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Particle swarm optimization service composition algorithm based on prior knowledge

Author

Listed:
  • Hongbin Wang

    (Kunming University of Science and Technology
    Kunming University of Science and Technology)

  • Yang Ding

    (Kunming University of Science and Technology
    Kunming University of Science and Technology)

  • Hanchuan Xu

    (Harbin Institute of Technology)

Abstract

In order to quickly find an appropriate composition of services that meet the individual user’s requirements in the Internet big data, this paper proposes an improved particle swarm service composition method based on prior knowledge. This method firstly mines the service composition partial segments with certain frequencies of usage from a large number of historical service composition solutions, i.e. the service pattern. While receiving the user’s service composition requirement, this method uses the service pattern matching algorithm proposed in this paper to match the corresponding service patterns as a partial solution of this composition requirement. Then the method proposes an improved particle swarm algorithm for the part that do not successfully match the corresponding service patterns. This improved particle swarm algorithm has a mechanism to escape from the local optima. Finally, the method integrates the partial solutions of the two aspects into a complete solution, i.e. a complete service composition solution. This paper compares the optimality, time complexity and convergence with other related service composition optimization algorithms through simulation experiments. According to the analysis of the experimental results, the method proposed in this paper shows good performance in three aspects: optimality, time complexity and convergence.

Suggested Citation

  • Hongbin Wang & Yang Ding & Hanchuan Xu, 2024. "Particle swarm optimization service composition algorithm based on prior knowledge," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 35-53, January.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:1:d:10.1007_s10845-022-02032-w
    DOI: 10.1007/s10845-022-02032-w
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    References listed on IDEAS

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    1. Jiajun Zhou & Xifan Yao, 2017. "A hybrid approach combining modified artificial bee colony and cuckoo search algorithms for multi-objective cloud manufacturing service composition," International Journal of Production Research, Taylor & Francis Journals, vol. 55(16), pages 4765-4784, August.
    2. Çağla Cergibozan & A. Serdar Tasan, 2022. "Genetic algorithm based approaches to solve the order batching problem and a case study in a distribution center," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 137-149, January.
    3. Ruilin Liu & Zhongjie Wang & Xiaofei Xu, 2019. "Parameter Tuning for S-ABCPK: An Improved Service Composition Algorithm Considering Priori Knowledge," International Journal of Web Services Research (IJWSR), IGI Global, vol. 16(2), pages 88-109, April.
    4. Tianyang Li & Ting He & Zhongjie Wang & Yufeng Zhang, 2020. "SDF-GA: a service domain feature-oriented approach for manufacturing cloud service composition," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 681-702, March.
    5. Shuai Zhang & Yangbing Xu & Wenyu Zhang & Dejian Yu, 2019. "A new fuzzy QoS-aware manufacture service composition method using extended flower pollination algorithm," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2069-2083, June.
    6. Khadijeh Momeni, 2021. "Service Integration: Supply Chain Integration in Servitization," Springer Books, in: Marko Kohtamäki & Tim Baines & Rodrigo Rabetino & Ali Ziaee Bigdeli & Christian Kowalkowski & Rogeli (ed.), The Palgrave Handbook of Servitization, pages 471-485, Springer.
    7. Yankai Wang & Shilong Wang & Bo Yang & Bo Gao & Sibao Wang, 2022. "An effective adaptive adjustment method for service composition exception handling in cloud manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 735-751, March.
    8. Fateh Seghir & Abdellah Khababa, 2018. "A hybrid approach using genetic and fruit fly optimization algorithms for QoS-aware cloud service composition," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1773-1792, December.
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