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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| Vydáno v: | Expert systems with applications Ročník 213; s. 119080 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
01.03.2023
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| Témata: | |
| ISSN: | 0957-4174, 1873-6793 |
| On-line přístup: | Získat plný text |
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| Abstract | •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. |
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| AbstractList | •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. |
| ArticleNumber | 119080 |
| Author | Gao, Diju D.Goodman, Erik Gu, Wei Bao, Chunteng Xu, Lihong |
| Author_xml | – sequence: 1 givenname: Chunteng surname: Bao fullname: Bao, Chunteng email: jiuzhe321@163.com organization: Key Laboratory Marine Technology and Control Engineering, Ministry of Transport, Shanghai Maritime University, Shanghai, 201306, China – sequence: 2 givenname: Diju surname: Gao fullname: Gao, Diju email: djgao@shmtu.edu.cn organization: Key Laboratory Marine Technology and Control Engineering, Ministry of Transport, Shanghai Maritime University, Shanghai, 201306, China – sequence: 3 givenname: Wei surname: Gu fullname: Gu, Wei email: weigu@shmtu.edu.cn organization: Key Laboratory Marine Technology and Control Engineering, Ministry of Transport, Shanghai Maritime University, Shanghai, 201306, China – sequence: 4 givenname: Lihong surname: Xu fullname: Xu, Lihong email: xulhk@163.com organization: College of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China – sequence: 5 givenname: Erik surname: D.Goodman fullname: D.Goodman, Erik email: goodman@egr.msu.edu organization: BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI 48824, USA |
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| Keywords | Pareto front Many-objective optimization Multi-objective evolutionary algorithm Adaptive decomposition |
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| SubjectTerms | Adaptive decomposition Many-objective optimization Multi-objective evolutionary algorithm Pareto front |
| Title | A new adaptive decomposition-based evolutionary algorithm for multi- and many-objective optimization |
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