Title: Solving high dimensional multimodal continuous optimisation problems using hybridisation between particle swarm optimisation variants

Authors: Hugo Deschenes; Caroline Gagne

Addresses: Department of Computer Science and Mathematics, Université du Québec à Chicoutimi, 555, boulevard de l'Université, Chicoutimi, QC, CA, G7H 2B1, Canada ' Department of Computer Science and Mathematics, Université du Québec à Chicoutimi, 555, boulevard de l'Université, Chicoutimi, QC, CA, G7H 2B1, Canada

Abstract: This paper presents a comparison between three new hybridisations using three particle swarm optimisation (PSO) variants: The Barebones PSO (BPSO), the comprehensive learning PSO (CLPSO) and the cooperative learning PSO (CoLPSO). The goal of these hybridisations is to improve the exploration and the exploitation of the search space from these three variants and contributes to PSO on high scale continuous optimisation problems. The performance of these three new hybrids, named HCLBPSO-Half, HBPSO+CL and HCoCLPSO, are compared with the original methods on which they are based. The comparison is done using six classical continuous optimisation functions with dimensions set to 50, 100 and 200, and all 15 continuous optimisation functions from the CEC'15 benchmark with dimensions set to 10, 30, 50 and 100. The results are compared using the mean and median of executions.

Keywords: metaheuristics; continuous optimisation; particle swarm optimisation; PSO; hybridisation; variants; high dimensional problems.

DOI: 10.1504/IJMHEUR.2020.107390

International Journal of Metaheuristics, 2020 Vol.7 No.3, pp.239 - 264

Received: 27 Jul 2018
Accepted: 15 May 2019
Published online: 26 May 2020 *