Authors: Guohan Lin; Jing Sun
Addresses: Hunan Province Cooperative Innovation Center for Wind Power Equipment and Energy Conversion, Hunan Institute of Engineering, Xiangtan 411104, Hunan, China ' College of Electrical and Information, Hunan Institute of Engineering, Xiangtan 411104, Hunan, China
Abstract: To ease the conflict between diversity and convergence rate encountered by Particle Swarm Optimisation (PSO), a multi-strategy learning PSO Algorithm (Multi-strategy Learning PSO, MSLPSO) is proposed. The proposed method can effectively preserve the heuristic information; a modified differential mutation is combined with PSO to expand search range and to increase the diversity of the population. The inferior particle adopts opposition-based learning when the population was trapped into local optimum. This mechanism can improve the diversity and can help the particles' flight away from the local optimum. Gaussian disturbance is applied to elite particles to further improve the diversity of particles. Twelve benchmark function tests from CEC2005 are used to evaluate the performance of the proposed algorithm. The results show that the proposed multi-strategy learning has performed consistently well compared to other state-of-art PSO algorithms.
Keywords: PSO; particle swarm optimisation; learning strategy; differential mutation; perturbation strategy; numerical optimisation.
International Journal of Wireless and Mobile Computing, 2020 Vol.18 No.1, pp.22 - 30
Received: 17 Oct 2018
Accepted: 07 May 2019
Published online: 28 Jan 2020 *