title: A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm.
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.
authors: Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.
authors: Mohammad Javad
authors: http://orcid.org/0000-0002-2257-7607
authors: MJ
authors: Amoshahy
authors: Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.
authors: Mousa
authors: M
authors: Shamsi
authors: Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.
authors: Mohammad Hossein
authors: MH
authors: Sedaaghi
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology; D003198:Computer Simulation; D015233:Models, Statistical; D012984:Software; D013997:Time Factors