Solving high dimensional multimodal continuous optimisation problems using hybridisation between particle swarm optimisation variants. (21st May 2020)

• Record Type: Journal Article
• Authors: Deschenes, Hugo; Gagne, Caroline
• Is Part Of: International journal of metaheuristics. Volume 7:Number 3(2019)
• Page Start: 239
• Page End: 264
• Publication Date: 2020-05-21
• Subjects: metaheuristics -- continuous optimisation -- particle swarm optimisation -- PSO -- hybridisation -- variants -- high dimensional problems; Heuristic algorithms -- Periodicals; 006.3105
• Languages: English
• ISSNs: 1755-2176

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.