Cuckoo Search and Firefly Algorithm pp 217-243

Part of the Studies in Computational Intelligence book series (SCI, volume 516) | Cite as

Hybridization of Cuckoo Search and Firefly Algorithms for Selecting the Optimal Solution in Semantic Web Service Composition

Chapter

Abstract

This chapter investigates how the Cuckoo Search and Firefly Algorithm can be hybridized for performance improvement in the context of selecting the optimal or near-optimal solution in semantic Web service composition. Cuckoo Search and Firefly Algorithm are hybridized with genetic, reinforcement learning and tabu principles to achieve a proper exploration and exploitation of the search process. The hybrid algorithms are applied on an enhanced planning graph which models the service composition search space for a given user request. The problem of finding the optimal solution encoded in the enhanced planning graph can be reduced to identifying a configuration of semantic Web services, out of a very large set of possible configurations, which maximizes a fitness function which considers semantics and QoS attributes as selection criteria. To analyze the benefits of hybridization we have comparatively evaluated the classical Cuckoo Search and Firefly Algorithms versus the proposed hybridized algorithms.

Keywords

Cuckoo search algorithm Firefly algorithm hybrid nature-inspired algorithm Optimal or near-optimal Web service composition 

References

  1. 1.
    Bahadori, S., Kafi, S., Far, K.Z., Khayyambashi, M.R.: Optimal Web service composition using hybrid GA-TABU search. J. Theor. Appl. Inf. Technol. 9(1), 10–15 (2009)Google Scholar
  2. 2.
    Batouche, B., Naudet, Y., Guinand, F.: Semantic web services composition optimized by multi-objective evolutionary algorithms. In: Proceedings of the 2010 Fifth International Conference on Internet and Web Applications and Services, pp. 180–185 (2010)Google Scholar
  3. 3.
    Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. 35(3), 268–308 (2003)CrossRefGoogle Scholar
  4. 4.
    Blum, C., Puchinger, J., Raidl, G.R., Roli, A.: Hybrid metaheuristics in combinatorial optimization: a survey. Appl. Soft Comput. J. 11(6), 4135–4151 (2011)CrossRefGoogle Scholar
  5. 5.
    Canfora, G., Penta, M., Di Esposito, R., Villani, M.L.: An approach for QoS-aware service composition based on genetic algorithms. In: Proceedings of the 2005 Conference on Genetic and, Evolutionary Computation, pp. 1069–1075 (2005)Google Scholar
  6. 6.
    Canfora, G., Di Penta, M., Esposito, R., Villani, M.L.: A framework for QoS-aware binding and re-binding of composite web services. J. Syst. Softw. 81(10), 1754–1769 (2008)CrossRefGoogle Scholar
  7. 7.
    Crepinsek, M., Liu, S., Mernik, M.: Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput. Surv. 45(3), pp. 35 (2013)Google Scholar
  8. 8.
    Fan, X., Fang, X.: On optimal decision for QoS-aware composite service selection. Inf. Technol. J. 9(6), 1207–1211 (2010)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Glover, F., Laguna, M.: Tabu search. Kluwer Academic Publishers, Norwell, MA, USA (1997)Google Scholar
  10. 10.
    Jaeger, M.C., Muhl G.: QoS-based selection of services: the implementation of a genetic algorithm. In: Proceedings of the 2007 ITG-GI Conference on Communication in Distributed Systems, pp. 1–12 (2007)Google Scholar
  11. 11.
    Jiang, H., Yang, X., Yin, K., Zhang, S., Cristoforo, J.A.: Multi-path QoS-aware web service composition using variable length chromosome genetic algorithm. Inf. Technol. J. 10, 113–119 (2011)CrossRefGoogle Scholar
  12. 12.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization, pp. 1942–1948. In: Proceedings of IEEE International Conference on Neural Networks (1995)Google Scholar
  13. 13.
    Ko, J.M., Kim, C.O., Kwon, I.H.: Quality-of-service oriented web service composition algorithm and planning architecture. J. Syst. Softw. 81(11), 2079–2090 (2008)CrossRefGoogle Scholar
  14. 14.
    Lecue, F.: Optimizing QoS-aware semantic web service composition. In: Proceedings of the 8th International Semantic Web Conference, pp. 375–391 (2009)Google Scholar
  15. 15.
    Li, W., Yan-xiang, H.: Web service composition algorithm based on Global QoS optimizing with MOCACO. In: Proceedings of the 10th International Conference on Algorithms and Architectures for Parallel Processing, Lecture Notes in Computer Science 6082/2010, pp. 218–224 (2010)Google Scholar
  16. 16.
    Liu, H., Zhong, F., Ouyang, B., Wu, J.: An approach for QoS-aware web service composition based on improved genetic algorithm. In: Proceedings of the 2010 International Conference on Web Information Systems and Mining, pp. 123–128 (2010)Google Scholar
  17. 17.
    Ming, C., Zhen-wu, 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
  18. 18.
    Pop, C.B., Chifu, V.R., Salomie, I., Dinsoreanu, M.: Immune-inspired method for selecting the optimal solution in web service composition. In: Resource Discovery, Lecture Notes in Computer Science vol. 6162, pp. 1–17 (2010)Google Scholar
  19. 19.
    Salomie, I., Cioara, T., Anghel, I., Salomie, T.: Distributed computing and systems. Albastra Publishing House, Cluj-Napoca, Romania (2008)Google Scholar
  20. 20.
    Tang, M., Ai, L.: A hybrid genetic algorithm for the optimal constrained web service selection problem in web service composition. In: Proceedings of the 2010 World Congress on, Computational Intelligence, pp. 1–8 (2010)Google Scholar
  21. 21.
    Vanrompay, Y., Rigole, P., Berbers, Y.: Genetic algorithm-based optimization of service composition and deployment. In: Proceedings of the 3rd International Workshop on Services Integration in Pervasive, Environments, pp. 13–18 (2008)Google Scholar
  22. 22.
    Wang, J., Hou, Y.: Optimal web service selection based on multi-objective genetic algorithm. In: Proceedings of the International Symposium on Computational Intelligence and Design, pp. 553–556 (2008)Google Scholar
  23. 23.
    Wang, X.L., Jing, Z., Yang, H.: Service selection constraint model and optimization algorithm for web service composition. Inf. Technol. J. 10, 1024–1030 (2011)Google Scholar
  24. 24.
    Wang, W., Sun, Q., Zhao, X., Yang, F.: An improved particle swarm optimization algorithm for QoS-aware web service selection in service oriented communication. Int. J. Comput. Intell. Syst. 3(1), 18–30 (2010)Google Scholar
  25. 25.
    Xu, J., Reiff-Marganiec, S.: Towards heuristic web services composition using immune algorithm. In: Proceedings of the International Conference on Web Services, pp. 238–245 (2008)Google Scholar
  26. 26.
    Yan, G., Jun, N., Bin, Z., Lei, Y., Qiang, G., Yu, D.: Immune algorithm for selecting optimum services in web services composition. Wuhan Univ. J. Nat. Sci. 11, 221–225 (2006)CrossRefMATHGoogle Scholar
  27. 27.
    Yang, X.-S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, Frome, United Kingdom (2008)Google Scholar
  28. 28.
    Yang, X.S., Deb, S.: Cuckoo Search via Levy flights. In: Proceedings of the World Congress on Nature and Biologically Inspired, Computing, pp. 210–214 (2009)Google Scholar
  29. 29.
    Yang, X.-S.: Engineering Optimization: An Introduction with Metaheuristic Applications. Wiley, Hoboken, USA (2010)CrossRefGoogle Scholar
  30. 30.
    Yang, X.S., Cui, Z., Xiao, R., Gandomi, A.H., Karamanoglu M.: Swarm Intelligence and Bio-inspired Computation: Theory and Applications. Elsevier, Amsterdam, The Netherlands (2013)Google Scholar
  31. 31.
    Zhang, W., Chang, C.K., Feng T., Jiang, H.: QoS-based dynamic web service composition with ant colony optimization. In: Proceedings of the 34th Annual Computer Software and Applications Conference, pp. 493–502 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Computer Science DepartmentTechnical University of Cluj-NapocaCluj-NapocaRomania

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