A Survey on Evolutionary Constrained Multiobjective Optimization

Handling constrained multiobjective optimization problems (CMOPs) is extremely challenging, since multiple conflicting objectives subject to various constraints require to be simultaneously optimized. To deal with CMOPs, numerous constrained multiobjective evolutionary algorithms (CMOEAs) have been...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 27; H. 2; S. 201 - 221
Hauptverfasser:	Liang, Jing, Ban, Xuanxuan, Yu, Kunjie, Qu, Boyang, Qiao, Kangjia, Yue, Caitong, Chen, Ke, Tan, Kay Chen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.04.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Benchmark test problems Benchmark testing constrained multiobjective optimization constraint handling Constraints Convergence Evolutionary algorithms Evolutionary computation Literature reviews Multiple objective analysis Optimization Pareto optimization Sociology Statistics
ISSN:	1089-778X, 1941-0026
Online-Zugang:	Volltext
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Abstract	Handling constrained multiobjective optimization problems (CMOPs) is extremely challenging, since multiple conflicting objectives subject to various constraints require to be simultaneously optimized. To deal with CMOPs, numerous constrained multiobjective evolutionary algorithms (CMOEAs) have been proposed in recent years, and they have achieved promising performance. However, there has been few literature on the systematic review of the related studies currently. This article provides a comprehensive survey for evolutionary constrained multiobjective optimization. We first review a large number of CMOEAs through categorization and analyze their advantages and drawbacks in each category. Then, we summarize the benchmark test problems and investigate the performance of different constraint handling techniques (CHTs) and different algorithms, followed by some emerging and representative applications of CMOEAs. Finally, we discuss some new challenges and point out some directions of the future research in the field of evolutionary constrained multiobjective optimization.
AbstractList	Handling constrained multiobjective optimization problems (CMOPs) is extremely challenging, since multiple conflicting objectives subject to various constraints require to be simultaneously optimized. To deal with CMOPs, numerous constrained multiobjective evolutionary algorithms (CMOEAs) have been proposed in recent years, and they have achieved promising performance. However, there has been few literature on the systematic review of the related studies currently. This article provides a comprehensive survey for evolutionary constrained multiobjective optimization. We first review a large number of CMOEAs through categorization and analyze their advantages and drawbacks in each category. Then, we summarize the benchmark test problems and investigate the performance of different constraint handling techniques (CHTs) and different algorithms, followed by some emerging and representative applications of CMOEAs. Finally, we discuss some new challenges and point out some directions of the future research in the field of evolutionary constrained multiobjective optimization.
Author	Ban, Xuanxuan Tan, Kay Chen Yue, Caitong Qu, Boyang Liang, Jing Yu, Kunjie Qiao, Kangjia Chen, Ke
Author_xml	– sequence: 1 givenname: Jing orcidid: 0000-0003-0811-0223 surname: Liang fullname: Liang, Jing email: liangjing@zzu.edu.cn organization: School of Electrical Engineering, Zhengzhou University, Zhengzhou, China – sequence: 2 givenname: Xuanxuan surname: Ban fullname: Ban, Xuanxuan email: 18438622381@163.com organization: School of Electrical Engineering, Zhengzhou University, Zhengzhou, China – sequence: 3 givenname: Kunjie orcidid: 0000-0001-9945-1976 surname: Yu fullname: Yu, Kunjie email: yukunjie@zzu.edu.cn organization: School of Electrical Engineering, Zhengzhou University, Zhengzhou, China – sequence: 4 givenname: Boyang orcidid: 0000-0001-7539-3927 surname: Qu fullname: Qu, Boyang email: qby1984@hotmail.com organization: School of Electrical and Information Engineering, Zhongyuan University of Technology, Zhengzhou, China – sequence: 5 givenname: Kangjia orcidid: 0000-0003-1713-7700 surname: Qiao fullname: Qiao, Kangjia email: qiaokangjia@yeah.net organization: School of Electrical Engineering, Zhengzhou University, Zhengzhou, China – sequence: 6 givenname: Caitong orcidid: 0000-0002-3362-0703 surname: Yue fullname: Yue, Caitong email: zzuyuecaitong@163.com organization: School of Electrical Engineering, Zhengzhou University, Zhengzhou, China – sequence: 7 givenname: Ke orcidid: 0000-0002-7851-5050 surname: Chen fullname: Chen, Ke email: chenkezixf@163.com organization: School of Electrical Engineering, Zhengzhou University, Zhengzhou, China – sequence: 8 givenname: Kay Chen orcidid: 0000-0002-6802-2463 surname: Tan fullname: Tan, Kay Chen email: kctan@polyu.edu.hk organization: Department of Computing, The Hong Kong Polytechnic University, Hong Kong, SAR
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Snippet	Handling constrained multiobjective optimization problems (CMOPs) is extremely challenging, since multiple conflicting objectives subject to various...
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SubjectTerms	Benchmark test problems Benchmark testing constrained multiobjective optimization constraint handling Constraints Convergence Evolutionary algorithms Evolutionary computation Literature reviews Multiple objective analysis Optimization Pareto optimization Sociology Statistics
Title	A Survey on Evolutionary Constrained Multiobjective Optimization
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