A two-stage maintenance and multi-strategy selection for multi-objective particle swarm optimization
Abstract In multi-objective particle swarm optimization, it is very important to select the personal best and the global best. These leaders are expected to effectively guide the population toward the true Pareto front. In this paper, we propose a two-stage maintenance and multi-strategy selection f...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2023-07-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-023-01128-x |
| _version_ | 1827781023959613440 |
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| author | Jun Liu Yanmin Liu Huayao Han Xianzi Zhang Xiaoli Shu Fei Chen |
| author_facet | Jun Liu Yanmin Liu Huayao Han Xianzi Zhang Xiaoli Shu Fei Chen |
| author_sort | Jun Liu |
| collection | DOAJ |
| description | Abstract In multi-objective particle swarm optimization, it is very important to select the personal best and the global best. These leaders are expected to effectively guide the population toward the true Pareto front. In this paper, we propose a two-stage maintenance and multi-strategy selection for multi-objective particle swarm optimization (TMMOPSO), which adaptively selects the global best and updates the personal best by means of hyper-cone domain and aggregation, respectively. This strategy enhances the global exploration and local exploitation abilities of the population. In addition, the excellent particles are perturbed and a two-stage maintenance strategy is used for the external archive. This strategy not only improves the quality of the solutions in the population but also accelerates the convergence speed of the population. In this paper, the proposed algorithm is compared with several multi-objective optimization algorithms on 29 benchmark problems. The experimental results show that TMMOPSO is effective and outperforms the comparison algorithms on most of the 29 benchmark problems. |
| first_indexed | 2024-03-11T15:12:00Z |
| format | Article |
| id | doaj.art-aa9d92ccccdf49e98f884a655a6883b4 |
| institution | Directory Open Access Journal |
| issn | 2199-4536 2198-6053 |
| language | English |
| last_indexed | 2024-03-11T15:12:00Z |
| publishDate | 2023-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj.art-aa9d92ccccdf49e98f884a655a6883b42023-10-29T12:41:32ZengSpringerComplex & Intelligent Systems2199-45362198-60532023-07-01967523754810.1007/s40747-023-01128-xA two-stage maintenance and multi-strategy selection for multi-objective particle swarm optimizationJun Liu0Yanmin Liu1Huayao Han2Xianzi Zhang3Xiaoli Shu4Fei Chen5School of Data Science and Information Engineering, Guizhou Minzu UniversitySchool of Mathematics, Zunyi Normal CollegeGuizhou Xingqian Information Technology Co. LtdSchool of Data Science and Information Engineering, Guizhou Minzu UniversitySchool of Data Science and Information Engineering, Guizhou Minzu UniversitySchool of Mathematics and Statistics, Guizhou UniversityAbstract In multi-objective particle swarm optimization, it is very important to select the personal best and the global best. These leaders are expected to effectively guide the population toward the true Pareto front. In this paper, we propose a two-stage maintenance and multi-strategy selection for multi-objective particle swarm optimization (TMMOPSO), which adaptively selects the global best and updates the personal best by means of hyper-cone domain and aggregation, respectively. This strategy enhances the global exploration and local exploitation abilities of the population. In addition, the excellent particles are perturbed and a two-stage maintenance strategy is used for the external archive. This strategy not only improves the quality of the solutions in the population but also accelerates the convergence speed of the population. In this paper, the proposed algorithm is compared with several multi-objective optimization algorithms on 29 benchmark problems. The experimental results show that TMMOPSO is effective and outperforms the comparison algorithms on most of the 29 benchmark problems.https://doi.org/10.1007/s40747-023-01128-xMulti-objective particle swarm optimizationHyper-cone domainTwo-stage maintenanceMulti-objective optimization |
| spellingShingle | Jun Liu Yanmin Liu Huayao Han Xianzi Zhang Xiaoli Shu Fei Chen A two-stage maintenance and multi-strategy selection for multi-objective particle swarm optimization Complex & Intelligent Systems Multi-objective particle swarm optimization Hyper-cone domain Two-stage maintenance Multi-objective optimization |
| title | A two-stage maintenance and multi-strategy selection for multi-objective particle swarm optimization |
| title_full | A two-stage maintenance and multi-strategy selection for multi-objective particle swarm optimization |
| title_fullStr | A two-stage maintenance and multi-strategy selection for multi-objective particle swarm optimization |
| title_full_unstemmed | A two-stage maintenance and multi-strategy selection for multi-objective particle swarm optimization |
| title_short | A two-stage maintenance and multi-strategy selection for multi-objective particle swarm optimization |
| title_sort | two stage maintenance and multi strategy selection for multi objective particle swarm optimization |
| topic | Multi-objective particle swarm optimization Hyper-cone domain Two-stage maintenance Multi-objective optimization |
| url | https://doi.org/10.1007/s40747-023-01128-x |
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