A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm

Mohammad Javad Amoshahy, Mousa Shamsi and Mohammad Hossein Sedaaghi

PLOS ONE, 2016, vol. 11, issue 8, 1-42

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.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0161558

DOI: 10.1371/journal.pone.0161558

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