MOCPSO: A multi-objective cooperative particle swarm optimization algorithm with dual search strategies

• Published in: Neurocomputing (Amsterdam) Vol. 562; p. 126892
• Main Authors: Zhang, Yan, Li, Bingdong, Hong, Wenjing, Zhou, Aimin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 28.12.2023
• Subjects: Evolutionary algorithm; Large-scale multi-objective optimization; Meta-heuristics; Particle swarm optimization; Large-scale multi-objective optimization; Particle swarm optimization; Evolutionary algorithm; Meta-heuristics
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2023.126892

Particle swarm optimization (PSO) is a widely embraced meta-heuristic approach to tackling the complexities of multi-objective optimization problems (MOPs), renowned for its simplicity and swift convergence. However, when faced with large-scale multi-objective optimization problems (LSMOPs), most PSOs suffer from limited local search capabilities and insufficient randomness. This can result in suboptimal results, particularly in high-dimensional spaces. To address these issues, this paper introduces MOCPSO, a Multi-Objective Cooperative Particle Swarm Optimization Algorithm with Dual Search Strategies. MOCPSO incorporates a diversity search strategy (DSS) to augment perturbation and enhance the local search scope of particles, alongside a more convergent search strategy (CSS) to expedite particle convergence. Moreover, MOCPSO utilizes a three-category framework to effectively leverage the benefits of both DSS and CSS. Experimental results on benchmark LSMOPs with 500, 1000, and 2000 decision variables demonstrate that MOCPSO outperforms existing state-of-the-art large-scale multi-objective evolutionary algorithms on most test instances.