A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm

• Published in: PloS one 2016-08, Vol.11 (8), p.e0161558-e0161558
• Main Authors: Amoshahy, Mohammad Javad, Shamsi, Mousa, Sedaaghi, Mohammad Hossein
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Artificial Intelligence; Biology and Life Sciences; Computational Biology; Computer and Information Sciences; Computer Simulation; Electrical engineering; Evolutionary algorithms; Exploitation; Exploration; Inertia; International conferences; Models, Statistical; Optimization; Optimization algorithms; Optimization theory; Particle swarm optimization; Physical Sciences; Problems; Research and Analysis Methods; S parameters; Social Sciences; Software; Statistical analysis; Strategy; Swarm intelligence; Time Factors
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0161558

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.