Constrained multi-objective state transition algorithm via adaptive bidirectional coevolution
Constrained multi-objective optimization problems (CMOPs) involve optimizing multiple conflicting objectives subject to at least one constraint. These constraints often divide the search space into various infeasible regions and narrow or disconnected feasible regions. Most existing constrained mult...
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| Vydáno v: | Expert systems with applications Ročník 266; s. 126073 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
25.03.2025
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| ISSN: | 0957-4174 |
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| Abstract | Constrained multi-objective optimization problems (CMOPs) involve optimizing multiple conflicting objectives subject to at least one constraint. These constraints often divide the search space into various infeasible regions and narrow or disconnected feasible regions. Most existing constrained multi-objective evolutionary algorithms struggle with imbalanced exploration between feasible and infeasible regions and exhibit poor search capabilities, resulting in populations becoming trapped in locally optimal feasible or infeasible areas. To overcome this limitation, we propose a novel constrained multi-objective state transition algorithm via adaptive bidirectional coevolution (CMOSTA). This algorithm comprises a main population (MP) and a cooperative population (CP), facilitating balanced exploration of both feasible and infeasible regions. CMOSTA adapts environmental selection strategies based on the proportion and distribution of feasible solutions within the MP, promoting efficient information sharing and avoiding unnecessary searches. Additionally, a dynamic ɛ-constraint relaxation strategy is put forward for the MP to prevent stagnation in locally feasible areas. A mating selection approach combining binary tournament and dynamic ɛ-constrained dominance is developed, followed by state transformation operators to generate candidate solutions with both global and local search capabilities. The effectiveness of CMOSTA is verified through 62 benchmark tests and an industrial case study on optimal copper removal, showing superior performance compared to ten well-established constrained multi-objective evolutionary algorithms.
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•Propose a coevolution-based constrained multi-objective state transition algorithm.•Adapt environmental strategies based on real-time status of feasible solutions.•Present a dynamic ɛ-relaxation strategy to prevent local stagnation.•Employ binary tournament with ɛ-dominance for mating selection.•Adopt state transformation operators to generate high-quality candidate solutions. |
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| AbstractList | Constrained multi-objective optimization problems (CMOPs) involve optimizing multiple conflicting objectives subject to at least one constraint. These constraints often divide the search space into various infeasible regions and narrow or disconnected feasible regions. Most existing constrained multi-objective evolutionary algorithms struggle with imbalanced exploration between feasible and infeasible regions and exhibit poor search capabilities, resulting in populations becoming trapped in locally optimal feasible or infeasible areas. To overcome this limitation, we propose a novel constrained multi-objective state transition algorithm via adaptive bidirectional coevolution (CMOSTA). This algorithm comprises a main population (MP) and a cooperative population (CP), facilitating balanced exploration of both feasible and infeasible regions. CMOSTA adapts environmental selection strategies based on the proportion and distribution of feasible solutions within the MP, promoting efficient information sharing and avoiding unnecessary searches. Additionally, a dynamic ɛ-constraint relaxation strategy is put forward for the MP to prevent stagnation in locally feasible areas. A mating selection approach combining binary tournament and dynamic ɛ-constrained dominance is developed, followed by state transformation operators to generate candidate solutions with both global and local search capabilities. The effectiveness of CMOSTA is verified through 62 benchmark tests and an industrial case study on optimal copper removal, showing superior performance compared to ten well-established constrained multi-objective evolutionary algorithms.
[Display omitted]
•Propose a coevolution-based constrained multi-objective state transition algorithm.•Adapt environmental strategies based on real-time status of feasible solutions.•Present a dynamic ɛ-relaxation strategy to prevent local stagnation.•Employ binary tournament with ɛ-dominance for mating selection.•Adopt state transformation operators to generate high-quality candidate solutions. |
| ArticleNumber | 126073 |
| Author | Huang, Tingwen Yang, Chunhua Sun, Yan Zhou, Xiaojun |
| Author_xml | – sequence: 1 givenname: Yan orcidid: 0009-0008-3699-4883 surname: Sun fullname: Sun, Yan email: sun_yan@csu.edu.cn organization: School of Automation, Central South University, Changsha 410083, China – sequence: 2 givenname: Xiaojun orcidid: 0000-0002-6367-696X surname: Zhou fullname: Zhou, Xiaojun email: michael.x.zhou@csu.edu.cn organization: School of Automation, Central South University, Changsha 410083, China – sequence: 3 givenname: Chunhua orcidid: 0000-0003-2550-1509 surname: Yang fullname: Yang, Chunhua email: ychh@csu.edu.cn organization: School of Automation, Central South University, Changsha 410083, China – sequence: 4 givenname: Tingwen orcidid: 0000-0001-9610-846X surname: Huang fullname: Huang, Tingwen email: tingwen.huang@qatar.tamu.edu organization: Texas A & M University at Qatar, Doha 23874, Qatar |
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| Cites_doi | 10.1016/j.asoc.2022.108553 10.1109/TCYB.2020.3031642 10.1109/TEVC.2007.892759 10.1109/TMAG.2004.825006 10.1109/TEVC.2020.2981949 10.1016/j.eswa.2022.119258 10.1007/s00500-019-03794-x 10.1016/j.hydromet.2017.08.007 10.1016/j.eswa.2023.122119 10.1016/j.hydromet.2018.03.021 10.1080/10556788.2020.1808977 10.1145/3510426 10.1016/j.eswa.2015.10.039 10.1109/TEVC.2020.3011829 10.1109/TCYB.2022.3163759 10.3390/math8010007 10.1145/508791.508907 10.1109/TCYB.2021.3056176 10.1109/TSMC.2022.3171076 10.1016/j.eswa.2023.122961 10.1016/j.ins.2021.01.029 10.1109/4235.873238 10.1016/j.cej.2015.07.094 10.1109/TEVC.2020.3004012 10.1016/j.swevo.2019.100619 10.1109/TEVC.2021.3066301 10.1109/TEVC.2018.2855411 10.1016/j.asoc.2017.06.053 10.1016/j.knosys.2021.107131 10.1007/s40747-022-00851-1 10.1007/s40747-020-00249-x 10.1109/TEVC.2013.2281534 10.1162/EVCO_a_00009 10.1109/TEVC.2019.2894743 10.3934/jimo.2012.8.1039 10.1109/TEVC.2013.2281535 10.1016/j.swevo.2018.08.017 10.1016/j.swevo.2024.101584 10.1016/j.energy.2022.125785 10.1016/j.swevo.2024.101581 10.1007/s11831-022-09859-9 10.1109/4235.996017 10.1109/MCI.2017.2742868 10.1109/CEC.2006.1688283 10.1016/j.knosys.2021.107653 10.1016/j.asoc.2022.109613 10.1109/TEVC.2019.2896967 10.1109/TEVC.2011.2161872 10.1016/j.asoc.2023.111172 10.1109/TEVC.2022.3155533 10.1109/TCYB.2018.2819208 |
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| Keywords | Constrained multi-objective optimization Adaptive bidirectional coevolution State transition algorithm Dynamic ɛ-constraint boundary |
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