze how, if any, the co-evolution between two learnable traders impacts their

trading behaviors. We have found that traders with di_erent degrees of rationality may exhibit di_erent behavior depending on the type of market they are in. When the market has a pro_t zone to explore, the more intelligent trader demonstrate more intelligent behaviors. Also, when the market has two learnable buyers, their co-evolution produced more pro_table transactions than when there was only one learnable buyer in the market. We have analyzed the learnable traders' strategies and found their behavior are very similar to humans in decision making. We will conduct human subject experiments to validate these results in the near future. Categories and Subject Descriptors: H.4 [Information Systems Applications]: Miscellaneous General Terms: Economics, Experimentation, Algorithm. Keywords: Bounded rationality, co-evolution, double-auction.

Bumblebees: A Multiagent Combinatorial Optimization Algorithm Inspired by Social Insect Behaviour Francesc Comellas Universitat Politècnica de Catalunya Dep. Matemàtica Aplicada IV - EPSC Avda. Canal Olimpic 15 Castelldefels, Catalonia, Spain

[email protected] Jesús Martínez-Navarro Universitat Politècnica de Catalunya Dep. Matemàtica Aplicada IV - EPSC Avda. Canal Olimpic s/n Castelldefels, Catalonia, Spain

ABSTRACT This paper introduces a multiagent optimization algorithm inspired by the collective behavior of social insects. In this method, each agent encodes a possible solution of the problem to solve, and evolves in a way similar to real life insects. We test the algorithm on a classical difficult problem, the kcoloring of a graph, and we compare its performance in relation to a standard genetic algorithm and another multiagent system. The results show that this algorithm is faster and outperforms the other methods for a range of random graphs with different orders and densities. Moreover, the method is easy to adapt to solve different NP-complete problems. Categories and Subject Descriptors: I.2.11 [Distributed Artificial Intelligence]: Multiagent systems, G.1.6 [Optimization]: Miscellaneous, G.2.2 [Graph Theory]: Graph algorithms General Terms: Algorithms, Experimentation. Keywords: Multiagent System, Combinatorial optimization, Graph coloring, Adaptative complex systems.

Research on an Orthogonal and Model Based Multi-objective Genetic Algorithm Guangming Dai1 Yanzhi Li 1, 2 Wei Zheng1 1 School of Computer, China University of Geosciences, Wuhan City, Hubei Province, China 2 Corresponding author [email protected] [email protected] [email protected]

ABSTRACT Against low efficiency of traditional multi-objective evolutionary

algorithms and poor utilization of Pareto-optimal solutions distribution regularity etc, in this paper, a new approach OMEA is proposed. It uses that distribution regularity to obtain good solutions, we also apply the orthogonal design to initialize population. Compared with SPEA2, NSGA-II and PAES, Pareto solutions by OMEA are closer to Pareto-optimal Front. The result of experiments shows a group of Pareto solutions with better convergence and diversity can be achieved, which gives strong supports to actual applications.

Solving the Packing Problem of Rectangles with Improved Genetic Algorithm Based on Statistical Analysis Ding Genhong Hohai University Nanjing, 210098 People’s Republic of China 86-25-83786626

[email protected] Li Dan Hohai University Nanjing, 210098 People’s Republic of China 86-13913801376

[email protected] Chen Leng Hohai University Nanjing, 210098 People’s Republic of China 86-15950524927

[email protected] ABSTRACT The genetic algorithm and the surplus rectangle algorithm are used for solving the orthogonal packing problem of rectangles. Based on statistical analysis of rectangular packing problem, a comparable standard for judgment of a solution has been proposed, which is adopted in classification of the parent population. A surplus rectangle algorithm is introduced to decode the permutation of rectangles to the corresponding packing pattern uniquely. For different constructions, corresponding genetic operations have been designed. And then an improved genetic algorithm has been constructed. Several rectangles packing problems have been solved by using this improved algorithm and the optimum packing results have been achieved. This shows that the improved genetic algorithm is efficacious.

Convergence Analysis of Gene Expression Programming Based on Maintaining Elitist Xin Du State-key Lab of Software Engineering, Wuhan University Wuhan, China Department of Information and Engineering, Shijiazhuang University of Economics Shijiazhuang, China

[email protected] Lixin Ding State-key Lab of Software Engineering, Wuhan University Wuhan, China

[email protected] Chenwang Xie State-key Lab of Software Engineering, Wuhan University Wuhan, China

[email protected] Xing Xu State Key Lab. of Software Engineering, Wuhan University Wuhan, China

[email protected] Shenwen Wang Shijiazhuang University of Economics Shijiazhuang, China

[email protected] Li Chen State-key Lab of Software Engineering, Wuhan University Wuhan, China

[email protected] ABSTRACT This paper analyzes the convergence of Gene Expression Programming based on maintaining elitist(ME-GEP).It is proved that ME-GEP algorithm will converge to the global optimal solution. The convergence speed of ME-GEP algorithm is estimated by the properties of transition matrices. The result hinges on four factors: population size, minimal transposition, mutation and selection probabilities. A category with the (minimum) three required fields

An Improved Quantum Genetic Algorithm for Stochastic Job Shop Problem Jinwei Gu East China Univ. of Sci. & Tech. 130 Meilong Road Xuhui District, Shanghai, China 86-21-64252576 [email protected]

Cuiwen Cao Bin Jiao Shanghai Dianji Univ. 690 Jiangchuan Road Minhang District, Shanghai, China 86-21-54758615 [email protected]

East China Univ. of Sci. & Tech. 130 Meilong Road Xuhui District, Shanghai, China 86-21-64252576 [email protected]

Xingsheng Gu* East China Univ. of Sci. & Tech. 130 Meilong Road Xuhui District, Shanghai, China 86-21-64252576 [email protected]

ABSTRACT This paper considers the stochastic job shop scheduling problem with the objective of minimizing the expected value of makespan and the processing times of jobs being subject to independent normal distributions. In order to solve this problem, we devise an Improved Quantum Genetic Algorithm (IQGA) and develop a stochastic expected value model. Different from traditional genetic algorithms, IQGA employs the idea of quantum theory, devises a converting mechanism of quantum representation aiming at job shop code, and proposes a new rotation angle table as the update mechanism of populatio. In addition, three crossover operators and three mutation operators are compared in order to obtain the best combination to improve algorithm performance. Compared with standard Genetic Algorithm (GA), experimental results achieved by IQGA demonstrate its feasibility and effectiveness while dealing with the stochastic job shop problem.

Descriptive Statistics of Non-Uniform Interval Symbolic Data Guo Jun-peng, School of Management, Tianjin University, P.R.China, 300072 86-13602053107

[email protected] Li Wen-hua, School of Management, Tianjin University, P.R.China, 300072 86-15900384566

[email protected] Gao Feng School of Management, Tianjin University, P.R.China, 300072 86-13752355795

[email protected] ABSTRACT As a new kind of data mining method, symbolic data analysis (SDA) can not only decrease the computational complexity of huge data, but also master the property of the sample integrally by data package technology. Interval number is one of the most important types of symbolic data. Previous studies assumed each individual to be uniformly distributed within the interval, but the fact is not so. Non-uniform interval symbolic data is defined in

this paper, and the study is concentrated on their descriptive univariate statistics and bivariate statistics. On the basis of the study on empirical distribution function for non-uniform interval symbolic data, the calculation formula of mean and variance of non-uniform interval variables is achieved. Furthermore, covariance and correlation coefficient between two non-uniform interval variables are solved based on their empirical joint distribution function. Finally an example is given.

The Optimum Method on Injection Molding Condition Based on RBF Network and Ant Colony Algorithm Fengli Huang* School of Mechanical and Electrical Engineering, Jiaxing University

WenChang road 355, Jiaxing Zhejiang, China +86-573-83643093, 314001

[email protected] Jinmei Gu School of Mechanical and Electrical Engineering, Jiaxing University

WenChang road 355, Jiaxing Zhejiang, China +86-573-83643093, 314001

[email protected] Jinhong Xu School of Mechanical and Electrical Engineering, Jiaxing University

WenChang road 355, Jiaxing Zhejiang, China +86-573-83643602, 314001

[email protected] ABSTRACT Aiming at the two principal; quality factors (warpage quantity and shrinkage rate) in injection molding process, the optimum method on injection molding condition based on RBF network and ant colony algorithm is provided. The definition and calculation

method of excellent degree are given first and then the optimum method of the approximate model based on radial basis neural network is given. In the case study of plastic injection of fruit plate, the range of molding condition and the design method of design variables based on excellent degree are given, then the approximate model is gotten by Hyper-Latin square experiment and RBF network, the optimum result is gotten by improved ant colony algorithm of continuous field. It shows that the optimum result of plastic injection parameters based on radial basis neural network response surface and ant colony algorithm is reliable, and has good practical meaning.

A Genetic Algorithm for Solving Fourth-Party Logistics Routing Optimizing Problem with Fuzzy Duration Time Min Huang1,2 , Yan Cui1,2 , Xingwei Wang 2 , Hongyu Dong1,2 1. Key Laboratory of Integrated Automation of Process Industry (Northeastern University), Ministry of Education 2. College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, China, 110004

[email protected] ABSTRACT From the beginning of the 21st century, Fourth Party Logistics (4PL) has been attracting more and more attention in many fields. In this paper, a 4PL routing problem with fuzzy duration time is presented ， and the fuzzy numbers is used to denote the uncertainty of the duration time. After a simple description of 4PL, a fuzzy programming for it is built and a crisp equivalent is derived by expected value. Then genetic algorithm is designed to solved the problem. Finally, an extensive computational analysis is presented and the numerical results show that which route should be selected in order to get minimum cost in the due date.

Research on Flight Test Calibration Strategy Based on Data Fusion Hongwei Jiang

Chinese Flight Test Establishment. Xi’an,Shanxi,710089, China Tel:8613720583834

[email protected] Zhaohui Yuan Northwestern Polytechnical University Xi’an,Shanxi,710072,China Tel:8613002993198

[email protected] Yajuan Zhao Chinese Flight Test Establishment Xi’an,Shanxi,710089, China Tel:8613572257203

[email protected] ABSTRACT Research on environment conditions and other non-target parameters influence on the accurate and reliability of the micro -difference pressure test system and the influence of these non-target parameters are relationship each other. So this paper puts forward improving the integrated performance of the micro-difference pressure test system by using the data fusion technology and provide correct flight test data processing basis.

Restoration of Coverage Blind Spots in Wireless Sensor Networks Based on Ant Colony Algorithm Lizhong Jin School of Information Science & Engineering Northeastern University Shenyang 110004, China [email protected] m.cn

Jie Jia School of Information Science & Engineering Northeastern University Shenyang 110004, China

[email protected]

Guiran Chang Computing Center Northeastern University Shenyang 110004, China [email protected]

Xingwei Wang School of Information Science & Engineering Northeastern University Shenyang 110004, China [email protected]

ABSTRACT Coverage control is one of the key problems of research and application of wireless sensor networks. In this paper, the coverage control problem for hybrid wireless sensor network consisting of both static and mobile sensors is investigated. A dynamic repair mechanism based on the ant colony algorithm is proposed for coverage blind spots found in the lifetime of wireless sensor networks. Simulation results show that the proposed method not only can make the nodes deployment more even, but also can improve the network quality of service, which verifies the effectiveness and feasibility of the restoration mechanism based on ant colony algorithm.

Restoration of Coverage Blind Spots in Wireless Sensor Networks Based on Ant Colony Algorithm Lizhong Jin School of Information Science & Engineering Northeastern University Shenyang 110004, China [email protected] m.cn

Jie Jia School of Information Science & Engineering Northeastern University Shenyang 110004, China

[email protected]

Guiran Chang Computing Center Northeastern University Shenyang 110004, China [email protected]

Xingwei Wang School of Information Science & Engineering Northeastern University Shenyang 110004, China [email protected]

ABSTRACT Coverage control is one of the key problems of research and application of wireless sensor networks. In this paper, the coverage control problem for hybrid wireless sensor network consisting of both static and mobile sensors is investigated. A dynamic repair mechanism based on the ant colony algorithm is proposed for coverage blind spots found in the lifetime of wireless sensor networks. Simulation results show that the proposed method not only can make the nodes deployment more even, but also can improve the network quality of service, which verifies the effectiveness and feasibility of the restoration mechanism based on ant colony algorithm.

Finding All Global Solutions of Several Variables and Multimodal Function Xunguang Ju1 Xuzhou Institute of Technology School of Information and Electrical Engineering 86-13776798295

[email protected] Rong Bao1 Xuzhou Institute of Technology School of Information and Electrical Engineering 86-13852099979 [email protected]

Xiaogen Shao1 Xuzhou Institute of Technology

School of Information and Electrical Engineering 86-13952296585

[email protected] Chengchun Han1 Xuzhou Institute of Technology School of Information and Electrical Engineering [email protected]

Liqing Xiao1 Xuzhou Institute of Technology School of Information and Electrical Engineering 86-13813282346

[email protected] Hongzhen Yu1 China University of Mining and Technology College of Information and Electrical Engineering

[email protected] ABSTRACT To improve Simple Genetic Algorithm convergence property s in the nonlinear and multimodal function of the optimization problem, constructing and applying the interval exclusion genetic algorithms (IEGA), the paper applied this hybrid algorithm to carrying on the global optimization problem of several variables and multimodal function in visual C++. The numerical experiment results showed that this algorithm is easy to be actualized, to have excellent performance. It is especially important for it ’ s speeding up upwards the convergence of 100% reliability and well solving the schema deception and premature convergence problem in Simple Genetic Algorithm.

Representation and Recombination over Nonsingular Binary Matrices Yong-Hyuk Kim Department of Computer Science & Engineering Kwangwoon University Wolge-dong, Nowon-gu, Seoul, 139-701, Korea

[email protected]

Yourim Yoon School of Computer Science & Engineering Seoul National University Sillim-dong, Gwanak-gu, Seoul, 151-744, Korea

[email protected] ABSTRACT In this paper, we study nonsingular binary matrix space, GLn(Z2). The space is important in that it is used for the change of basis in binary encoding, which is the representation typically used in genetic algorithms. We analyze the properties of GLn(Z2) and discuss possible representation and recombination operators when used in evolutionary algorithms. Not only typical approaches but also ones using elementary matrices of linear algebra are presented.

Symbolic Regression using Abstract Expression Grammars Michael F. Korns Freeman Investment Management 1 Plum Hollow Henderson, Nevada 89052 1 (702) 837 3498

[email protected] ABSTRACT Abstract Expression Grammars have the potential to integrate Genetic Algorithms, Genetic Programming, Swarm Intelligence, and Differential Evolution into a seamlessly unified array of tools for use in symbolic regression. The features of abstract expression grammars are explored, examples of implementations are provided, and the beneficial effects of abstract expression grammars are tested with several published nonlinear regression problems.

Synchronization Analysis and Control in Chaos System based on Complex Network Li Li Guilin University of Electronic Technology, Department

of Computer and Control Jinji Road No. 1, Guilin, Guangxi, China

[email protected] Feng Kong Guangxi University of Technology, Department of Electronic Information and Control Engineering Donghuan Road No. 268, Liuzhou, Guangxi, China

[email protected] ABSTRACT For a certain kind of complex network, Lorenz chaos system is used to describe the state equation of nodes in network. By constructing a Lyapunov function, it is proved that this network model can achieve synchronization under the adaptive control scheme. The control strategy is simple, effective and easy for the engineering design in the future. The simulation results show the effectiveness of control scheme.

Research on Multi-supplier Performance Measurement Based on Genetic Ant Colony Algorithm Li Xiaomei School of Management, Tianjin University, Tianjin, CHINA +86-13820771588

[email protected] Mao Zhaofang School of Management, Tianjin University, Tianjin, CHINA +86-13302035658

[email protected] Qi Ershi School of Management, Tianjin University, Tianjin, CHINA +86-22-27405100

[email protected] ABSTRACT With the growing maturation of the economical globalization and the fast progress of the IT industry, both the development of the global market and intellectual economy has overrun the national broad lines. However, the subsequent competition has also

becoming fiercer and fiercer. Many enterprises have made more closely joint development with their partners, and built up “supply chain” with their partners to further expand supply-and-demand network. In the whole chain even the whole network Suppliers are upstream and key organizations of this chain and the net, selection of suppliers is the key for whole chain, and it plays important role for efficient operation of whole chain. Although many specialists have done research on multi-supplier selection and performance measurement system, it is still one of the most difficult problems for most manufacturing, but many subjective and objective issues exist during actual operation of supplier selection. In this paper, the improved genetic ant colony algorithm is used for research about selection of multi-supplier based on various relevant literatures about selection of suppliers at home and abroad. Via analysis for simulated examples, it is proven that this method is effective and feasible, and provides referential model and algorithm for selection of various types in supply chain. Categories and Subject Descriptors: K.6.4 [System Management]: Using the systematic Method to sole the management problem. General Terms: Algorithms, Management, Measurement, Performance, Design, Experimentation, Standardization, Theory, Verification. Keywords: Performance Measurement, Genetic Ant Colony Algorithm

Quantum-Inspired Evolutionary Clustering Algorithm Based on Manifold Distance Yangyang Li [email protected]

Hongzhu Shi [email protected]

Maoguo Gong [email protected]

Ronghua Shang [email protected] Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University Xi’an, 710071, China

ABSTRACT Based on the concepts and principles of quantum computing, a quantum-inspired evolutionary algorithm for data clustering (QECA) is proposed in this paper. And a novel distance measurement index called manifold distance is introduced. These attribute data are the main source of clustering problem, due to its complex distribution, most clustering algorithms available are only suitable for these types of characteristic data. In this study, a new algorithm which can deal with these data with manifold distribution is more effective. The main motives of using QECA consist in searching for appropriate cluster center so that a similarity metric of clusters are optimized more quickly and effectively. The superiority of QECA over fuzzy c-means (FCM) algorithm and immune evolutionary clustering algorithm (IECA) is extensively demonstrated in our experiments

Image based Reconstruction using Hybrid Optimization of Simulated Annealing and Genetic Algorithm Cong Liu Shanghai University No.149 Yanchang Rd. Shanghai,20007,China +86-21-56334945

[email protected] Wangge Wan Shanghai University No.149 Yanchang Rd. Shanghai,200072,China +86-21-56334945

[email protected] Youyong Wu Shanghai University No.149 Yanchang Rd. Shanghai,200072,China +86-21-56334945

[email protected] ABSTRACT This work deals with the problem of estimating depth information of 3-D surface from a pair of images. The proposed method relies

on Second-order Priors on the smoothness of 3D surface which cause intractable (non-submodular) optimization problems; we solved it by using the strategy of Hybrid Optimization of Simulated Annealing and Genetic Algorithm. Experimental results demonstrate the Second-order priors are a better model of typical scenes than first-order priors and the performance of the hybrid algorithm outperforms SA and GA alone.

A Discrete Differential Evolution Algorithm for the Job Shop Scheduling Problem Fang Liu 1．School of Computer Science and Technology Xidian University Xi'an, China 2．Key Laboratory of Intelligent Perception & Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xi'an, China [email protected]

Yutao Qi 1．School of Computer Science and Technology Xidian University Xi'an, China 2．Key Laboratory of Intelligent Perception & Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xi'an, China

[email protected]

Zhuchang Xia 1．School of Computer Science and Technology Xidian University Xi'an, China 2．Key Laboratory of Intelligent Perception & Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xi'an, China [email protected]

Hongxia Hao 1．School of Computer Science and Technology Xidian University Xi'an, China 2．Key Laboratory of Intelligent Perception & Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xi'an, China [email protected]

ABSTRACT Differential Evolution (DE) Algorithm is a new evolutionary computation algorithm with rapid convergence rate. However, it does not perform well on dealing with job shop scheduling problems that have discrete decision variables. To remedy this, a Discrete Differential Evolution (DDE) Algorithm with special crossover and mutation operators is proposed to solve this problem. Under the skeleton of DE algorithm, The DDE algorithm inherits the advantage of rapid convergence rate. The experimental results on the well-known benchmark instances show the proposed algorithm is efficient in solving Job Shop Scheduling Problem.

Training Fuzzy Support Vector Machines by Using Boundary of Rough Set Hongbing Liu School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China

[email protected] Shengwu Xiong ¤

School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China

[email protected] Qiong Chen School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China

[email protected] ABSTRACT Support Vector Machines (SVMs) are statistical learning methods based on two-class problems and exist unclassi¯able regions when they are extended to multi-class problems. In order to reduce unclassi¯able regions, S. Abe and T. Inoue proposed the improved multi-class SVMs called Fuzzy Support Vector Machines (FSVMs) by which the unclassi¯able regions are reduced. In this paper, we train FSVMs by using the training data lying in the boundary of rough set. Firstly, the whole training set is divided into some equivalence classes by transforming all attribute values into discrete ones. Secondly, the lower approximation sets of the training data with the same categories are obtained by the formed equivalence classes. Thirdly, the boundary induced by the whole training set and the lower approximation sets is selected to form FSVMs. The experimental results on classic benchmark data sets show that the proposed learning machines can downsize the number of training data and achieve the higher predictions.

Stochastic Ranking Based Differential Evolution Algorithm for Constrained Optimization Problem Ruochen Liu [email protected]

Yong Li [email protected]

Wei Zhang [email protected]

Licheng Jiao [email protected] Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University Xi’an, 710071, China

ABSTRACT Based on differential evolution and stochastic ranking strategy, a new differential evolution algorithm for constrained optimization problem is proposed in this paper. The proposed algorithm reserves sub-optimal solutions in the process of population evolution, which effectively enhances the diversity of the population. The experiment results on 13 well-known benchmark problems show that the proposed algorithm is capable of improving the search performance significantly in convergent speed and precision with respect to four other algorithms such as Evolutionary Algorithm based on Homomorphous Maps (EAHM), Artificial Immune Response Constrained Evolutionary Strategy (AIRCES), Constraint Handling Differential Evolution (CHDE), and Evolutionary Strategies based on Stochastic Ranking (ESSR).

Segmentation of Multispectral Remote Sensing Images Based on Ant Colony Optimization Algorithm Shuo Liu The Institute of Remote Sensing Application, Chinese Academy of Sciences, Beijing, China P.O.Box 9718, Datun Road

+86-10-64862913

[email protected] Yan-you Qiao The Institute of Remote Sensing Application, Chinese Academy of Sciences, Beijing, China P.O.Box 9718, Datun Road +86-10-64862913

[email protected] Qing-ke Wen The Institute of Remote Sensing Application, Chinese Academy of Sciences, Beijing, China P.O.Box 9718, Datun Road +86-10-64889205 [email protected]

ABSTRACT Segmentation of remote sensing image is not only a hot topic but a difficult technological field in remote sensing image processing as well. Recently, Ant Colony Optimization (ACO) algorithm has been introduced into image segmentation. But seldom study has been done in segmentation of multispectral remote sensing images based on Ant Colony Optimization Algorithm. In this paper, ACO algorithm is used in segmentation of multispectral remote sensing images. Three vectors of multispectral remote sensing images at each pixel site are extracted as eigenvectors, such as multispectrum gray values at one pixel site, mean gray values of neighborhood pixels in each band, and multi-spectrum gradient values at one pixel site. They reflect both value features and spatial features of remote sensing images. The combination of these three eigenvectors is used as the fuzzy cluster features. Furthermore, ACO Algorithm is used to optimize fuzzy clustering process. This method not only improves the segmentation result of multispectral remote sensing images, but also controls calculation amount effectively. Experiment and comparison results show that fuzzy clustering algorithm optimized by ACO is a preferable mothod for segmentation of multispectral remote sensing images.

Hybrid Simulated Annealing Algorithm Based on Adaptive Cooling Schedule for TSP

Yi Liu School of Computer Science and Technology, Wuhan University of Technology

[email protected] Shengwu Xiong ¤

School of Computer Science and Technology, Wuhan University of Technology

[email protected] Hongbing Liu School of Computer Science and Technology, Wuhan University of Technology

[email protected] ABSTRACT The traveling salesman problem(TSP) is one of the most notoriously intractable NP-complete optimization problems. Over the last 10 years, simulated annealing and tabu search have emerged as an e®ective algorithm for the TSP. However, the quality of solutions found by using tabu search approach depends on the initial solution and the iteration process of simulated annealing is slow. To overcome this problem and provide an e±cient methodology for the TSP, the heuristic search approach based on simulated annealing which combining tabu search strategy and two neighborhood perturbation factor is developed. The proposed hybrid algorithm is tested on standard benchmark sets and compared with the conventional simulated annealing algorithm. The computational results show that the proposed algorithm has signi¯cantly better convergence speed compared with conventional simulated annealing algorithm and can obtain high-quality solutions within reasonable computing times.

A New Multimedia Information Data Mining Method Jin Longcun Shanghai University No. 149 Yanchang Road

Shanghai, China +86-21-56331619

[email protected] Wan Wanggen Shanghai University No. 149 Yanchang Road Shanghai, China +86-21-56334945

[email protected] Cui Bin Shanghai University No. 149 Yanchang Road Shanghai, China +86-21-56331619

[email protected] Yu Xiaoqing Shanghai University No. 149 Yanchang Road Shanghai, China +86-21-56334945

[email protected] Xu Hongwei Shanghai University No. 149 Yanchang Road Shanghai, China +86-21-56331619

[email protected] ABSTRACT In this paper, we proposed an annotated multimedia information data mining method. We present a Bayesian hierarchical framework model for mining objects in multimedia data. The Multimedia can switch between different shots, the unknown objects can leave or enter the scene at multiple times, and the background can be clustered. The proposed framework model consists of annotation part and Bayesian hierarchical mining part. This algorithm has several advantages over traditional distance-based agglomerative mining algorithms. Bayesian hierarchical hypothesis testing is used to decide which merges are advantageous and to output the recommended depth of the tree. The framework model can be interpreted as a novel fast bottom-up approximate inference method for a process mixture model. We describe procedures for learning the model hyperparameters, computing the predictive distribution, and extensions to the framework model. Experimental results on virtual reality multimedia data sets demonstrate useful

properties of the framework model.

Hybrid EDA-based Optimal Attitude Control for a Spacecraft in a Class of Control Task Xiong Luo Zengqi Sun Department of Computer Science and Technology Tsinghua University Beijing 100084, China National Laboratory of Space Intelligent Control Beijing 100080, China School of Information Engineering University of Science and Technology Beijing Beijing 100083, China

[email protected] [email protected] Xiang Zhang Laihong Hu Chao Wang Yangtze University, Jingzhou, Hubei 434023,China Department of Computer Science and Technology Tsinghua University Beijing 100084, China School of Information Engineering University of Science and Technology Beijing Beijing 100083, China

ABSTRACT In the practical situation, if failure of one of the actuators occurs, there exists the attitude control task of a rigid spacecraft using only two control torques supplied by momentum wheel actuators. Here, this class of control task for a rigid spacecraft is discussed. This nonlinear control problem can be converted to the nonholonomic motion planning optimization problem of a driftfree system. In order to improve the search efficiency of current optimization algorithms, the hybrid estimation of distribution algorithm (EDA) is presented by combing the idea of differential evolution strategy (DES). Then, the optimal attitude control task

for the spacecraft using two momentum wheel actuators is achieved. By comparing the proposed algorithm with existing genetic algorithm and evolutionary programming, the simulation results show the accuracy and efficiency of hybrid EDA.

Emotional Speech Synthesis By XML File Using Interactive Genetic Algorithms Siliang Lv, Shangfei Wang, Xufa Wang Department of Computer Science and Technology Key Laboratory of Software in Computing and Communication in Anhui University of Science and Technology of China, Hefei Anhui, 230027

[email protected] , [email protected] , [email protected] ABSTRACT As a technique that can ”let computer speak”, speech synthesis is drawing more and more attention. Today, much speech synthesis software can synthesize neutral speech naturally and flowingly. However, it is hard to make computers speak with ”emotion” as that in our daily life, because of the complexity of emotion model. Interactive Genetic Algorithms which can be acted self-organizingly, adaptively and self-learningly can just resolve the problem of difficulty in modeling emotional speech synthesis. As a result, this paper designs an emotional speech synthesis process, which adjusts the parameters (XML-tags) used to synthesize emotional speech dynamically, using interactive Genetic Algorithms, to optimize the quality of emotional speech. Also, the paper includes an evaluation experiment, which proves the feasibility of the algorithms.

Computational Model Design and Performance Estimation in Registration Brake Control P.S. Pa Department of Digital Content Design, Graduate School of Toy and Game Design, National Taipei University of

Education No.134, Sec. 2, Heping E. Rd., Taipei City 106, Taiwan +886-2-27321104 [email protected]

S.C. Chang Department of Power Mechanical Engineering, Army Academy No.113, Sec. 4, Zhongshan E. Rd., Zhongli City, Taoyuan County 320, Taiwan

+886-2-27321104 [email protected]

ABSTRACT Electric motorcycles are applicable to both toys and real motorcycles, and also is a reference for constructing larger electrical vehicles. A design computational model of regenerative braking control of electric motorcycles and an experimental identification is presented to achieve regenerative current effectively. The purpose is to extend the driving distance of electric motorcycles by optimizing the brake regeneration energy. Based on the Time Ratio Control (TRC) method, two methods, one using the Hall sensor and the other using the optical encoder for feedback purposes, are proposed to achieve regenerative braking control. Simulation and experimental results show that both methods are effective in tracking the regenerative current command. By evaluating the simulation results, a simulator could provide valuable data to design and analyze prototypes of electrical vehicles. Therefore, rapid prototyping can be achieved to speed up the development of a new vehicle.

Discussion on Convergence of a Fuzzy Adaptive Simulated Annealing Genetic Algorithm Peng Yonggang College of Electrical Engineering, Zhejiang University Hangzhou,Zhejiang,310027,P.R.China

[email protected] Luo Xiaoping* Zhejiang University City College Hangzhou Zhejiang,310015,P.R.China

[email protected]

Wei Wei College of Electrical Engineering, Zhejiang University Hangzhou,Zhejiang,310027,P.R.China

[email protected] ABSTRACT Due to shortcomings of genetic algorithm that its convergence speed is slow and it is often premature convergence, a new improved genetic algorithm---fuzzy adaptive simulated annealing genetic algorithm (FASAGA) is presented by integrating fuzzy inference, simulated annealing algorithm and adaptive mechanism. The strong Markovian property attributed to the population sequence was deduced by mathematical modeling. Then the convergence in probability of the fuzzy adaptive simulated annealing genetic algorithm was proved on the condition that the time tended to infinity. The results show that the methods are helpful for directing choice of better FASAGA parameters and improving the performance of the algorithm.

A Hybrid Simulated Annealing Algorithm for Container Loading Problem Yu Peng Department of Computer Science Xiamen University China

[email protected] Defu Zhang Department of Computer Science Xiamen University China

[email protected] Francis Y.L. Chin Department of Computer Science The University of Hong Kong Hong Kong

[email protected] ABSTRACT

This paper presents a hybrid simulated annealing algorithm for container loading problem with boxes of different sizes and single container for loading. A basic heuristic algorithm is introduced to generate feasible solution from a special structure called packing sequence. The hybrid algorithm uses basic heuristic to encode feasible packing solution as packing sequence, and searches in the encoding space to find an approximated optimal solution. The computational experiments on 700 weakly heterogeneous benchmark show that our algorithm outperforms all previous methods in average.

A Dynamic Evolutionary Algorithm and Its Application in Automated Antenna Design Danping Yu , School of Computer Science, Research Center for Space China University of Geosciences Science & Technology Wuhan, 430074, China

[email protected] Sanyou Zeng School of Computer Science, Research Center for Space China University of Geosciences Science & Technology Wuhan, 430074, China

[email protected] Song Gao, Zu Yan, Yulong Shi, Xianqiang Yang, Bo Xiao School of Computer Science, Research Center for Space China University of Geosciences Science & Technology Wuhan, 430074, China

[email protected] ABSTRACT Abstract: An X-band antenna has been designed for NASA’s Space Technology 5 (ST5) spacecraft by using genetic algorithm.

It had been deployed on schedule on March 22-June 30 2006 and became the first evolved hardware in space. It is known that antenna design is a complicated optimization problem with many constraints. In this paper, we take a different way to solve antenna problems: A dynamic evolutionary algorithm (DEA) is designed for solving general constrained optimization problems and well tested by a kit of benchmark constrained problems firstly. Then the algorithm is used to solve antenna design problems.Simulation results are quite promising. Our evolved antennas are quite competitive with NASA's. The algorithm will be applied in real antenna design in our future work. Keywords: Evolutionary algorithms, automated antenna design, constrained optimization, dynamic optimization

Feedback-Control Modeling for Cellualr Response Mechanisms based on a Gene Regulatory Networks under Radiotherapy Jinpeng Qi*, Shihuang Shao, and Zhihai Rong College of Information Sciences and Technology, Donghua University, Shanghai 201620, China Engineering Research Center of Digitized Textile & Fashion Technology, Ministry of Education Donghua University, Shanghai 201620, P. R. China

[email protected], [email protected], [email protected], ABSTRACT In response to genome stresses, cell can trigger the self-defensive mechanisms by regulating the vital genes and their complicated signal pathways. To illustrate the celluar response in fighting against DNA damage under radiotherapy, a feedback-control model of P53 stress response networks is proposed at single cell level. The kinetics of Double Strand Breaks(DSBs) generation and repair, ARF and ATM activation, P53-MDM2 regulation, toxins degradation, as well as feedback-control to ion radiation (IR) dose are presented.

Evolutionary Algorithm for Multi-objective

Optimization and its Application in Unmanned Flight Vehicle Trajectory Control Xu Qian Beijing Institute of Technology School of Aerospace Engineering South Zhongguancun Street.5 +8615711000124

[email protected] Tang Shengjing Beijing Institute of Technology School of Aerospace Engineering South Zhongguancun Street.5 +8613911082906

[email protected] Guo Jie Beijing Institute of Technology School of Aerospace Engineering South Zhongguancun Street.5 +8613520828290

[email protected] ABSTRACT To make sure that unmanned flight vehicle safely landed on the ground, it is necessary to control its trajectory. By adopting proper control law and optimization, the vehicle can achieve a perfect landing, and resources can be most economically assigned. It is a multi-parameters and multi-objectives optimization (MPMO) problem. Two primary problems exist in traditional way: must simplify equation and easy to trap in constrained results. To solve these problems, an evolutionary algorithm using following strategies is adopted: 1. An interface for Simulink toolbox of Matlab, serving as core of the fitness function computing module; 2. Norm based Regret Function serving as fitness function; 3. Adaptive crossover and mutation probability; 4. Elitist strategy. Result proves that the “Improved Genetic Algorithm (IGA)” has better ability in dealing with multi-objective optimization. Finally, the trajectory optimization problem of an unmanned flight vehicle is solved, and the result is satisfying.

Log-optimal Portfolio Models with Risk Control of VaR and CVaR Using Genetic Algorithms Sen Qin School of Science, Hangzhou Dianzi University Hangzhou, Zhejiang, 310018, P.R. China

[email protected] ABSTRACT Value-at-risk (VaR) and conditional value-at-risk (CVaR) have become two very popular measures of market risk during the last decade. Log-optimal portfolio problem with risk control of VaR and CVaR is put forward ¯rstly. Then, we propose the portfolio models with VaR and CVaR and prove the existence and uniqueness of the optimal solutions of these two models. We provide a newly genetic algorithm based on real-code strings of assets' returns to overcome the problem of local optima. Finally, an empirical study is carried out to illustrate the optimal solutions of the log-optimal portfolio models with VaR and CVaR. The numeric results indicate that the optimal portfolio of the log-optimal portfolio model with CVaR gives a balance between the investment risk and the return simultaneously, and is more e®ective than the corresponding portfolios of the VaR model and the mean-variance model.

Selected Population Characteristics of Fine-grained Parallel Genetic Algorithms with Re-initialization Ivan Sekaj, Michal Oravec

Institute of Control and Industrial Informatics Faculty of Electrical Engineering and Information Technology, Slovak University of Technology Ilkovičova 3, 812 19 Bratislava, Slovak Republic

[email protected], [email protected]

ABSTRACT A class of fine-grained parallel genetic algorithms (F-PGA) are analyzed and experimentally compared. Each node of the F-PGA represents a single individual. Selected topologies are proposed, which are using various parent selection and offspring selection methods. Also the influence of population re-initialization on the parallel genetic algorithm performance is analyzed and selected characteristics of evolutionary algorithm population are proposed. These characteristics represent such properties as relative number of modified genes and number of duplicate individuals in population. The results are demonstrated on examples with minimization of selected test functions.

Structural Damping Identification Using Analytic Wavelet Transformation Shen Jian-hong Institute of Civil Engineering Qingdao Technological University China Qingdao 266520

[email protected] Li Chun-xiang Department of Civil Engineering Shanghai University China Shanghai 200072

[email protected] Li Jin-hua Department of Civil Engineering Shanghai University China Shanghai 200072

[email protected]

ABSTRACT By applying the Analytic Wavelet Transform (AWT) based on Gabor wavelet function in conjunction with the well-known Random Decrement Technique (RDT), this paper analyzes the time-frequency resolution of Gabor wavelet and the process of identifying structural damping parameters. The method selecting the parameters of Gabor wavelet function and the formula determining the usable length of signal are thus proposed. Eventually, the efficiency of the present method is confirmed by applying it to a numerical simulation data of a three

degree-of-freedom (3DOF) structure with the closely natural frequencies and to ambient vibration measurements of a super high-rise building excited by wind.

MILCS in Protein Structure Prediction with Default Hierarchies Robert E. Smith Department of Computer Science University College London London, United Kingdom +44 7771852565

[email protected] Max K. Jiang Department of Computer Science University of London London, United Kingdom +44 7828761996

[email protected] ABSTRACT This paper studies the performance of a newly developed supervised Michigan-style learning classifier system (LCS), called MILCS, on protein structure prediction problems and our observation of its default hierarchies (DHs). We present experimental results, and contrast them to results from other machine learning systems, named XCS, UCS, GAssist, BioHEL, C4.5 and Naïve Bayes. We use our technique for visualizing explanatory power of the resulting rule sets and their hierarchical structure. Final comments include future directions for this research, including investigations in neural networks and other systems.

Maximum Margin Transfer Learning∗ Bai Su Institute of Software, Chinese Academy of Sciences Graduate University of Chinese Academy of Sciences P.O.Box 8718, Beijing, China

[email protected] Yi-Dong Shen Institute of Software, Chinese Academy of Sciences P.O.Box 8718, Beijing, China

[email protected] ABSTRACT To achieve good generalization in supervised learning, the training and testing examples are usually required to be drawn from the same source distribution. However, in many cases, this identical distribution assumption might be violated when a task from one new domain(target domain) comes, while there are only labeled data from a similar old domain(auxiliary domain). Labeling the new data can be costly and it would also be a waste to throw away all the old data. In this paper, we present a discriminative approach that utilizes the intrinsic geometry of input patterns revealed by unlabeled data points and derive a maximummargin formulation of unsupervised transfer learning. Two alternative solutions are proposed to solve the problem. Experimental results on many real data sets demonstrate the effectiveness and the potential of the proposed methods.

Traffic Flow Forecasting Based on Multitask Ensemble Learning Shiliang Sun Department of Computer Science and Technology, East China Normal University 500 Dongchuan Road, Shanghai 200241, China

[email protected] ABSTRACT A new method for traffic flow forecasting based on multitask ensemble learning, which combines the advantages of

multitask learning and ensemble learning, is proposed. Traditional traffic flow forecasting methods are a single task learning mode, which may neglect potential rich information embedded in some related tasks. In contrast to this, multitask learning can integrate information from related tasks for effective induction. Recent developments also witness the potential of ensemble learning for traffic flow forecasting. This paper devises a new method named MTLBag, a combination of multitask learning and a famous ensemble learning method bagging, for traffic flow forecasting. Using a neural network predictor, this paper first empirically shows the superiority of multitask learning over single task learning for traffic flow forecasting. Experimental results also indicate that the performance of MTLBag is statistically significantly better than that of the multitask neural network predictor, and that MTLBag outperforms a state-of-the-art method Bayesian networks.

Distributed Risk Management Model and Algorithm for Virtual Enterprise with Private Information Xianli Sun1,2,3, Min Huang1,2, Xingwei Wang1, Fuqiang Lu1,2 1. College of Information Science and Engineering, Northeastern University, Liaoning, 110004, China. 2. Key Laboratory of Integrated Automation of Process Industry (Northeastern University), Ministry of Education, Liaoning, 110004, China 3. Department of Information and Engineering, Shenyang Institute of Engineering, Liaoning, 110136, China +86-24-83671469

[email protected] ABSTRACT For the desired profit and anticipated goal, the virtual enterprise (VE) must avoid the risk successfully. In view of its characteristics, such as the diversity of partners and distribution of cooperative regions, the idea of distributed decision-making (DDM) is applied to the management of the virtual enterprise’ risks, with a Organizational-DDM risk management model developed for those virtual enterprises which are in relation to enforced team and with private information. A taboo search algorithm is designed to solve the model. The computation results

of simulative examples show the effectiveness and feasibility of the model and algorithm

Quantum and Biogeography based Optimization for a Class of Combinatorial Optimization Li-xiang Tan Dept. Electronic Science and Technology, University of Science and Technology of China Postbox 4, Hefei, China, 230027 +86-551-3601802

[email protected] Li Guo Dept. Electronic Science and Technology, University of Science and Technology of China Postbox 4, Hefei, China, 230027 +86-551-3601802

[email protected] ABSTRACT In this paper, an algorithm named Quantum and Biogeography based Optimization(QBO) is proposed to investigate the possibility of optimization by evolving multiple Quantum Probability Models(QPMs) via evolutionary strategies inspired by the mathematics of biogeography. In QBO, each QPM modeling an area in decision space represents a habitat, the whole population of QPMs evolve as an ecosystem with multiple habitats interacting. The migration and immigration mechanisms originally presented in Biogeography Based Optimization (BBO) [1] is introduced into QBO to implement the efficient information sharing among QPMs, which enhance the evolution of probability models towards the better status that can generate more better solutions. Experimental results on classical 0/1 knapsack problems of various scale show that the mechanisms in BBO are feasible to evolve multiple QPMs, and QBO is efficient for hard optimization problem.

Using GA-ANN Algorithm to Predicate Coal Bump Energy

Yunliang Tan Key Laboratory of Mine Disaster Prevention and Control of Education Ministry 579 Qian-wangang Road, Qingdao Economic and Technical Developing Zone,Qingdao, China 86-532-86057017

[email protected] Tongbin Zhao Natural Resources and Environmental School, Shandong University of Science and Technology 579 Qian-wangang Road, Qingdao Economic and Technical Developing Zone, Qingdao, China 86-532-86057946

[email protected] Zhigang Zhao Natural Resources and Environmental School, Shandong University of Science and Technology 579 Qian-wangang Road, Qingdao Economic and Technical Developing Zone, Qingdao, China 86-532-86057946

[email protected] ABSTRACT A GA-ANN network was constructed for preidcating coal bump energy, based on the 300 training samples form simulated results with PFC2D software for different coal particle stiffness. It was tested that the average relative error of fitted-output value is only 2.5%, the averagre relative error of generalized predicated output is only 8.4%.It is valuable for coal bump energy predication

Modelling and Evolutionary Multi-objective Evaluation of Interdependencies and Work Processes in Airport Operations

Jiangjun Tang ITEE, UNSW@ADFA Canberra, Australia

[email protected] Sameer Alam ITEE, UNSW@ADFA Canberra, Australia

[email protected] Hussein Abbass ITEE, UNSW@ADFA Canberra, Australia

[email protected] Chris Lokan ITEE, UNSW@ADFA Canberra, Australia

[email protected] ABSTRACT An airport is a multi-stakeholders environment, with work processes and operations cutting across a number of organizations. Airport landside operations involve a variety of services and entities that interact and depend on each others. In this paper, we introduce the Landside Modelling and Analysis of Services (LAMAS) tool to simulate, analyze and evaluate the interdependencies of services in airport operations. A genetic algorithm is used to distribute resources among the different entities in an airport such that the level of service is maintained. The problem is modelled as a multiobjective constrained resource allocation problem with the objective functions being the maximization of quality of service while reducing the total cost.

A GA-Based Automatic Pore Segmentation Algorithm Hangjun Wang School of Information Information Science and Technology, ZheJiang Forestry University, Linan, China 311300

[email protected] Hengnian Qi School of Information Information Science and Technology, ZheJiang

Forestry University, Linan, China 311300

[email protected] Wenzhu Li School of Engineering, ZheJiang Forestry University, Linan, China 311300

[email protected] Guangqun Zhang School of Information Information Science and Technology, ZheJiang Forestry University, Linan, China 311300

[email protected] Paoping Wang College of Computer and Information Technology, Nanyang Normal University, Nanyang, China 311300

[email protected] ABSTRACT Pore feature is important for hardwood identification. But it’s difficult to segment pores from wood cross-section images since pore, fiber and longitudinal parenchyma in the image are similar in shapes but different only in size, and the different hardwood species varies in the size of pores. In order to segment pores automatically without parameters set manually, it is necessary to design an adaptive algorithm which may be applied for all kinds of hardwood cross-section images. In the paper, an adaptive method is proposed to evaluate the optimal threshold of closed region area for pore segmentation. The method sorts all closed regions according to the area and classifies closed regions into two classes with maximum between-class variance method. We implements the method based on genetic algorithm to overcome the drawback of being time-consuming. Experiment on images of hardwood species shows that the threshold obtained by the genetic algorithm is very close to but more efficient than the ordinary enumeration algorithm. Moreover, with the obtained threshold majority of pores can be extracted except for some very small ones.