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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Vydáno v:IEEE transactions on evolutionary computation Ročník 24; číslo 5; s. 938 - 947
Hlavní autoři: Zhu, Qingling, Zhang, Qingfu, Lin, Qiuzhen
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.10.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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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Shrnutí: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.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2020.2981949