A Prediction and Weak Coevolution-Based Dynamic Constrained Multiobjective Optimization
Dynamic multiobjective evolutionary algorithms (DMOEAs) have gained great popularity in dealing with the dynamic multiobjective optimization problems (DMOPs). However, the existing studies have difficulties in tackling DMOPs subject to (dynamic) constraints. In this article, we propose a prediction...
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| Published in | IEEE transactions on evolutionary computation Vol. 29; no. 4; pp. 1328 - 1342 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
IEEE
01.08.2025
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1089-778X 1941-0026 |
| DOI | 10.1109/TEVC.2024.3418470 |
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| Abstract | Dynamic multiobjective evolutionary algorithms (DMOEAs) have gained great popularity in dealing with the dynamic multiobjective optimization problems (DMOPs). However, the existing studies have difficulties in tackling DMOPs subject to (dynamic) constraints. In this article, we propose a prediction and weak coevolutionary multiobjective optimization algorithm (PWDCMO) to handle the dynamic constrained multiobjective optimization problems (DCMOPs), where a prediction strategy is employed to forecast potential optimal regions under the new environment, with a weak coevolutionary constrained multiobjective optimization (CCMO) as the optimizer aiming at balancing exploration and convergence. The proposed method is compared with the four popular dynamic constrained multiobjective evolutionary algorithms (DCMOEAs) on six test instances from two various test suites with their convergence and the overall performance being discussed. Furthermore, the performance of the proposed prediction strategy is also investigated to observe its impact on the final results. Additionally, the PWDCMO is employed in the optimization of an integrated coal mine energy system (ICMES) to validate the proficiency in addressing real world problems. Experimental results demonstrate the superiority of PWDCMO. |
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| AbstractList | Dynamic multiobjective evolutionary algorithms (DMOEAs) have gained great popularity in dealing with the dynamic multiobjective optimization problems (DMOPs). However, the existing studies have difficulties in tackling DMOPs subject to (dynamic) constraints. In this article, we propose a prediction and weak coevolutionary multiobjective optimization algorithm (PWDCMO) to handle the dynamic constrained multiobjective optimization problems (DCMOPs), where a prediction strategy is employed to forecast potential optimal regions under the new environment, with a weak coevolutionary constrained multiobjective optimization (CCMO) as the optimizer aiming at balancing exploration and convergence. The proposed method is compared with the four popular dynamic constrained multiobjective evolutionary algorithms (DCMOEAs) on six test instances from two various test suites with their convergence and the overall performance being discussed. Furthermore, the performance of the proposed prediction strategy is also investigated to observe its impact on the final results. Additionally, the PWDCMO is employed in the optimization of an integrated coal mine energy system (ICMES) to validate the proficiency in addressing real world problems. Experimental results demonstrate the superiority of PWDCMO. |
| Author | Pedrycz, Witold Yang, Shengxiang Gong, Dunwei Rong, Miao Hu, Na Wang, Yan |
| Author_xml | – sequence: 1 givenname: Dunwei orcidid: 0000-0003-2838-4301 surname: Gong fullname: Gong, Dunwei organization: College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao, Shandong, China – sequence: 2 givenname: Miao orcidid: 0000-0003-4273-9367 surname: Rong fullname: Rong, Miao email: rongmiao307@163.com organization: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China – sequence: 3 givenname: Na surname: Hu fullname: Hu, Na organization: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China – sequence: 4 givenname: Yan orcidid: 0000-0002-2809-581X surname: Wang fullname: Wang, Yan organization: School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China – sequence: 5 givenname: Witold orcidid: 0000-0002-9335-9930 surname: Pedrycz fullname: Pedrycz, Witold organization: Electrical and Computer Engineering Department, University of Alberta, Edmonton, AB, Canada – sequence: 6 givenname: Shengxiang orcidid: 0000-0001-7222-4917 surname: Yang fullname: Yang, Shengxiang organization: School of Computer Science and Informatics, De Montfort University, Leicester, U.K |
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| SubjectTerms | Constraint Convergence dynamic multiobjective optimization Evolutionary computation exploration and exploitation Heuristic algorithms Optimization Power system dynamics prediction Sociology Statistics weak coevolution |
| Title | A Prediction and Weak Coevolution-Based Dynamic Constrained Multiobjective Optimization |
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