A Coevolutionary Algorithm Based on Constraints Decomposition for Constrained Multi-objective Optimization Problems
Abstract Constrained multi-objective optimization problems (CMOPs) are challenging for evolutionary algorithms (EAs). Due to the interaction of multiple constraints, the constrained Pareto fronts (CPFs) exhibit various complex characteristics, e.g., degeneracy, discontinuity or irregularity. Most al...
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Springer
2025-05-01
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| Series: | International Journal of Computational Intelligence Systems |
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| Online Access: | https://doi.org/10.1007/s44196-025-00830-7 |
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| author | Guangpeng Li Li Li Guoyong Cai |
| author_facet | Guangpeng Li Li Li Guoyong Cai |
| author_sort | Guangpeng Li |
| collection | DOAJ |
| description | Abstract Constrained multi-objective optimization problems (CMOPs) are challenging for evolutionary algorithms (EAs). Due to the interaction of multiple constraints, the constrained Pareto fronts (CPFs) exhibit various complex characteristics, e.g., degeneracy, discontinuity or irregularity. Most algorithms achieve poor convergence and diversity performance on these problems. Therefore, we proposed a coevolutionary framework based on constraints decomposition to solve complex CMOPs. Specifically, this framework decomposes the CMOP into multiple help subproblems with a single constraint, thereby decoupling the complex constraints. Then, multiple subpopulations optimize these subproblems to assist in solving the original problem. In addition, a two-stage strategy is used to fully utilize the auxiliary populations to search for feasible solutions. In addition, an evolutionary state detection strategy based on historical information is proposed, which is used to determine whether the evolution moves to the next stage. The framework can take the advantage of the low complexity of single-constraint problems to help algorithm search the complete feasible regions. Experiments on benchmark problems show that the proposed algorithm is competitive with eight other most representative constrained evolutionary algorithms in terms of convergence and diversity performance. |
| format | Article |
| id | doaj-art-aff32ff0e0b4491490bd3d5fbd20df47 |
| institution | OA Journals |
| issn | 1875-6883 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj-art-aff32ff0e0b4491490bd3d5fbd20df472025-08-20T02:15:11ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832025-05-0118113410.1007/s44196-025-00830-7A Coevolutionary Algorithm Based on Constraints Decomposition for Constrained Multi-objective Optimization ProblemsGuangpeng Li0Li Li1Guoyong Cai2Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic TechnologyGuangxi Key Laboratory of Trusted Software, Guilin University of Electronic TechnologyGuangxi Key Laboratory of Trusted Software, Guilin University of Electronic TechnologyAbstract Constrained multi-objective optimization problems (CMOPs) are challenging for evolutionary algorithms (EAs). Due to the interaction of multiple constraints, the constrained Pareto fronts (CPFs) exhibit various complex characteristics, e.g., degeneracy, discontinuity or irregularity. Most algorithms achieve poor convergence and diversity performance on these problems. Therefore, we proposed a coevolutionary framework based on constraints decomposition to solve complex CMOPs. Specifically, this framework decomposes the CMOP into multiple help subproblems with a single constraint, thereby decoupling the complex constraints. Then, multiple subpopulations optimize these subproblems to assist in solving the original problem. In addition, a two-stage strategy is used to fully utilize the auxiliary populations to search for feasible solutions. In addition, an evolutionary state detection strategy based on historical information is proposed, which is used to determine whether the evolution moves to the next stage. The framework can take the advantage of the low complexity of single-constraint problems to help algorithm search the complete feasible regions. Experiments on benchmark problems show that the proposed algorithm is competitive with eight other most representative constrained evolutionary algorithms in terms of convergence and diversity performance.https://doi.org/10.1007/s44196-025-00830-7Constrained multi-objective optimization problemsCoevolutionary frameworkConstraints decompositionSingle-constraint problems |
| spellingShingle | Guangpeng Li Li Li Guoyong Cai A Coevolutionary Algorithm Based on Constraints Decomposition for Constrained Multi-objective Optimization Problems International Journal of Computational Intelligence Systems Constrained multi-objective optimization problems Coevolutionary framework Constraints decomposition Single-constraint problems |
| title | A Coevolutionary Algorithm Based on Constraints Decomposition for Constrained Multi-objective Optimization Problems |
| title_full | A Coevolutionary Algorithm Based on Constraints Decomposition for Constrained Multi-objective Optimization Problems |
| title_fullStr | A Coevolutionary Algorithm Based on Constraints Decomposition for Constrained Multi-objective Optimization Problems |
| title_full_unstemmed | A Coevolutionary Algorithm Based on Constraints Decomposition for Constrained Multi-objective Optimization Problems |
| title_short | A Coevolutionary Algorithm Based on Constraints Decomposition for Constrained Multi-objective Optimization Problems |
| title_sort | coevolutionary algorithm based on constraints decomposition for constrained multi objective optimization problems |
| topic | Constrained multi-objective optimization problems Coevolutionary framework Constraints decomposition Single-constraint problems |
| url | https://doi.org/10.1007/s44196-025-00830-7 |
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