Dual-Population Cooperative Correlation Evolutionary Algorithm for Constrained Multi-Objective Optimization
When addressing constrained multi-objective optimization problems (CMOPs), the key challenge lies in achieving a balance between the objective functions and the constraint conditions. However, existing evolutionary algorithms exhibit certain limitations when tackling CMOPs with complex feasible regi...
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MDPI AG
2025-04-01
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| Series: | Mathematics |
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| author | Junming Chen Yanxiu Wang Zichun Shao Hui Zeng Siyuan Zhao |
| author_facet | Junming Chen Yanxiu Wang Zichun Shao Hui Zeng Siyuan Zhao |
| author_sort | Junming Chen |
| collection | DOAJ |
| description | When addressing constrained multi-objective optimization problems (CMOPs), the key challenge lies in achieving a balance between the objective functions and the constraint conditions. However, existing evolutionary algorithms exhibit certain limitations when tackling CMOPs with complex feasible regions. To address this issue, this paper proposes a constrained multi-objective evolutionary algorithm based on a dual-population cooperative correlation (CMOEA-DCC). Under the CMOEA-DDC framework, the system maintains two independently evolving populations: the driving population and the conventional population. These two populations share information through a collaborative interaction mechanism, where the driving population focuses on objective optimization, while the conventional population balances both objectives and constraints. To further enhance the performance of the algorithm, a shift-based density estimation (SDE) method is introduced to maintain the diversity of solutions in the driving population, while a multi-criteria evaluation metric is adopted to improve the feasibility quality of solutions in the normal population. CMOEA-DDC was compared with seven representative constrained multi-objective evolutionary algorithms (CMOEAs) across various test problems and real-world application scenarios. Through an in-depth analysis of a series of experimental results, it can be concluded that CMOEA-DDC significantly outperforms the other competing algorithms in terms of performance. |
| format | Article |
| id | doaj-art-20ba80d1c7204125819db2d117554b6b |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-20ba80d1c7204125819db2d117554b6b2025-08-20T01:49:11ZengMDPI AGMathematics2227-73902025-04-01139144110.3390/math13091441Dual-Population Cooperative Correlation Evolutionary Algorithm for Constrained Multi-Objective OptimizationJunming Chen0Yanxiu Wang1Zichun Shao2Hui Zeng3Siyuan Zhao4Faculty of Humanities and Arts, Macau University of Science and Technology, Macao 999078, ChinaFaculty of Humanities and Arts, Macau University of Science and Technology, Macao 999078, ChinaFaculty of Humanities and Arts, Macau University of Science and Technology, Macao 999078, ChinaSchool of Design, Jiangnan University, Wuxi 214122, ChinaFaculty of Humanities and Arts, Macau University of Science and Technology, Macao 999078, ChinaWhen addressing constrained multi-objective optimization problems (CMOPs), the key challenge lies in achieving a balance between the objective functions and the constraint conditions. However, existing evolutionary algorithms exhibit certain limitations when tackling CMOPs with complex feasible regions. To address this issue, this paper proposes a constrained multi-objective evolutionary algorithm based on a dual-population cooperative correlation (CMOEA-DCC). Under the CMOEA-DDC framework, the system maintains two independently evolving populations: the driving population and the conventional population. These two populations share information through a collaborative interaction mechanism, where the driving population focuses on objective optimization, while the conventional population balances both objectives and constraints. To further enhance the performance of the algorithm, a shift-based density estimation (SDE) method is introduced to maintain the diversity of solutions in the driving population, while a multi-criteria evaluation metric is adopted to improve the feasibility quality of solutions in the normal population. CMOEA-DDC was compared with seven representative constrained multi-objective evolutionary algorithms (CMOEAs) across various test problems and real-world application scenarios. Through an in-depth analysis of a series of experimental results, it can be concluded that CMOEA-DDC significantly outperforms the other competing algorithms in terms of performance.https://www.mdpi.com/2227-7390/13/9/1441constrained multi-objective optimizationevolutionary algorithmdual populationcooperative correlation |
| spellingShingle | Junming Chen Yanxiu Wang Zichun Shao Hui Zeng Siyuan Zhao Dual-Population Cooperative Correlation Evolutionary Algorithm for Constrained Multi-Objective Optimization Mathematics constrained multi-objective optimization evolutionary algorithm dual population cooperative correlation |
| title | Dual-Population Cooperative Correlation Evolutionary Algorithm for Constrained Multi-Objective Optimization |
| title_full | Dual-Population Cooperative Correlation Evolutionary Algorithm for Constrained Multi-Objective Optimization |
| title_fullStr | Dual-Population Cooperative Correlation Evolutionary Algorithm for Constrained Multi-Objective Optimization |
| title_full_unstemmed | Dual-Population Cooperative Correlation Evolutionary Algorithm for Constrained Multi-Objective Optimization |
| title_short | Dual-Population Cooperative Correlation Evolutionary Algorithm for Constrained Multi-Objective Optimization |
| title_sort | dual population cooperative correlation evolutionary algorithm for constrained multi objective optimization |
| topic | constrained multi-objective optimization evolutionary algorithm dual population cooperative correlation |
| url | https://www.mdpi.com/2227-7390/13/9/1441 |
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