An Improved Coevolutionary Algorithm for Constrained Multi-Objective Optimization Problems
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| Názov: | An Improved Coevolutionary Algorithm for Constrained Multi-Objective Optimization Problems |
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| Autori: | Shumin Xie, Zhenjia Zhu, Hui Wang |
| Zdroj: | International Journal of Cognitive Informatics and Natural Intelligence. 18:1-16 |
| Informácie o vydavateľovi: | IGI Global, 2024. |
| Rok vydania: | 2024 |
| Popis: | Constrained multi-objective optimization problems are ubiquitous in engineering applications. In recent years, constrained multi-objective optimization algorithms based on the dual population coevolutionary framework have been widely studied due to their excellent performance. However, when facing optimization problems with complex constraints, the performance of existing algorithms still needs further improvement. This paper proposes an improved constrained multi-objective coevolutionary algorithm (iCMOCA). The algorithm mainly includes two populations: One population takes into account constraints, while the other population disregards them. Meanwhile, the iCMOCA employs effective collaboration between two populations during the process of offspring generation and environmental selection, and it utilizes an environmental selection strategy based on multi-objective to multi-objective decomposition to improve the performance. Comparative analysis conducted on the DAS-CMOP and MW test suites provides empirical evidence that iCMOCA outperforms five state-of-the-art algorithms. |
| Druh dokumentu: | Article |
| Jazyk: | Ndonga |
| ISSN: | 1557-3966 1557-3958 |
| DOI: | 10.4018/ijcini.355766 |
| Rights: | CC BY |
| Prístupové číslo: | edsair.doi...........15bc9c049cae8ac4bd5ce2e66c1a91f5 |
| Databáza: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstrakt: | Constrained multi-objective optimization problems are ubiquitous in engineering applications. In recent years, constrained multi-objective optimization algorithms based on the dual population coevolutionary framework have been widely studied due to their excellent performance. However, when facing optimization problems with complex constraints, the performance of existing algorithms still needs further improvement. This paper proposes an improved constrained multi-objective coevolutionary algorithm (iCMOCA). The algorithm mainly includes two populations: One population takes into account constraints, while the other population disregards them. Meanwhile, the iCMOCA employs effective collaboration between two populations during the process of offspring generation and environmental selection, and it utilizes an environmental selection strategy based on multi-objective to multi-objective decomposition to improve the performance. Comparative analysis conducted on the DAS-CMOP and MW test suites provides empirical evidence that iCMOCA outperforms five state-of-the-art algorithms. |
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| ISSN: | 15573966 15573958 |
| DOI: | 10.4018/ijcini.355766 |