A Coevolutionary Framework for Constrained Multiobjective Optimization Problems

Constrained multiobjective optimization problems (CMOPs) are challenging because of the difficulty in handling both multiple objectives and constraints. While some evolutionary algorithms have demonstrated high performance on most CMOPs, they exhibit bad convergence or diversity performance on CMOPs...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 25; no. 1; pp. 102 - 116
Main Authors: Tian, Ye, Zhang, Tao, Xiao, Jianhua, Zhang, Xingyi, Jin, Yaochu
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Coevolution
constrained multiobjective optimization
Constraint handling
Constraints
Convergence
evolutionary algorithm
Evolutionary algorithms
Evolutionary computation
Multiple objective analysis
Optimization
Populations
Route planning
Sociology
Statistics
Vehicle routing
vehicle routing problem
Windows (intervals)
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Summary:Constrained multiobjective optimization problems (CMOPs) are challenging because of the difficulty in handling both multiple objectives and constraints. While some evolutionary algorithms have demonstrated high performance on most CMOPs, they exhibit bad convergence or diversity performance on CMOPs with small feasible regions. To remedy this issue, this article proposes a coevolutionary framework for constrained multiobjective optimization, which solves a complex CMOP assisted by a simple helper problem. The proposed framework evolves one population to solve the original CMOP and evolves another population to solve a helper problem derived from the original one. While the two populations are evolved by the same optimizer separately, the assistance in solving the original CMOP is achieved by sharing useful information between the two populations. In the experiments, the proposed framework is compared to several state-of-the-art algorithms tailored for CMOPs. High competitiveness of the proposed framework is demonstrated by applying it to 47 benchmark CMOPs and the vehicle routing problem with time windows.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2020.3004012