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A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm

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  • Mohammad Javad Amoshahy
  • Mousa Shamsi
  • Mohammad Hossein Sedaaghi

Abstract

Particle swarm optimization (PSO) is an evolutionary computing method based on intelligent collective behavior of some animals. It is easy to implement and there are few parameters to adjust. The performance of PSO algorithm depends greatly on the appropriate parameter selection strategies for fine tuning its parameters. Inertia weight (IW) is one of PSO’s parameters used to bring about a balance between the exploration and exploitation characteristics of PSO. This paper proposes a new nonlinear strategy for selecting inertia weight which is named Flexible Exponential Inertia Weight (FEIW) strategy because according to each problem we can construct an increasing or decreasing inertia weight strategy with suitable parameters selection. The efficacy and efficiency of PSO algorithm with FEIW strategy (FEPSO) is validated on a suite of benchmark problems with different dimensions. Also FEIW is compared with best time-varying, adaptive, constant and random inertia weights. Experimental results and statistical analysis prove that FEIW improves the search performance in terms of solution quality as well as convergence rate.

Suggested Citation

  • Mohammad Javad Amoshahy & Mousa Shamsi & Mohammad Hossein Sedaaghi, 2016. "A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-42, August.
    • Handle: RePEc:plo:pone00:0161558
    DOI: 10.1371/journal.pone.0161558
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    References listed on IDEAS

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    1. Mohd Nadhir Ab Wahab & Samia Nefti-Meziani & Adham Atyabi, 2015. "A Comprehensive Review of Swarm Optimization Algorithms," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-36, May.
    2. Chen Liu & Wen-Bo Du & Wen-Xu Wang, 2014. "Particle Swarm Optimization with Scale-Free Interactions," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-8, May.
    3. Jiao, Bin & Lian, Zhigang & Gu, Xingsheng, 2008. "A dynamic inertia weight particle swarm optimization algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 37(3), pages 698-705.
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