Web Service Composition Research Based On Ant Colony Optimization

With the continuous development of Internet and cloud computing technology,the development of Web service brings new experience to enterprises and users.However,the demand for users to obtain multiple services is growing.In the cloud computing environment,the complex needs of users for Web can be realized by combining the single Web service in the resource pool in some effective way.Quality of service can be evaluated from the functional level,so it is widely used as an evaluation index of service composition.However,the index can not reflect the subjective feelings of users.Quality of experience(QoE)is based on the user satisfaction as the evaluation standard,which can directly reflect the subjective satisfaction of users to the service.This paper mainly studies the application of ant colony optimization to solve the Web service composition problem based on QoE in cloud computing environment.The main work of this paper are as follows:(1)On the basis of fuzzy expert system,this paper studies a Web service composition evaluation model based on QoE,which quantifies the subjective satisfaction of users to the service as an accurate QoE score.According to the result of subjective test,the membership function of the fuzzy expert system is derived by using probability theory.The inference rules of the fuzzy expert system can be obtained by applying the fuzzy set theory to the result of subjective test,and finally the QoE evaluation system of web service can be obtained.Through fuzzy expert system,the QoE is calculated according to the membership function and inference rules;According to the basic topology of Web service,the QoE evaluation model of Web service composition is established.(2)Aiming at the shortcomings of ant colony optimization(ACO),three improved strategies are proposed,and an adaptive chaotic flying ant colony optimization(CSFACO)based on exchange mechanism is obtained.This paper analyzes the basic principle of ACO,introduces chaos mechanism to form adaptive chaos ant colony optimization(ACACO).Chaos initialization can accelerate the convergence speed of the algorithm,and adaptive chaos disturbance can enhance the search ability of ant colony and improve the solution accuracy of the algorithm;On the basis of ACACO,exchange mechanism and special flying ants are introduced to form CSFACO.Exchange mechanism can integrate multiple optimal solutions to help the algorithm get better solutions;Special flying ants can increase the chance for ants to explore other paths,so as to avoid the algorithm falling into local optimum;The benchmark function is used to test the CSFACO and verify its superior performance in optimization problems.(3)The CSFACO is used to solve the Web service composition optimization problem,which proves that CSFACO is better in the service composition problem.This paper studies the specific process of the CSFACO to solve the Web service composition problem based on QoE,and verifies the success rate of the algorithm through a small-scale service composition problem;By comparing CSFACO with ACACO,ACO,particle swarm optimization,differential evolution algorithm and imperialist competitive algorithm,the experimental results show that CSFACO algorithm has better effectiveness and robustness,faster convergence speed and higher convergence accuracy,more stable performance,but the execution time of the algorithm is slightly longer.