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(IJEECS) International Journal of Electrical, Electronics and Computer Systems. Vol: 02 Issue: 02, June 2011

QoS-aware Composite Web Service Selection using Memetic Algorithm G.Krishnaveni#1, M.Sathya*2, C.Rajeswary#3 Department of Computer Science Pondicherry University, India. 1

[email protected] , [email protected] , [email protected]

Abstract-- QoS driven composite web service selection (WSS) is a process of selecting the group of services to form abstract services is the NP hard as well as combinatorial optimization problem; evolutionary algorithms are more suitable to solve that kind of problem. The evolutionary algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and co-operative coevolutionary algorithms and so on are used to solve WSS problem with QoS constraint up to some extent. As it plays a vital role in web service composition process, it has direct relation with the quality of composite service. In order to improve the QoS parameter in solving WSSP, Memetic Algorithm (MA), a metaheuristic optimization algorithm is applied in this paper. MA employs two algorithms to select the services where, GA is used in WSSP first to perform global search and hill climbing (HC) algorithm is used as a sub module in GA where local search is performed. HC algorithm employed as a part of MA to provide the QoS in efficient way and better convergence results. Service QoS representing through trusted ontology. Advantage of this ontology is well representation and understanding of concepts. We have applied four optimization test functions compared with GA and PSO shows better performance. Experimental analysis are done through optimization test functions results the memetic algorithm can better satisfy QoS as well as time convergence requirements than other algorithm for optimal composite web service selection. Keywords-- Web service selection; genetic algorithm; hill climbing algorithm; memetic algorithm; composite service; QoS constraints; convergence factor

I. INTRODUCTION Service oriented architecture (SOA) has been employed in the past years for developing web services (WS), it has the large framework of software and protocols to integrate those services in flexible way. WSS is defined as the process of choosing the appropriate services from the service set which satisfies user requirements. Web services have been stored in the registry as in a static way, it has the ability to provide the solution for single functional problems but it does not provide solution when user requesting a composite service [1]. Dynamic integration of all the web services to provide the new valueadded and composite web services in order to meet the requirements of users. Web services composition is necessary to make coordination among the service resources, in which services are discovered, selected and assembled into a much more grouped service to satisfy the application requirement of

user. According to the performance of dynamic composition, the services has been composed in two way which are static service composition and dynamic service composition based on whether the service information is static or dynamic [2]. During service composition to get composite services, consider the following characteristics: Isolation of Web services, the services selection process separation and the rapid increase of web services [1]. QoS defines the degree of performance of a web service. The parameters used for estimating the QoS are not static and those are determined at run time. Some of the QoS parameters which are used for WSSP are time convergence, availability and accuracy. These QoS parameters are used non-functional requirements to make the WSS optimal. Therefore, it is very difficult to dynamically select the Web service based on QoS for services composition. In most of the distributed environment, the functional attributes provided by them are more or less same whereas the nonfunctional attributes varied according to their nature of work [1]. So the WSS problem is to select the optimal service that provides the functional as well as non-functional requirements of users from this dynamic composite web services. Based on the description logic, the target layout algorithm and efficient reasoning are used to provide the dynamic service composition. Then selection of services from the service composition is carried out effectively where the process of QoS scheduling is selecting the optimal QoS service from the participant services which is having different quality is represented as spanning graph. But scheduling algorithm is based on the QoS localbest. But this cannot resolve the QoS global optimization problem because it does not provide the composite service instances with each node. Then the weighted linear method is applied in order to transform multi-restriction parameters (service QoS) to a Single-goal function It satisfies the local optimization but it is hard to express the robustness while using PSO algorithm. As it is an evolutionary algorithm depends fully on iteration. In order to overcome that problem, GA parameters and PSO algorithm are combined to decrease the iteration loops for searching a better solution, thus reduces the causes of a rapid increase services on the service enterprises to form the service composition which provides the service requirements. In that, PSO uses GA’s crossover and mutation operations to resolve the problems of estimate the

(IJEECS) International Journal of Electrical, Electronics and Computer Systems. Vol: 02 Issue: 02, June 2011 best route to achieve QoS service and provides the position and the velocity of the services. GA is a population-based algorithm [3] used to solve the combinatorial optimization problems [4]. It solves the formulated optimization problem using Darwin’s evolutionary theory. It is an iterative method in which the population size is constant where the solution is eliminated from each of the individuals in the given population. The general evolution operation involves crossover, mutation and selection operation which makes efficiently process the global search using GA. The design of genetic algorithm has the greatest influence global QoS constraints. It is need for GA to accord with the characters of web services composition for the global convergence. In service selection, binary strings of chromosome were proposed and every gene in the chromosome represented as candidate who provides services with values of 0 and 1. By increasing the gene’s chromosome increases the number of service candidate or web service clusters. Then selecting the service is complex if length of the genosome increases and readability also going worst. The coding manner of chromosome and the fitness function are proposed in GA, but it does not provide the clear information about the algorithm. Only talks about the service selection in stable manner and also the prematurity in genetic algorithm makes the greater problem which is overcome by population diversity handling [5]. In some extent the modified diversity control is not applicable to eliminate the prematurity problems so they moving steps towards an immune algorithm which is actually a modified genetic algorithm. In that modified proposal the reverse meaning of diversity with affinity is described in terms of information entropy of each and every gene in the chromosome. At result the higher priority is evolved with the higher fitness and lower affinity of the individual. The diversity of the population is also derived using the concepts of information entropy. In order to calculate the selection probability, a hamming distance is taken between the common individual and the one which has the best fitness value was used as a main criterion. This satisfies the global optimum without getting stuck at a local optimum which is also the type of individual diversity controlling approach. In order to maintain the normal population diversity during the GA’s operation a fuzzy controller is used to change the crossover as well as mutation rate by using population diversity measurements. Generally during the population evolution, the initial having higher value than the terminal and checking whether the current population has the major priority than the former one to attain the local optimal solution which satisfies the QoS factors. In genetic algorithm, the relation matrix coding scheme is used to achieve QoS-aware web service selection [5]. It traps of falling in to local optimal solution and

for diversity control the simulated annealing is used. GA overcomes the validity and pre-maturity but it needs some other algorithm to satisfy the convergence factor. Linear programming using simplex method has been used to determine the objective weights of QoS criteria [6]. Even though it provides QoS- based ranking of list of web services in realistic way, problem with this approach is it took more convergence time. The artificial neural networks (ANN) are used for service selection process in ubiquitous environment where the genetic algorithm is used with fuzzy logic system [2]. This system provides QoS in dynamic manner also with convergence but the problem with that is there are no rules to set an inequality parameters and also it needs the NN prediction controller to control the ubiquitous services which is costlier than web service selection controller. Web service selection is based on Fuzzy linear programming technologies (FLP) in [6], Integer programming (IP) in [7] and bio-inspired algorithms such as genetic algorithm (GA), Artificial neural networks (ANN) and PSO. In QoS scheduling algorithm, while calculating the QoS it consider number of non-functional properties such as reputation, price, availability and response time. Through the fair computation of QoS of each component web services, the QoS value of composition is reached. QoS matrix is a representation solution in the QoS computation. Normalizing the QoS matrix provides the ranked web services, however it is only a local optimization algorithm. But it does not provide the global QoS constraints. Thus by those existing algorithm for web service selection attain QoS but convergence up to some extent so in this paper the memetic algorithm is proposed for effective service selection where the ontology makes easy understanding of composite service QoS and efficiently satisfies time convergence and MA works in two phases which are: (i) QoSaware service selection and (ii) Composition process to provide composite service selection. The rest of this paper is organized as follows: section 2 covers the memetic algorithm for service selection problem, section 3 covers matching service selection with algorithm, section 4 covers the architecture of WSS using memetic algorithm, section 5 has Experiments and results and section 6 summarizes the conclusion. II. MEMETIC ALGORITHM FOR WEB SERVICE SELECTION PROBLEM Memetic algorithm is otherwise called as Genetic Local Search method (GLS). Generally it is a population-based method which combines the local search procedures with crossing or mutating operators (in GA) [3]. Because of its structure is similar to genetic algorithm it is also called as Hybrid Genetic Algorithms (HGA) and Parallel Genetic Algorithms (PGA) [8]. This method is widely used in the

(IJEECS) International Journal of Electrical, Electronics and Computer Systems. Vol: 02 Issue: 02, June 2011 problems of combinatorial optimization and it uses memes instead of genes i.e. the GA using biological evolution and MA uses Mimic cultural evolution [9][10]. In the first step, MA generates an initial population of random solutions from that problem starts which is to be solved and improving these solutions through local search method. Then this problem is formulated in vector form or chromosome representation. In second step, the fitness value for each of the chromosomes is derived so the solution is classified from best to worst based on the objective function and from the initial population the subset of the nsel best solutions is selected. In third step, the memetic algorithm applies the crossover and mutation operators are applied where exchanging the information’s in the former solution and mutated. Then the local search procedure is applied in order to improve the obtained solution which is so called as child solution. In final stage, the worst solution in population is improved by the child solution then this solution is replaced. MA uses metaheuristic strategy where the use of meta-heuristic allows us to obtain reasonably better solutions without having to take the whole solution space [8]. To increase understandability of the problem the solution is represented in the binary form which consists of 0’s and 1’s (called as pseudo Boolean operators in the function). The crossover operator is the most essential element in the functioning of memetic algorithm [11] [12]. During search, the uniqueness between the highly suited (fitness) strings found. Thus the subset of similar strings is placed in a particular position as similarity template called schema. In parent chromosomes, the schemata’s are hidden behind it. In order to extract them as an evolved population from the parent population efficiently the crossover operator is used. Let s1 and s2 be the solutions from that the child solution s3 is derived where the crossover operator is works as a single point crossover so the crossover points are generated between 1 and m-1 or 0. The mutation operator helps to maintain the diversity. From each offspring, each of the elements can mutate which is in the probability of p_mut [12]. If a needed element is changed then the random value is generated in (0, 1) and if p_mut is greater than the given value then change the elements. This change makes selecting the new element which is not present in the solution. A. Steps involved in Memetic Algorithm Step 1: An initial population of random solutions of size npop is generated. Step 2: Improve these solutions through local search. Step 3: Repeat the following steps: 1) Select the subset of nsel elements of the population with the highest fitness value.

2) Cross reproduction: cross a pair of these parent solutions to derive the new child solutions. From each pair of parent a new pair of child solutions is generated. 3) Mutation: the child solutions can change some of their elements with a small probability of p_mut. 4) Improve child solutions with local search. 5) Population replacement: substitute the worst solutions of the population with the new child solutions. Until reaching a number of n_iter iterations. Step 4: Choose the highest fitness value solution as the final solution. The flow diagram of this memetic algorithm clearly explains the working of this algorithm and also shows the condition to attain the convergence. B. Local search-Hill climbing algorithm The generalized Hill climbing (HC) algorithm is used in local search which describes the meta- heuristics to solve the optimization problems [3]. There is a variation in the hill climbing operations based on the functioning .In case of normal HC; there the problem occurs if the search space is so large [13]. In this paper, the First-choice hill-climbing method is used to obtain the optimized result even there is a large number of successors is present and produce the value either as objective function or heuristic function. The search process will terminate if the better state or goal state in the state space is achieved, that is it reached the global maximum from the local maximum Procedure of Hill climbing algorithm to perform local search is given below: Step 1: Generate the initial population randomly. Step 2: Convert the population into the binary strings. Step 3: Evaluate the initial population and apply the operators to produce the new state. Step 4: Then evaluate the new state to check whether it is a goal state, if it no then consider that state as current state. Step 5: From the current state evaluate the strings in order to achieve the improvement of the objective function. Step 6: check again goal state is reached to provide the effective convergence with local maximum, if yes then stop the process.

(IJEECS) International Journal of Electrical, Electronics and Computer Systems. Vol: 02 Issue: 02, June 2011

Fig.2 Global maximum using Hill climbing

Fig.1 Flow diagram of memetic algorithm

Thus by [11] the hill climbing algorithm provides the optimal service selection with best time convergence. [9] During the local search in memetic algorithm using hill climbing for service selection consider the following: 1)Representation of the solution. 2)Evaluation function. 3)Neighbourhood function: Used to define solutions which can be considered close to a given solution. For example: For optimisation of real-valued functions in elementary calculus, for a current solution x0, neighbourhood is defined as an interval (x0 –r, x0 +r). And in clustering problem, all the solutions which can be derived from a given solution by moving one customer from one cluster to another. 4)Neighbourhood search strategy: random and systematic search. 5)Acceptance criterion: first improvement, best improvement, best of non-improving solutions, random criteria.

III. MAPPING OF MA WITH WSS In memetic algorithm, the concepts of genetic algorithm are used and the sub-module of GA consists of hill climbing algorithm used for local search. The chromosomes in the binary string format are represented as service selection. So every gene present in the chromosome is considered as service candidates with the values of 0 and 1. If the service candidates are increased then it is called as service cluster. When selecting the service it is necessary to select the clusters of services too. The problems arrives during the length of composition of services increases when GA resolves the service selection problem by representing the composition of services in terms of composite services which is defined as a service created by combination of other services to complete particular service activities and each service in composition as an abstract services [7]. Thus the abstract services from different composite services are grouped to select as a new service that would not affect the existing composite services. The GA has the special accounts to web service selection with QoS constraints globally. The pre-maturity of GA using the diversity control of population diversity. Hence the fitness value can be calculated between two values to obtain the quality of services (QoS) and the divergence of GA to hill climbing provides the convergence to attain the global maximum (global optimal solution) without stucking at the local maximum (optimal solution).The convergence in the sense, increasing the speed of processing as well as taking the shorter route to get the service. The route has been initiated and speed of the service arrival through that route is represented in terms of velocity. A. Working of MA in architecture level The components in the web service selection architecture using memetic algorithm involve service discovery engine, service selection engine and service storage manager apart from service requestor and provider. Functions of each module are described below:

(IJEECS) International Journal of Electrical, Electronics and Computer Systems. Vol: 02 Issue: 02, June 2011

1) Service discovery and selection module: It discovers all the available services and checks the quality of those services which is done in Qos evaluator where the genetic algorithm is used. Then WSS matchmaker matches the features of services requested with the available service and stores temporarily the matched services in it. Finally, the query processing engine updates the query given by the requestor and processes it and sends it to the WSS module where memetic algorithm works. Otherwise again passes it to matchmaker if requested service unavailable. And service selection consists of 3 sub-modules which are service invoker, service selector and performance analyzer [6]. The service invoker invokes services from the service discovery engine and passes it to selector for selection. The selector selecting the optimal services where memetic algorithm is used to achieve the global optimization and hill climbing algorithm is used to boost up the local optimization prevents the struck in local minima. Then the performance analyzer checks the quality of that selected service along with the convergence time of selecting the service. The report generation process done by report module which defines what are all the process happens from the starting of service selection to composite services. 2) Service composition for composite WSS module: In order to get the composite service, the service composition is needed. The composition of services is purely based on the functional and non-functional requirements of the user query. After composition, the service engine again provides the optimal composite service. Service storage manager consists of two types of registry and trusted ontology. The local registry stores the recent services and its related data’s and UDDI (Universal descriptive and discovery Integration) registry is a permanent storage where the available media services, streaming services, and transcoding services are register automatically after the discovery. In UDDI, the transcoders and the streamers are present in order to register the services while the particulars about the address are mentioned such as proxy nodes, transcoders etc .The transcoders use the same IP address of the Proxy address in some cases. At last, it generates the report. The ontology is a consensual semantic specification of the quality for a specific field. It consists of service groundings, profiles and its models. In addition to the multi-agent system, it provides a common vocabulary for quality and provides the communication and reasoning among agents such as broker’s agent.

S

Service discovery engine

E R

E QoS evalua tor

WSS matc h make r

V I C E

Query processi ng engine

Service selection engine Web service invoke r

Memetic algorithm =GA + HCA

Performan ce matchmak er(QoS,

converg ence)

P R

I C E R

U Local regist ry

UDDI registr y

T.ont ology

D

R

V

Q

V

E

R

E

Service storage Manager

O

I

S

E S

Report s

Fig.3 WSS architecture deploying MA

Service profiles

Service groundi ngs

Service model

T O R

Representation of input and output to the WSS is based on the ontology. And classification of service is related with ranking of services [13] [14]: Input: Set of services S, request Q in WSDL using ontology (used for representing the information about QoS). Output: The rank list of relevant services for each pair(S, Q) do 1) Logical matching: Semantic bipartite operations graph match. 2) Syntactic matching: Text similarity of semantic signatures. 3) Structural matching of WSDL files S and Q by WSDL Analyzer. Compute Match(S, Q, KB) Hybrid belongs to {Rl, NotRl} x [0, 1] Where Rl is reliable and NotRl is non reliable. B. QoS composite selection process Phase-I

(IJEECS) International Journal of Electrical, Electronics and Computer Systems. Vol: 02 Issue: 02, June 2011 The service selection is performed from the composition in order to get the maximum services with QoS .The QoS having 4 properties based on that the service selection is doing for composition which are: 1) Effect Level: used in discrete manner to specific elements of a Web service, such as its services, interfaces and operations etc. 2) Quality Level: specify different usage modes so that a service requester meets the demand by selecting the suitable quality levels. 3) Roles: With providers and requesters, other clients also are supported in a process of measurement and evaluation of QoS information, such as certificate authorities. 4) Constraints: It relates to the question of how to specify a constraint on a QoS property. Almost the simple operators like >, =, =< are used for expressing QoS constraints. Also the other operator’s related to value like string type, set, list, etc. Probability values of selection of services at a time:

TABLE I PARAMETERS DEFINED USING MEMETIC ALGORITHMFOR WEB SERVICE SELECTION

Exp value (i, t) = S + (S - S ) (rank (i, t)-1)

l h l where, Sh - higher priority services, Sl- lower priority services. The objective function isNsto -1 maximize the quality of service and minimize the convergence time and the convergence time is expressed as

Tconv = Rs+ sum (RsTc-1Lt-1) + sum (Tmut) Thus the fitness function is same as the convergence time, it is represented as F(x) = Tconv And the QoS=max (accuracy + availability) where, availability=max (Ss) and accuracy is Ss=Sh. then the objective on is

Objective function=Tconv/ (max (Ss)

Sh)

So the optimization goal of QoS in case of services composition for composite service is described as follows: C(x) =F (a1 (n), a2 (n), …, a5 (n)) Here, C(x) is the composition of services, a1, a2,…a5 are the abstract services present in composite form and the value of a1 to a5 are increasing from minimum to maximum. Then the quality of each service is expressed as: fi(x) =maxSUM (Wj,a( n))≥ gj

Where, Wj is the weight given to the goal, a (n) is the user’s different QoS and gj be the goal of optimization. The quality of all the service links is used to provide the total quality is defined as fitness function which is expressed as, F(x)=SUM(fi(x)) , i=1 to n Phase-II Composite service selection derives the composition of existing as well as new services, for that the convergence is important. To calculate the velocity of getting the service selection from the existing methods to new value using the hill climbing algorithm. Thus the new route is defined as: R (t, i) =R (t−1, i) P (t, i) Where (t, i) is the variable replacement and R(t-1,i) is the existing route. The route quality value f (t, i) in the new route can be estimated, and the new route is reserved only when f (t, i) ≥f (t−1, i). The route quality value is defined as f(tm, i) ≥ f (tm−1, i), and tm≥tm−1. Thus, the new route calculated from the former route is better than the former route using number of iterations. And the robustness of selecting the route helps to calculate the new route. Thus the selection is performed with shorter time compared to genetic algorithm.

(IJEECS) International Journal of Electrical, Electronics and Computer Systems. Vol: 02 Issue: 02, June 2011

IV. EXPERIMENTAL ANALYSIS AND RESULTS: The implementation of memetic algorithm is done for composite service selection and the performance of memetic algorithm is compared with PSO and GA which are used in the existing papers related to web service processes like web service composition and selection. The parameters used in this paper for comparison are mean and the standard deviations and for testing we use the optimization test functions working for unimodal with multi-dimensional and multimodal with twodimensional (low and high local extremes) categories [15]. This paper takes 4 commonly used test functions comes under the above mentioned category such as Sphere, Rastrigin, Griewank and Ackley functions which are used to compare the performance of the three algorithms includes MA, GA and PSO. These functions have been tested using mathematica software (version5.2) why because this software provides graphical representation in 3D format and easy to handle the dimensionality features and the reason for taking test functions is it has the ability to compare the optimization of algorithms with other algorithms for the same problem based on the “No Free Lunch Theorem” by [15]. Comparatively MA performs better than the two algorithms through mean and standard deviation for almost all test function. PSO and GA provide better SD value for Rastrigin and Griewank function whereas overall comparison the MA satisfies SD in best. When taking the mean value for each function, PSO works well with Ackley function and GA provides the value of slight variation with MA. Thus the mean value comparison also shows the memetic algorithm gives better result than other two algorithms. TABLE II MULTIMODAL OPTIMIZATION TEST FUNCTIONS

Name

Sketch in 3D

Formula

f(x) = Griewank

–

f(x)= Ackley

The functions taken in this paper for testing the algorithm is highly multimodal shown in table-2. And multimodal functions are very tough to optimize, though these functions have been used for various combinatorial optimization problems for comparison with performance of other problems, provides the better result. Here, the Web service selection is considered as the optimization problem where the test functions having the features of multimodality are used to test the non functional parameters. MA parameters are applied in each test function; the result shows the sphere, Rastrigin, Griewank and Ackley in graphical format. A. Sphere function The values given for each run has been executed to minimize the sphere function with dimension of D= 2 to 30. Services arriving optimal point from initial point are noted. Fig 4 shows the variable number of local optima and single global optima. The services having higher fitness value are avoided and taken the least fitness value service which is the best service for selection. Table: 3 shows the parameter values of MA to this function includes amount of services taken for composition and comparison of services for selection. TABLE III MA PARAMETERS AND ITS VALUES FOR SPHERE FUNCTION

Sphere

f(x) =

f(x)=10n+

-

Rastrigin 10cos(2∏

Symbols Ns

Parameter set Initial no of services

Value 10

Rs

No. of iteration to select the service Dimension of problem Max no. of services taken for comparison Nonlinear modulation index Max no. of service taken to compose Initial & final value of SD Service discovery area

30, 40 & 50

D Ncs

)] I Nsc SD X&Y

2 to 30 50 2 3 to 5 4 & 0.01 -100 to 100

(IJEECS) International Journal of Electrical, Electronics and Computer Systems. Vol: 02 Issue: 02, June 2011

Fig: 5 Rastrigin function in 3D.

C. Griewank function Fig: 4 Sphere function in 3D.

B. Rastrigin function It is one of the challenging optimization test function and minimizing Rastrigin function is comparatively tough. Table:4 shows the MA parameter value applied in that function, it has number local optima and single global optima. Fitness value for this function ranges from 5 to 1.006. Thus final fitness value is taken as 1.006. Function evaluated with the dimension of D=2 shown in fig 5. TABLE IV MA PARAMETERS AND ITS VALUES FOR RASTRIGIN FUNCTION.

Symbols Ns

Parameter set Initial no of services

Value 10

Rs

No. of iteration to select the service Dimension of problem Max no. of services taken for comparison Nonlinear modulation index

40 & 50

Max no. of service taken to compose Initial & final value of SD Service discovery area

Griewank function is another harder multimodal test function. Optimizing this function is tough as Rastrigin function; it takes maximum number of iteration for convergence as shown in table. The fig: 6 shows the global minima at (0, 0) very clearly and better fitness value than GA and PSO. Table: 5 shows the parameters values used for Griewank function. TABLE V MA PARAMETERS AND ITS VALUE FOR GRIEWANK FUNCTION.

Symbols Ns

Parameter set Initial no of services

Value 10

Rs

No. of iteration to select the service Dimension of problem Max no. of services taken for comparison Nonlinear modulation index Max no. of service taken to compose Initial & final value of SD Service discovery area

50 & 60

D Ncs

D Ncs I Nsc SD X&Y

I 2 6

Nsc

2

SD

4

X&Y

5 & 0.006 -50 to 50

2 50 2 4 5 & 0.0136 -50 to 50

(IJEECS) International Journal of Electrical, Electronics and Computer Systems. Vol: 02 Issue: 02, June 2011 Ncs I Nsc SD X&Y

Functions used for mean calculation

GA

PSO

MA

Sphere

1.35x10^-7

2.46x 10^3

1.8x10^-8

Rastrigin

1.017

1.423

1.006

Griewank

0.023

.0316

0.0136

Ackley

1.097

1.6x10^-6

2x10^-5

Fig: 6 Griewank function in 3D.

D. Ackley function It belongs to the family of cosine function, the test functions having trigonometric variables are comparatively complex to optimize. It performs a non-constrained optimization, that it has the ability of taking large no of services for comparison and providing higher fitness with MA than GA but less than PSO. Fig-7 shows the global minimum value converge exactly at (0, 0) and less number of local minima. Table: 6 show the parameter of MA to Ackley function.

50

Max no. of service taken to compose Initial & final value of SD Service discovery area

5

2

7 & 0.00005 -100 to 100

Fig. 7 Ackley function in 3D.

The graphical representation of fitness function comparison of MA with PSO and GA shown in fig-4 that MA is better satisfying the selection process with user queries. Comparison of mean and standard deviation for three algorithms shown in table: 7 & 8. Algorithms having lesser values show that it gives better performance than the remaining algorithms taken for comparison. MA works well with four optimization test function which is multimodal and dimensional. PSO works well with Ackley function when considering the values of mean and standard deviation. GA gives better result for Griewank function than MA and PSO. TABLE VII MEAN VALUE COMPARISON OF THREE ALGORITHMS USING TEST FUNCTION TABLE VIII COMPARISON OF STANDARD DEVIATION OF THREE ALGORITHMS USING THE TEST FUNCTION

TABLE VI MA PARAMETERS AND ITS VALUE FOR ACKLEY FUNCTION.

Symbols Ns

Parameter set Initial no of services

Value 10

Rs

No. of iteration to select the service Dimension of problem

50, 60 & 70

D

Max no. of services taken for comparison Nonlinear modulation index

2

Functions used for standard deviation (SD) calculation

GA

PSO

MA

(IJEECS) International Journal of Electrical, Electronics and Computer Systems. Vol: 02 Issue: 02, June 2011 Sphere

2.38x10-3

3.76

1.2x10^-8

Rastrigin

1.03x 10-2

1.88x10^-1

0.0926

Griewank

0.019

2.031

2.47

Ackley

1.057

2.61x10^-6

2.127x10^-5

The comparison graph fig-4 shows the MA satisfies the nonfunctional attributes such QoS and time convergence factor since the fitness value reaches good when the QoS constrain is high and the time taken for selecting the service (time convergence) is low. Thus MA overcomes the disadvantages of GA and PSO in the sense GA fall in local minima and PSO takes much time for selection process. MA compared with existing algorithms which are used for web service selection and satisfies the user requirements 0.12 Fitness0.1

0.08 Value

GA

0.06

PSO

0.04

MA

0.02 0 10 20 30 40 50 60 100200 Number of Services

Fig.4 Fitness functions for GA, PSO and MA.

V. CONCLUSION AND FUTURE WORK The web service selection using MA satisfies the QoS (quality of Service) constraints and time convergence. The genetic algorithm is used as a part of memetic algorithm to achieve the QoS and the time convergence for service selection is achieved through Hill climbing algorithm which is used as a local search algorithm since it has the rapid local searching ability. The local search algorithm is applied to satisfy the following three criteria’s: For most of the practical problems, the exponential growth of the solution space is provided; Ambiguity of the model of the problem for being solved with exact algorithms; In design of algorithm, ease of use of problem specific knowledge than in design of classical optimisation methods for an specific problem. The QoS constraint attains by fitness calculation and the velocity of the route as well as new route derived to achieve the convergence factor. The QoS and convergence of the service selection provided by memetic

algorithm is compared with other bio-inspired algorithms like particle swarm optimization (PSO) and genetic algorithm (GA). The experimental results are provided using the optimization test functions of the three algorithms. From the theoretical analysis and experimental results, it shows that the proposed memetic algorithm provides optimal web service selection that supports to work in any distributed environment. The future work is to improve the cooperativeness of MA among the services to decrease further the time convergence parameter. REFERENCES [1] HU Chun-hua, CHEN Xiao-hong, LIANG Xi-ming,“Dynamic services selection algorithm in Web services composition supporting cross-enterprises collaboration”,Springer(2009) 16: 0269−0274 DOI: 10.1007/s11771−009−0046−y. [2] HaibinCai ,XiaohuiHu, Qingchong Lu¨ , QiyingCao”, A novel intelligent service selection algorithm and application for ubiquitous web services environment”, Expert Systems with Applications 36 (2009) 2200– 2212,elsevier. [3] Pablo Moscato, Carlos Cotta”, A Gentle Introduction to Memetic Algorithms”,2002. WWW: http://www.densis.fee.unicamp.br/»moscato [4] EmadElbeltagi, TarekHegazy, Donald Grierson,” Comparison among five evolutionary based optimization algorithms” Advanced Engineering Informatics 19 (2005) 43–53, Elsevier [5] ChengwenZhang ,Sen Su, Junliang Chen “DiGA: Population diversity handling genetic algorithm for QoS-aware web services selection” Beijing University of Posts and Telecommunications, Elsevier,2006. [6] Ping Wang ,Kuo-Ming Chao, Chi-Chun Lo,” On optimal decision for QoS-aware composite service selection”, Expert Systems with Applications 37 (2010)440–449,elsevier. [7] AngusF.M.Huang, Ci-WeiLan, Stephen. J.H.Yang, “An optimal QoSbased Web service selection scheme”, Journal of information Sciences, Elsevier, vol:179, Issue19, pp. 3309-3322, September,2009. [8] Silvia CasadoYusta “Different meta-heuristic strategies to solve the feature selection problem”, Elsevier, (2009)525-534. [9] Salvador García, José Ramón Cano, Francisco Herrera, “A memetic algorithm for evolutionary prototype selection: A scaling up approach”, Pattern Recognition 41 (2008) 2693 – 2709, Elsevier [10] J. Digalakis , K. Margaritis “Performance comparison of memetic algorithms”,Parallel and Distributed Processing Laboratory, University of Macedonia, Applied Mathematics and Computation , Elsevier,158 (2004) 237– 252 . [11] N. Baskara, P. Asokan , R. Saravanan, G. Prabhaharan,” Selection of optimal machining parameters for multi-tool milling operations using a memetic algorithm”, Journal of Materials Processing Technology 174 (2006) 239–249,Received 31 May 2004; accepted 16 September 2005. [12] Dirk Sudholt, “The impact of parameterization in memetic evolutionary algorithms”, Technische Universität Dortmund, Dortmund, Germany,2008. [13]Website:http://carbon.videolectures.net/2009/active/eswc09_hersonissos/kl usch_haws/eswc09_klusch_haws_01.pdf [14] NabilKeskes, Ahmed Lehireche and Abdellatif Rahmoun,”Web Services Selection Based on Context Ontology and Quality of Services”,International Arab Journal of e-Technology, Vol. 1, No. 3, January 2010. [15] S. He, Q. H. Wu, and J. R. Saunders,”Group Search Optimizer: An Optimization Algorithm Inspired by Animal searching Behavior”, IEEE transactions on evolutionary computation, vol. 13, no. 5, October 2009.