A Novel Dual-Stage Dual-Population Evolutionary Algorithm for Constrained Multiobjective Optimization

In addition to the search for feasible solutions, the utilization of informative infeasible solutions is important for solving constrained multiobjective optimization problems (CMOPs). However, most of the existing constrained multiobjective evolutionary algorithms (CMOEAs) cannot effectively explor...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 26; no. 5; pp. 1129 - 1143
Main Authors: Ming, Mengjun, Wang, Rui, Ishibuchi, Hisao, Zhang, Tao
Format: Journal Article
Language:English
Published: New York IEEE 01.10.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Coevolution
constrained multiobjective optimization problems (CMOPs)
Constraints
Convergence
Evolutionary algorithms
Evolutionary computation
Exploitation
exploration
Genetic algorithms
Matrix
Multiple objective analysis
Optimization
Pareto optimization
Populations
Search problems
Sociology
Statistics
Switches
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Summary:In addition to the search for feasible solutions, the utilization of informative infeasible solutions is important for solving constrained multiobjective optimization problems (CMOPs). However, most of the existing constrained multiobjective evolutionary algorithms (CMOEAs) cannot effectively explore and exploit those solutions and, therefore, exhibit poor performance when facing problems with large infeasible regions. To address the issue, this article proposes a novel method, called DD-CMOEA, which features dual stages (i.e., exploration and exploitation) and dual populations. Specifically, the two populations, called mainPop and auxPop, first individually evolve with and without considering the constraints, responsible for exploring feasible and infeasible solutions, respectively. Then, in the exploitation stage, mainPop provides information about the location of feasible regions, which facilitates auxPop to find and exploit surrounding infeasible solutions. The promising infeasible solutions obtained by auxPop in turn help mainPop converge better toward the Pareto-optimal front. Extensive experiments on three well-known test suites and a real-world case study fully demonstrate that DD-CMOEA is more competitive than five state-of-the-art CMOEAs.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2021.3131124