A Constrained Multiobjective Evolutionary Algorithm With Detect-and-Escape Strategy

Overall constraint violation functions are commonly used in multiobjective evolutionary algorithms (MOEAs) for handling constraints. Constraints could cause these algorithms stuck in two stagnation states: 1) since the feasible region of a multiobjective optimization problem can consist of several d...

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Vydané v:	IEEE transactions on evolutionary computation Ročník 24; číslo 5; s. 938 - 947
Hlavní autori:	Zhu, Qingling, Zhang, Qingfu, Lin, Qiuzhen
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.10.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Constraint handling Constraint handling technique (CHT) Constraints Evolutionary algorithms Evolutionary computation Genetic algorithms MOEA/D multiobjective optimization Multiple objective analysis Optimization Pareto optimization Searching Sociology Stagnation Statistics Strategy Urban areas
ISSN:	1089-778X, 1941-0026
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Abstract	Overall constraint violation functions are commonly used in multiobjective evolutionary algorithms (MOEAs) for handling constraints. Constraints could cause these algorithms stuck in two stagnation states: 1) since the feasible region of a multiobjective optimization problem can consist of several disconnected feasible subregions, the search can be easily trapped in a feasible subregion which does not contain all the global Pareto optimal solutions and 2) an overall constraint violation function may have many nonzero minimal points, it can make the search stuck in an unfeasible area. To address these two issues, this article proposes a strategy to detect whether or not the search is stuck in these two stagnation states and then escape from them. Our proposed detect-and-escape strategy uses the feasible ratio and the change rate of overall constraint violation to detect stagnation, and adjusts the weight of the constraint violation for guiding the search to escape from stagnation states. We develop and implement a decomposition-based constrained MOEA with this strategy. Extensive experiments on a number of benchmark problems demonstrate the competitiveness of our proposed algorithm when compared to five other state-of-the-art constrained evolutionary algorithms.
AbstractList	Overall constraint violation functions are commonly used in multiobjective evolutionary algorithms (MOEAs) for handling constraints. Constraints could cause these algorithms stuck in two stagnation states: 1) since the feasible region of a multiobjective optimization problem can consist of several disconnected feasible subregions, the search can be easily trapped in a feasible subregion which does not contain all the global Pareto optimal solutions and 2) an overall constraint violation function may have many nonzero minimal points, it can make the search stuck in an unfeasible area. To address these two issues, this article proposes a strategy to detect whether or not the search is stuck in these two stagnation states and then escape from them. Our proposed detect-and-escape strategy uses the feasible ratio and the change rate of overall constraint violation to detect stagnation, and adjusts the weight of the constraint violation for guiding the search to escape from stagnation states. We develop and implement a decomposition-based constrained MOEA with this strategy. Extensive experiments on a number of benchmark problems demonstrate the competitiveness of our proposed algorithm when compared to five other state-of-the-art constrained evolutionary algorithms.
Author	Zhang, Qingfu Zhu, Qingling Lin, Qiuzhen
Author_xml	– sequence: 1 givenname: Qingling orcidid: 0000-0002-0228-8226 surname: Zhu fullname: Zhu, Qingling email: qlzhu4-c@my.cityu.edu.hk organization: Department of Computer Science, City University of Hong Kong, Hong Kong – sequence: 2 givenname: Qingfu surname: Zhang fullname: Zhang, Qingfu email: qingfu.zhang@cityu.edu.hk organization: Department of Computer Science, City University of Hong Kong, Hong Kong – sequence: 3 givenname: Qiuzhen orcidid: 0000-0003-2415-0401 surname: Lin fullname: Lin, Qiuzhen email: qiuzhlin@szu.edu.cn organization: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
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Snippet	Overall constraint violation functions are commonly used in multiobjective evolutionary algorithms (MOEAs) for handling constraints. Constraints could cause...
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SubjectTerms	Constraint handling Constraint handling technique (CHT) Constraints Evolutionary algorithms Evolutionary computation Genetic algorithms MOEA/D multiobjective optimization Multiple objective analysis Optimization Pareto optimization Searching Sociology Stagnation Statistics Strategy Urban areas
Title	A Constrained Multiobjective Evolutionary Algorithm With Detect-and-Escape Strategy
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