MOCPSO: A multi-objective cooperative particle swarm optimization algorithm with dual search strategies
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 PS...
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| Published in | Neurocomputing (Amsterdam) Vol. 562; p. 126892 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
28.12.2023
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0925-2312 1872-8286 |
| DOI | 10.1016/j.neucom.2023.126892 |
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| Abstract | 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. |
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| AbstractList | 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. |
| ArticleNumber | 126892 |
| Author | Zhang, Yan Li, Bingdong Hong, Wenjing Zhou, Aimin |
| Author_xml | – sequence: 1 givenname: Yan orcidid: 0000-0002-3545-7890 surname: Zhang fullname: Zhang, Yan email: 71215901034@stu.ecnu.edu.cn organization: Lab of Artificial Intelligence for Education, East China Normal University, 3663 Zhongshan North Road, Shanghai, 200062, China – sequence: 2 givenname: Bingdong orcidid: 0000-0002-1742-2766 surname: Li fullname: Li, Bingdong email: bdli@cs.ecnu.edu.cn organization: Lab of Artificial Intelligence for Education, East China Normal University, 3663 Zhongshan North Road, Shanghai, 200062, China – sequence: 3 givenname: Wenjing orcidid: 0000-0001-9054-5714 surname: Hong fullname: Hong, Wenjing email: hongwj@sustech.edu.cn organization: Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China – sequence: 4 givenname: Aimin orcidid: 0000-0002-4768-5946 surname: Zhou fullname: Zhou, Aimin email: amzhou@cs.ecnu.edu.cn organization: Lab of Artificial Intelligence for Education, East China Normal University, 3663 Zhongshan North Road, Shanghai, 200062, China |
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