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An Enhanced Partial Search to Particle Swarm Optimization for Unconstrained Optimization

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  • Shu-Kai S. Fan

    (Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei City 10608, Taiwan)

  • Chih-Hung Jen

    (Department of Information Management, Lunghwa University of Science and Technology, Guishan, Taoyuan County 33306, Taiwan)

Abstract

Particle swarm optimization (PSO) is a population-based optimization technique that has been applied extensively to a wide range of engineering problems. This paper proposes a variation of the original PSO algorithm for unconstrained optimization, dubbed the enhanced partial search particle swarm optimizer (EPS-PSO), using the idea of cooperative multiple swarms in an attempt to improve the convergence and efficiency of the original PSO algorithm. The cooperative searching strategy is particularly devised to prevent the particles from being trapped into the local optimal solutions and tries to locate the global optimal solution efficiently. The effectiveness of the proposed algorithm is verified through the simulation study where the EPS-PSO algorithm is compared to a variety of exiting “cooperative” PSO algorithms in terms of noted benchmark functions.

Suggested Citation

  • Shu-Kai S. Fan & Chih-Hung Jen, 2019. "An Enhanced Partial Search to Particle Swarm Optimization for Unconstrained Optimization," Mathematics, MDPI, vol. 7(4), pages 1-16, April.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:4:p:357-:d:223587
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    References listed on IDEAS

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    1. Fan, Shu-Kai S. & Zahara, Erwie, 2007. "A hybrid simplex search and particle swarm optimization for unconstrained optimization," European Journal of Operational Research, Elsevier, vol. 181(2), pages 527-548, September.
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