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A dynamic global and local combined particle swarm optimization algorithm

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  • Jiao, Bin
  • Lian, Zhigang
  • Chen, Qunxian

Abstract

Particle swarm optimization (PSO) algorithm has been developing rapidly and many results have been reported. PSO algorithm has shown some important advantages by providing high speed of convergence in specific problems, but it has a tendency to get stuck in a near optimal solution and one may find it difficult to improve solution accuracy by fine tuning. This paper presents a dynamic global and local combined particle swarm optimization (DGLCPSO) algorithm to improve the performance of original PSO, in which all particles dynamically share the best information of the local particle, global particle and group particles. It is tested with a set of eight benchmark functions with different dimensions and compared with original PSO. Experimental results indicate that the DGLCPSO algorithm improves the search performance on the benchmark functions significantly, and shows the effectiveness of the algorithm to solve optimization problems.

Suggested Citation

  • Jiao, Bin & Lian, Zhigang & Chen, Qunxian, 2009. "A dynamic global and local combined particle swarm optimization algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 42(5), pages 2688-2695.
    • Handle: RePEc:eee:chsofr:v:42:y:2009:i:5:p:2688-2695
    DOI: 10.1016/j.chaos.2009.03.175
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    References listed on IDEAS

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    1. Liu, Bo & Wang, Ling & Jin, Yi-Hui & Tang, Fang & Huang, De-Xian, 2005. "Improved particle swarm optimization combined with chaos," Chaos, Solitons & Fractals, Elsevier, vol. 25(5), pages 1261-1271.
    2. Coelho, Leandro dos Santos, 2008. "A quantum particle swarm optimizer with chaotic mutation operator," Chaos, Solitons & Fractals, Elsevier, vol. 37(5), pages 1409-1418.
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