Title: Particle swarm optimisation by adding Gaussian disturbance item guided by hybrid narrow centre

Authors: Hui Sun; Zhicheng Deng; Jia Zhao; Haihua Xie

Addresses: School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China; Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang, 330099, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China; Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang, 330099, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China; Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang, 330099, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China; Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang, 330099, China

Abstract: This study proposed the optimised PSO algorithm after the addition of Gaussian disturbance guided by hybrid narrow centre. By combining narrow centre and improved narrow centre particles, the hybrid narrow centre can be constructed. In the updating formula of particle velocity, Gaussian disturbance item controlled by hybrid narrow centre is used for replacing self-cognition item. Owing to the guidance of hybrid narrow centre, the convergence is accelerated, while the introduction of Gaussian disturbance can prevent the particles from falling into local optimum. The combination of hybrid narrow centre and Gaussian disturbance can effectively avoid premature convergence and greatly increase convergence rate.

Keywords: particle swarm optimisation; PSO; narrow centre; Gaussian disturbance; self-cognition.

DOI: 10.1504/IJCSM.2020.10029258

International Journal of Computing Science and Mathematics, 2020 Vol.11 No.4, pp.327 - 337

Received: 19 Jul 2018
Accepted: 04 Sep 2018

Published online: 02 Jun 2020 *

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