A Genetic Algorithm with Improved Convergence Capability for QoS-Aware Web Service Selection

Genetic Algorithm (GA) is a good service selection algorithm to select an optimal composite plan from many composite plans. Since the execution of GA relies on a randomly search procedure to seek possible solutions, bad convergence of GA is produced by random sequences generation. To improve the convergence of GA for web service selection with global Qualityof-Service (QoS) constraints, chaos theory is introduced into GA with the relation matrix coding scheme. The chaotic law is based on the relation matrix coding scheme. During crossover process phase, chaotic time series are adopted instead of random ones. The effect of chaotic sequences and random ones is compared during several numerical tests. The performance of GA using chaotic time series and random ones is investigated. The simulation results on web service selection with global QoS constraints have shown that the proposed strategy based on chaotic sequences can enhance GA’s convergence capability. Keywords-web service selection; QoS-aware; genetic algorithm; convergence; chaotic sequence

[1]  Tian Chao,et al.  On demand Web services-based business process composition , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[2]  James A. Foster,et al.  Using chaos in genetic algorithms , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[3]  Junliang Chen,et al.  A Novel Genetic Algorithm for QoS-Aware Web Services Selection , 2006, DEECS.

[4]  Yuhong Yan,et al.  Using Genetic Algorithms to Navigate Partial Enumerable Problem Space for Web Services Composition , 2007, Third International Conference on Natural Computation (ICNC 2007).

[5]  Liu Jianqin,et al.  Premature convergence in genetic algorithm: analysis and prevention based on chaos operator , 2000, Proceedings of the 3rd World Congress on Intelligent Control and Automation (Cat. No.00EX393).

[6]  Tao Yu,et al.  Efficient algorithms for Web services selection with end-to-end QoS constraints , 2007, TWEB.

[7]  Thomas Stützle,et al.  MAX-MIN Ant System , 2000, Future Gener. Comput. Syst..

[8]  Maria Luisa Villani,et al.  An approach for QoS-aware service composition based on genetic algorithms , 2005, GECCO '05.

[9]  Junliang Chen,et al.  Efficient Population Diversity Handling Genetic Algorithm for QoS-Aware Web Services Selection , 2006, International Conference on Computational Science.

[10]  Luigi Fortuna,et al.  Does chaos work better than noise , 2002 .

[11]  Leon O. Chua,et al.  Practical Numerical Algorithms for Chaotic Systems , 1989 .

[12]  Anne H. H. Ngu,et al.  QoS-aware middleware for Web services composition , 2004, IEEE Transactions on Software Engineering.

[13]  W. Freeman,et al.  How brains make chaos in order to make sense of the world , 1987, Behavioral and Brain Sciences.

[14]  Tao Jiang,et al.  Combine automatic and manual process on web service selection and composition to support QoS , 2008, 2008 12th International Conference on Computer Supported Cooperative Work in Design.

[15]  Luigi Fortuna,et al.  Chaotic sequences to improve the performance of evolutionary algorithms , 2003, IEEE Trans. Evol. Comput..

[16]  Lalit M. Patnaik,et al.  Genetic algorithms: a survey , 1994, Computer.

[17]  Junliang Chen,et al.  DiGA: Population diversity handling genetic algorithm for QoS-aware web services selection , 2007, Comput. Commun..

[18]  Maria Luisa Villani,et al.  A Lightweight Approach for QoS–Aware Service Composition , 2006 .