Proceedings of 2015 IEEE 12th International Conference on Networking, Sensing and Control Howard Civil Service International House, Taipei, Taiwan, April 9-11, 2015
Social Network-based Swarm Optimization Algorithm Xiaolei Liang, Wenfeng Li, PanPan Liu, Yu Zhang and Aaron Agbenyegah Agbo School of Logistics Engineering Wuhan University of Technology, Wuhan, China {liangxiaolei, liwf, liupanpan, sanli}@whut.edu.com and
[email protected] introduced in SNSO to utilize individuals’ information efficiently. Considering labor division in insect and animal groups, SNSO classifies the individuals into two classes and update them by different models. In this paper, the proposed algorithm is compared with other well-known SI algorithms via several benchmark functions. This paper is organized as follows. In Section Ⅱ SNSO and its characteristics are both described. Section Ⅲ outlines the results, concludes and gives the future research directions.
Abstract—We propose a new population-based optimization algorithm, named Social Network-based Swarm Optimization algorithm (SNSO), for solving unconstrained single-objective optimization problems. In SNSO, the population topology, neighborhood structure and individual learning behavior are used to improve the search performance of a swarm. Specifically, a social network model is introduced to adjust the population topology dynamically, so as to change the information flow among different individuals. Based on the new topology, an extended neighborhood strategy is provided to build a neighborhood for each individual. Different form other forms of neighborhoods, the new structure includes some real individuals connected to the current one and some virtual individuals having better fitness in history, which could bring to more useful information to individuals for avoiding invalid attempts. Furthermore, we propose a new learning framework that defines two different position update methods for two types of individuals with the aim of enhancing the diversity and search ability of the swarm. The performance of SNSO is compared with seven other swarm algorithms on twelve well-known benchmark functions. The experimental results show that SNSO has a better performance than the selected algorithms.
II.
A. Dynamic population topology based on social network model Population topology describes the social relationships of a swarm. The learning behavior is based on these social relations for individual to learn from the others. In SI algorithms, there are two main structures of the population topology: static and dynamic. The social relations of a population in a static topology are fixed, signifying that the individuals learn knowledge in a stable microenvironment. Differently, a dynamic model, in which the size of the population or relationships among individuals is variable when searching, allows the topology to change dynamically during the whole optimization process. Since the process of swam search is nonlinear and dynamic, the static population topology constrains the ability of an individuals to learn from others in a swarm. Compared to a static topology, a dynamic topology can introduce more opportunities for individuals to learn from others. Researches have proven that a dynamic topology is beneficial to swarm search and can thus improve the performance of SI algorithms.
Keywords—swarm intelligence; algorithm; optimization; social network
I.
INTRODUCTION
Swarm Intelligence (SI) is an intelligence optimizations technology whereby the cooperation behaviors of individuals are well utilized to solve real-world complex problems. Over the last decades, it became very popular. Various SI algorithms have been proposed and applied in the field of science and engineering. Most of them such as genetic algorithms (GA) [1], particle swarm optimization (PSO) [2], ant colony optimization (ACO) [3], artificial bee colony algorithm (ABC) [4], biogeography-based optimization (BBO) [5], firefly algorithm (FA) [6] and cuckoo search algorithm (CS) [7] have been successfully implemented to solved numerous optimization problems. However, no single algorithm that can be the best for solving all types of problems [8]. How to improve the performance of the algorithms is still an important topic in SI research.
Multiple clusters [9], neighborhood extending [10] and varying population size [11] are common structures of a dynamic topology. Their main characteristic is the adjustment of the population topology based on distance among individuals in a search space. That can make the individuals easier to communicate with each other in the same area. Its advantage is that it can drive the swarm to fully exploit the current local area. Unfortunately it also brings a negative effect that the individuals tend to be dropped into local extremum area easily if no good jump mechanism is in place. Hence we intend to propose a novel strategy of population topology adjustment based on social network evolution. It considers an individual’ ability (e.g. fitness) and its social influence (e.g. connectivity) in the population. According to the rule of social
Topology, neighborhood and individual’s behavior are the key components of swarm intelligence. Based on them, a novel swarm algorithm, called social network-based swarm optimization algorithm (SNSO) is proposed in this paper. The SNSO adopts a social network evolution model to adjust the topology dynamically. An extended neighborhood structure is
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PROPOSED ALGORITHM
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relation construction, the individual who has greater ability and social influence is more likely to affect others. This strategy weakens the effect of geographical positions and makes the individual not only learn from other nearby individuals’ information but also have the opportunity to connect to some outstanding individuals in other areas to obtain new information. In order to improve the diversity of the swarm, we divide the swarm into two different subpopulations normal individual (NI) and random individual (RI) based on fitness. Then the algorithm of dynamic population topology based on social network model can be described as Algorithm1.
Ⅳ j=1 Choose individual k with Pk m,n∈Nek j=j+1 Adj(m, n)=0 && D(m)
