A new adaptive decomposition-based evolutionary algorithm for multi- and many-objective optimization

•An adaptive decomposition approach is proposed to guide the evolution process.•A structured metric is designed to assess the quality of the candidate solutions.•The structured metric performs differently on different rank fronts.•Once a weight vector is generated, the sub-objective space is divided...

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Bibliographic Details
Published in:Expert systems with applications Vol. 213; p. 119080
Main Authors: Bao, Chunteng, Gao, Diju, Gu, Wei, Xu, Lihong, D.Goodman, Erik
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.03.2023
Subjects:
Adaptive decomposition
Many-objective optimization
Multi-objective evolutionary algorithm
Pareto front
Pareto front
Many-objective optimization
Multi-objective evolutionary algorithm
Adaptive decomposition
ISSN:0957-4174, 1873-6793
Online Access:Get full text
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Summary:•An adaptive decomposition approach is proposed to guide the evolution process.•A structured metric is designed to assess the quality of the candidate solutions.•The structured metric performs differently on different rank fronts.•Once a weight vector is generated, the sub-objective space is divided into two ones. In decomposition-based multi-objective evolutionary algorithms (MOEAs), a set of uniformly distributed reference vectors (RVs) is usually adopted to decompose a multi-objective optimization problem (MOP) into several single-objective sub-problems, and the RVs are fixed during evolution. When it comes to multi-objective optimization problems (MOPs) with complex Pareto fronts (PFs), the effectiveness of the multi-objective evolutionary algorithm (MOEA) may degrade. To solve this problem, this article proposes an adaptive decomposition-based evolutionary algorithm (ADEA) for both multi- and many-objective optimization. In ADEA, the candidate solutions themselves are used as RVs, so that the RVs can be automatically adjusted to the shape of the Pareto front (PF). Also, the RVs are successively generated one by one, and once a reference vector (RV) is generated, the corresponding sub-objective space is dynamically decomposed into two sub-spaces. Moreover, a variable metric is proposed and merged with the proposed adaptive decomposition approach to assist the selection operation in evolutionary many-objective optimization (EMO). The effectiveness of ADEA is compared with several state-of-the-art MOEAs on a variety of benchmark MOPs with up to 15 objectives. The empirical results demonstrate that ADEA has competitive performance on most of the MOPs used in this study.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2022.119080