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A Multi-Objective Particle Swarm Optimization for Web Service Composition

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Networked Digital Technologies (NDT 2010)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 88))

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Abstract

The main advantage of the web services technology is the possibility of preparing a compound web service with the existing to perform a proper task, but a service may be presented by several producers which one different in the quality of services. An adaptive process should select the elements of a compound web service in a way to answer effectively the user’s needs in the quality of the services. There may be contrast in the optimization of the services qualities for some of them and against the others so we are involved with a multi multi-objective optimization. In this paper a web service composition model based on the Discrete Multi-Objective Particle Swarm Optimization is presented at which besides using the main advantages of standard PSO namely simplicity and speed a Pareto optimal set is presented as solutions.

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References

  1. Rezgui, A., Ouzzani, M., Bouguettaya, A., Medjahed, B.: Preserving privacy in Web services. In: Proceedings of the 4th International ACM Workshop on Web Information and Data Management, pp. 56–62 (2002)

    Google Scholar 

  2. Medjahed, B., Bouguettaya, A., Elmagarmid, A.K.: Composing Web services on the Semantic Web. The VLDB Journal 12(4), 333–351 (2003)

    Article  Google Scholar 

  3. Lakhal, N.B., Kobayashi, T., Yokota, H.: THROWS: An architecture for highly available distributed execution of Web services compositions. In: Proceedings of the 14th International Workshop on Research Issues on Data Engineering: Web Services for E-Commerce and E-Government Applications, pp. 56–62 (2004)

    Google Scholar 

  4. Srivastava, B., Kohler, J.: Web Service Composition-Current Solutions and Open Problems. In: ICAPS 2003 Workshop on Planning for Web Services, pp. 1–8 (2003)

    Google Scholar 

  5. Agarwal, S., Handschuh, S., Staab, S.: Annotation, composition and invocation of semantic web services. In: Web Semantics: Science, Services and Agents on the World Wide Web, vol. 2(1), pp. 31–48 (2004)

    Google Scholar 

  6. Garey, M., Johnson, D.: Computers and Intractability: a Guide to the Theory of NP-Completeness. W.H. Freeman, New York (1979)

    MATH  Google Scholar 

  7. Zhang, C., Ma, Y.: Dynamic Genetic Algorithm for Search in Web Service Compositions Based on Global QoS Evaluations. In: ScalCom-EmbeddedCom, pp. 644–649 (2009)

    Google Scholar 

  8. Lei, Z., Kai, S.: TTS-Coded Genetic Algorithm for QoS-driven Web Service Selection. In: Proceedings of IEEE International Conference on Communication Technology and Applications, pp. 885–889 (2009)

    Google Scholar 

  9. Gao, C., Cai, M., Chen, H.: QoS-aware Service Composition based on Tree-Coded Genetic Algorithm. In: 31st Annual International Computer Software and Applications Conference (COMPSAC 2007), vol. 1, pp. 361–367 (2007)

    Google Scholar 

  10. Gao, Z., Chen, J., Qiu, X., Meng, L.: QoE/QoS driven simulated annealing-based genetic algorithm for Web services selection. The Journal of China Universities of Posts and Telecommunications 16, 102–107 (2009)

    Article  Google Scholar 

  11. Chen, M., Wang, Z.W.: An Approach for Web Services Composition Based on QoS and Discrete Particle Swarm Optimization. In: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, pp. 37–41 (2007)

    Google Scholar 

  12. Ngatchou, P., Zarei, A., El-Sharkawi, A.: Pareto Multi Objective Optimization. Intelligent Systems Application to Power Systems, 84–91 (2005)

    Google Scholar 

  13. Coello, C.A.C.: A comprehensive survey of evolutionary-based multiobjective optimization. Knowledge and Information Systems 1(3), 269–308 (1999)

    Google Scholar 

  14. Deb, K.: Evolutionary Algorithms for Multi-Criterion Optimization in Engineering Design. In: Proceedings of Evolutionary Algorithms in Engineering and Computer Science (EUROGEN 1999), pp. 135–161 (1999)

    Google Scholar 

  15. Kennedy, J., Ebenhart, R.C.: Particle Swarm Optimization. Proceedings of the IEEE International Conference on Neural Networks 4, 1942–1948 (1995)

    Article  Google Scholar 

  16. Engelbrecht, P.: Computational Intelligence An introduction. John Wiley & Sons Ltd., Chichester (2002)

    Google Scholar 

  17. Kennedy, J.: The behavior of particle swarm. In: Saravan, V.W.N., Waagen, D. (eds.) Proceedings of the 7th International Conference on Evolutionary Programming, pp. 581–589 (1998)

    Google Scholar 

  18. Mostaghim, S., Teich, J.: Strategies for finding good local guides in multi-objective particle swarm optimization. In: IEEE Swarm Intelligence Symposium, pp. 26–33 (2003)

    Google Scholar 

  19. van der Aaslt, W.M.P., Hofstede, A.H.M.: YAWL: Yet Another Workflow Language. Information Systems 30(4), 245–275

    Google Scholar 

  20. Qinma, K., Hong, H., Hongrun, W., Changjun, J.: A novel discrete particle swarm optimization algorithm for job scheduling in grids. In: Proceedings of the 4th International ACM Workshop Conference on Natural Computation, ICNC, pp. 401–405 (2008)

    Google Scholar 

  21. Zitzler, E., Thiele, L.: An Evolutionary Algorithm for Multiobjective optimization: The Strength Pareto Approach. TIK-Report, No. 43, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich (1998)

    Google Scholar 

  22. Torn, A.: A program for global optimization. In: Proceedings of Euro IFIP, pp. 427–434 (1979)

    Google Scholar 

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Rezaie, H., NematBaksh, N., Mardukhi, F. (2010). A Multi-Objective Particle Swarm Optimization for Web Service Composition. In: Zavoral, F., Yaghob, J., Pichappan, P., El-Qawasmeh, E. (eds) Networked Digital Technologies. NDT 2010. Communications in Computer and Information Science, vol 88. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14306-9_12

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  • DOI: https://doi.org/10.1007/978-3-642-14306-9_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14305-2

  • Online ISBN: 978-3-642-14306-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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