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A Coevolutionary Framework for Constrained Multi-Objective Optimization Problems

Tian, Ye, Zhang, Tao, Xiao, Jinhua, Zhang, Xingyi and Jin, Yaochu (2020) A Coevolutionary Framework for Constrained Multi-Objective Optimization Problems Transactions on Evolutionary Computation.

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Abstract

Constrained multi-objective 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 paper proposes a coevolutionary framework for constrained multi-objective 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.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computer Science
Authors :
NameEmailORCID
Tian, Ye
Zhang, Tao
Xiao, Jinhua
Zhang, Xingyi
Jin, YaochuYaochu.Jin@surrey.ac.uk
Date : 18 June 2020
Uncontrolled Keywords : Constrained multi-objective optimization, evolutionary algorithm, coevolution, vehicle routing problem.
Additional Information : Embargo OK Metadata Pending
Depositing User : James Marshall
Date Deposited : 14 Jul 2020 15:25
Last Modified : 14 Jul 2020 15:25
URI: http://epubs.surrey.ac.uk/id/eprint/858209

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