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Particle Swarm Optimization algorithm with multiple social learning structures

Author

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  • Pisut Pongchairerks
  • Voratas Kachitvichyanukul

Abstract

This paper proposes a variant of Particle Swarm Optimisation (PSO) algorithm which enhances the social learning structure of the standard PSO by incorporating multiple social best positions. The research in this paper analyses the effects of main parameters on the proposed algorithm's performance by using factorial experiment. To verify the research findings, this paper compares the proposed algorithm's performance to those of several well-known PSO algorithms. Eventually, the comparison results indicate that the proposed algorithm outperforms others.

Suggested Citation

  • Pisut Pongchairerks & Voratas Kachitvichyanukul, 2009. "Particle Swarm Optimization algorithm with multiple social learning structures," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 6(2), pages 176-194.
    • Handle: RePEc:ids:ijores:v:6:y:2009:i:2:p:176-194
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    Citations

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    Cited by:

    1. D. G. Mogale & Sri Krishna Kumar & Manoj Kumar Tiwari, 2020. "Green food supply chain design considering risk and post-harvest losses: a case study," Annals of Operations Research, Springer, vol. 295(1), pages 257-284, December.
    2. Neungmatcha, Woraya, 2016. "Multi-objective particle swarm optimization for mechanical harvester route planning of sugarcane field operationsAuthor-Name: Sethanan, Kanchana," European Journal of Operational Research, Elsevier, vol. 252(3), pages 969-984.
    3. Vincent F. Yu & Parida Jewpanya & Voratas Kachitvichyanukul, 2016. "Particle swarm optimization for the multi-period cross-docking distribution problem with time windows," International Journal of Production Research, Taylor & Francis Journals, vol. 54(2), pages 509-525, January.

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