A corrective direction particle swarm optimization for large-scale multi-objective optimization
Abstract In multi-objective optimization, Large-Scale Multi-Objective Optimization Problems (LSMOPs) involve a large number of decision variables and present significant challenges in finding satisfactory results with a limited number of function evaluations. To address this issue, a Corrective Dire...
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| Format: | Article |
| Language: | English |
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Springer
2025-06-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-025-01954-1 |
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| author | Weichao Chen Ziyang Li Xue Li |
| author_facet | Weichao Chen Ziyang Li Xue Li |
| author_sort | Weichao Chen |
| collection | DOAJ |
| description | Abstract In multi-objective optimization, Large-Scale Multi-Objective Optimization Problems (LSMOPs) involve a large number of decision variables and present significant challenges in finding satisfactory results with a limited number of function evaluations. To address this issue, a Corrective Direction Particle Swarm Optimization (CDPSO) is proposed for large-scale multi-objective optimization in this paper. The core idea of CDPSO is to maintain the correctness of the search direction of the particles in the decision space to improve convergence speed. Specifically, a competitive learning strategy that is employed by normal particles in the decision space to select elite particles with the smallest directional deviation improves the population’s ability to discover the Pareto optimal solutions. Moreover, a novel exploration mechanism is used by the elite particles, which spreads the exploration across each dimension of the decision variables and adaptively adjusts the velocity components in each dimension to enhance the algorithm’s performance further. Experimental results show that our proposed CDPSO exhibits strong competitiveness compared to the other nine state-of-the-art algorithms when tested on both benchmark problems and real-world problems. |
| format | Article |
| id | doaj-art-3ad7f6e564c04642ad43c23ebdd552e2 |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-3ad7f6e564c04642ad43c23ebdd552e22025-08-20T04:02:49ZengSpringerComplex & Intelligent Systems2199-45362198-60532025-06-0111813110.1007/s40747-025-01954-1A corrective direction particle swarm optimization for large-scale multi-objective optimizationWeichao Chen0Ziyang Li1Xue Li2School of Software, Xinjiang UniversitySchool of Software, Xinjiang UniversitySchool of Computer Science and Technology, Xinjiang UniversityAbstract In multi-objective optimization, Large-Scale Multi-Objective Optimization Problems (LSMOPs) involve a large number of decision variables and present significant challenges in finding satisfactory results with a limited number of function evaluations. To address this issue, a Corrective Direction Particle Swarm Optimization (CDPSO) is proposed for large-scale multi-objective optimization in this paper. The core idea of CDPSO is to maintain the correctness of the search direction of the particles in the decision space to improve convergence speed. Specifically, a competitive learning strategy that is employed by normal particles in the decision space to select elite particles with the smallest directional deviation improves the population’s ability to discover the Pareto optimal solutions. Moreover, a novel exploration mechanism is used by the elite particles, which spreads the exploration across each dimension of the decision variables and adaptively adjusts the velocity components in each dimension to enhance the algorithm’s performance further. Experimental results show that our proposed CDPSO exhibits strong competitiveness compared to the other nine state-of-the-art algorithms when tested on both benchmark problems and real-world problems.https://doi.org/10.1007/s40747-025-01954-1Large-scaleMulti-objective optimizationParticle swarm optimizationCorrective direction |
| spellingShingle | Weichao Chen Ziyang Li Xue Li A corrective direction particle swarm optimization for large-scale multi-objective optimization Complex & Intelligent Systems Large-scale Multi-objective optimization Particle swarm optimization Corrective direction |
| title | A corrective direction particle swarm optimization for large-scale multi-objective optimization |
| title_full | A corrective direction particle swarm optimization for large-scale multi-objective optimization |
| title_fullStr | A corrective direction particle swarm optimization for large-scale multi-objective optimization |
| title_full_unstemmed | A corrective direction particle swarm optimization for large-scale multi-objective optimization |
| title_short | A corrective direction particle swarm optimization for large-scale multi-objective optimization |
| title_sort | corrective direction particle swarm optimization for large scale multi objective optimization |
| topic | Large-scale Multi-objective optimization Particle swarm optimization Corrective direction |
| url | https://doi.org/10.1007/s40747-025-01954-1 |
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