A Zoning Search-Based Multimodal Multi-Objective Brain Storm Optimization Algorithm for Multimodal Multi-Objective Optimization
For multimodal multi-objective optimization problems (MMOPs), there are multiple equivalent Pareto optimal solutions in the decision space that are corresponding to the same objective value. Therefore, the main tasks of multimodal multi-objective optimization (MMO) are to find a high-quality PF appr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/16/7/350 |
| _version_ | 1797590596662067200 |
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| author | Jiajia Fan Wentao Huang Qingchao Jiang Qinqin Fan |
| author_facet | Jiajia Fan Wentao Huang Qingchao Jiang Qinqin Fan |
| author_sort | Jiajia Fan |
| collection | DOAJ |
| description | For multimodal multi-objective optimization problems (MMOPs), there are multiple equivalent Pareto optimal solutions in the decision space that are corresponding to the same objective value. Therefore, the main tasks of multimodal multi-objective optimization (MMO) are to find a high-quality PF approximation in the objective space and maintain the population diversity in the decision space. To achieve the above objectives, this article proposes a zoning search-based multimodal multi-objective brain storm optimization algorithm (ZS-MMBSO). At first, the search space segmentation method is employed to divide the search space into some sub-regions. Moreover, a novel individual generation strategy is incorporated into the multimodal multi-objective brain storm optimization algorithm, which can improve the search performance of the search engineering. The proposed algorithm is compared with five famous multimodal multi-objective evolutionary algorithms (MMOEAs) on IEEE CEC2019 MMOPs benchmark test suite. Experimental results indicate that the overall performance of the ZS-MMBSO is the best among all competitors. |
| first_indexed | 2024-03-11T01:22:48Z |
| format | Article |
| id | doaj.art-b30e0fa75f1b4b3e960e9fb8d0eea6a0 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-11T01:22:48Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-b30e0fa75f1b4b3e960e9fb8d0eea6a02023-11-18T17:59:25ZengMDPI AGAlgorithms1999-48932023-07-0116735010.3390/a16070350A Zoning Search-Based Multimodal Multi-Objective Brain Storm Optimization Algorithm for Multimodal Multi-Objective OptimizationJiajia Fan0Wentao Huang1Qingchao Jiang2Qinqin Fan3Logistics Research Center, Shanghai Maritime University, Shanghai 201306, ChinaKey Laboratory of Control of Power Transmission and Conversion, Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, ChinaKey Laboratory of Smart Manufacturing in Energy Chemical, Ministry of Education, East China University of Science and Technology, Shanghai 200237, ChinaLogistics Research Center, Shanghai Maritime University, Shanghai 201306, ChinaFor multimodal multi-objective optimization problems (MMOPs), there are multiple equivalent Pareto optimal solutions in the decision space that are corresponding to the same objective value. Therefore, the main tasks of multimodal multi-objective optimization (MMO) are to find a high-quality PF approximation in the objective space and maintain the population diversity in the decision space. To achieve the above objectives, this article proposes a zoning search-based multimodal multi-objective brain storm optimization algorithm (ZS-MMBSO). At first, the search space segmentation method is employed to divide the search space into some sub-regions. Moreover, a novel individual generation strategy is incorporated into the multimodal multi-objective brain storm optimization algorithm, which can improve the search performance of the search engineering. The proposed algorithm is compared with five famous multimodal multi-objective evolutionary algorithms (MMOEAs) on IEEE CEC2019 MMOPs benchmark test suite. Experimental results indicate that the overall performance of the ZS-MMBSO is the best among all competitors.https://www.mdpi.com/1999-4893/16/7/350brain storm optimization algorithmmultimodal multi-objective optimizationevolutionary computationzoning search |
| spellingShingle | Jiajia Fan Wentao Huang Qingchao Jiang Qinqin Fan A Zoning Search-Based Multimodal Multi-Objective Brain Storm Optimization Algorithm for Multimodal Multi-Objective Optimization Algorithms brain storm optimization algorithm multimodal multi-objective optimization evolutionary computation zoning search |
| title | A Zoning Search-Based Multimodal Multi-Objective Brain Storm Optimization Algorithm for Multimodal Multi-Objective Optimization |
| title_full | A Zoning Search-Based Multimodal Multi-Objective Brain Storm Optimization Algorithm for Multimodal Multi-Objective Optimization |
| title_fullStr | A Zoning Search-Based Multimodal Multi-Objective Brain Storm Optimization Algorithm for Multimodal Multi-Objective Optimization |
| title_full_unstemmed | A Zoning Search-Based Multimodal Multi-Objective Brain Storm Optimization Algorithm for Multimodal Multi-Objective Optimization |
| title_short | A Zoning Search-Based Multimodal Multi-Objective Brain Storm Optimization Algorithm for Multimodal Multi-Objective Optimization |
| title_sort | zoning search based multimodal multi objective brain storm optimization algorithm for multimodal multi objective optimization |
| topic | brain storm optimization algorithm multimodal multi-objective optimization evolutionary computation zoning search |
| url | https://www.mdpi.com/1999-4893/16/7/350 |
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