A Multi-Objective Particle Swarm Optimization Algorithm Based on Enhanced Selection
Most multi-objective particle swarm optimization algorithms, which have demonstrated their good performance on various practical problems involving two or three objectives, face significant challenges in complex problems. For overcoming this challenges, a multi-objective particle swarm optimization...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8907855/ |
| _version_ | 1818621519955230720 |
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| author | Xin Li Xiao-Li Li Kang Wang Yang Li |
| author_facet | Xin Li Xiao-Li Li Kang Wang Yang Li |
| author_sort | Xin Li |
| collection | DOAJ |
| description | Most multi-objective particle swarm optimization algorithms, which have demonstrated their good performance on various practical problems involving two or three objectives, face significant challenges in complex problems. For overcoming this challenges, a multi-objective particle swarm optimization algorithm based on enhanced selection(ESMOPSO) is proposed. In order to increase the ability of exploration and exploitation, enhanced selection strategy is designed to update personal optimal particles, and objective function weighting is used to update global optimal particle adaptively. In addition, R2 indicator is incorporated into the achievement scalarizing function to layer particles in archive, which promotes the archive update. Besides, Gaussian mutation strategy is designed to avoid particles falling into local optimum, and polynomial mutation is applied in archive to increase the diversity of elite solutions. The performance of the proposed algorithm is validated and compared with some state-of-the-art algorithms on a number of test problems. Experimental results demonstrate that ESMOPSO algorithm shows very competitive performance when dealing with complex MOPs. |
| first_indexed | 2024-12-16T18:10:34Z |
| format | Article |
| id | doaj.art-070391bcf0c34b35b76f8dde1783ac60 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T18:10:34Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-070391bcf0c34b35b76f8dde1783ac602022-12-21T22:21:47ZengIEEEIEEE Access2169-35362019-01-01716809116810310.1109/ACCESS.2019.29545428907855A Multi-Objective Particle Swarm Optimization Algorithm Based on Enhanced SelectionXin Li0https://orcid.org/0000-0002-0342-6547Xiao-Li Li1https://orcid.org/0000-0002-8627-6221Kang Wang2https://orcid.org/0000-0002-8403-7606Yang Li3Faculty of Information Technology, Beijing University of Technology, Beijing, ChinaBeijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing, ChinaSchool of International Studies, Communication University of China, Beijing, ChinaMost multi-objective particle swarm optimization algorithms, which have demonstrated their good performance on various practical problems involving two or three objectives, face significant challenges in complex problems. For overcoming this challenges, a multi-objective particle swarm optimization algorithm based on enhanced selection(ESMOPSO) is proposed. In order to increase the ability of exploration and exploitation, enhanced selection strategy is designed to update personal optimal particles, and objective function weighting is used to update global optimal particle adaptively. In addition, R2 indicator is incorporated into the achievement scalarizing function to layer particles in archive, which promotes the archive update. Besides, Gaussian mutation strategy is designed to avoid particles falling into local optimum, and polynomial mutation is applied in archive to increase the diversity of elite solutions. The performance of the proposed algorithm is validated and compared with some state-of-the-art algorithms on a number of test problems. Experimental results demonstrate that ESMOPSO algorithm shows very competitive performance when dealing with complex MOPs.https://ieeexplore.ieee.org/document/8907855/Multi-objective particle swarmenhanced selectionobjective function weightingachievement scalarizing function |
| spellingShingle | Xin Li Xiao-Li Li Kang Wang Yang Li A Multi-Objective Particle Swarm Optimization Algorithm Based on Enhanced Selection IEEE Access Multi-objective particle swarm enhanced selection objective function weighting achievement scalarizing function |
| title | A Multi-Objective Particle Swarm Optimization Algorithm Based on Enhanced Selection |
| title_full | A Multi-Objective Particle Swarm Optimization Algorithm Based on Enhanced Selection |
| title_fullStr | A Multi-Objective Particle Swarm Optimization Algorithm Based on Enhanced Selection |
| title_full_unstemmed | A Multi-Objective Particle Swarm Optimization Algorithm Based on Enhanced Selection |
| title_short | A Multi-Objective Particle Swarm Optimization Algorithm Based on Enhanced Selection |
| title_sort | multi objective particle swarm optimization algorithm based on enhanced selection |
| topic | Multi-objective particle swarm enhanced selection objective function weighting achievement scalarizing function |
| url | https://ieeexplore.ieee.org/document/8907855/ |
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