Title: Parameter co-evolution mechanism of particle swarm optimisation algorithm

Authors: Ming Zhao; Xiaoyu Song; Yichen Gao

Addresses: Information and Control Engineering Faculty, Shenyang Jianzhu University, Shenyang, Liaoning Province, 110168, China ' Information and Control Engineering Faculty, Shenyang Jianzhu University, Shenyang, Liaoning Province, 110168, China ' Information Technology Department, Shenyang Gas Co., Ltd., Shenyang, Liaoning Province, 110005, China

Abstract: The running parameters are the important factors that influence the performance of PSO, and the optimisation of the selection and the adjustment strategy on them is one of the hot research directions. Based on the related research, this paper designs a co-evolution mechanism for the parameters of PSO including both the inertia weight and the acceleration factors, which defines stochastic evolution speed to reflect the current state of population evolution during the iterative process, and uses it as the feedback to set the inertia weight and the two acceleration factors. PSO with the parameter co-evolution mechanism can realise cooperative evolution of the running parameters with the population by dynamically adjusting parameter values according to population evolution state. Compared with five widely recognised parameter selection or adjustment strategies, on 20 numerical optimisation benchmark functions of different categories, the effectiveness and the efficiency of the proposed mechanism are verified.

Keywords: particle swarm optimisation; PSO; parameter co-evolution; inertia weight; acceleration factor; adjustment mechanism; stochastic evolution speed; population evolution; numerical optimisation; benchmark functions.

DOI: 10.1504/IJSPM.2020.107327

International Journal of Simulation and Process Modelling, 2020 Vol.15 No.3, pp.255 - 267

Received: 30 Jul 2018
Accepted: 06 Apr 2019
Published online: 15 May 2020 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article