Title: Population diversity of particle swarm optimisation algorithms for solving multimodal optimisation problems

Authors: Shi Cheng; Junfeng Chen; Quande Qin; Yuhui Shi

Addresses: Division of Computer Science, University of Nottingham Ningbo China, Ningbo, Zhejiang, China ' College of IOT Engineering, Hohai University, Changzhou, China ' Department of Management Science, Shenzhen University, Shenzhen, China ' Department of Electrical and Electronic Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, China

Abstract: The aim of multimodal optimisation is to locate multiple peaks/optima in a single run and to maintain these found optima until the end of a run. In this paper, seven variants of particle swarm optimisation (PSO) algorithms are utilised to solve multimodal optimisation problems. The position diversity is utilised to measure the candidate solutions during the search process. Our goal is to measure the performance and effectiveness of variants of PSO algorithms and investigate why an algorithm performs effectively from the perspective of population diversity. Based on the experimental results, the conclusions could be made that the PSO with ring structure and social-only PSO with ring structure perform better than the other PSO variants on multimodal optimisation. From the population diversity measurement, it is shown that to obtain good performances on multimodal optimisation problems, an algorithm needs to balance its global search ability and solutions maintenance ability.

Keywords: swarm intelligence algorithm; multimodal optimisation; particle swarm optimisation; PSO; population diversity; nonlinear equation systems.

DOI: 10.1504/IJCSE.2018.094419

International Journal of Computational Science and Engineering, 2018 Vol.17 No.1, pp.69 - 79

Received: 07 Sep 2015
Accepted: 05 Apr 2016
Published online: 03 Sep 2018 *

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