A two-stage adaptive penalty method based on co-evolution for constrained evolutionary optimization
Abstract Penalty function method is popular for constrained evolutionary optimization. However, it is non-trivial to set a proper penalty factor for a constrained optimization problem. This paper takes advantage of co-evolution to adjust the penalty factor and proposes a two-stage adaptive penalty m...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2023-01-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-022-00965-6 |
| _version_ | 1797769229573816320 |
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| author | Bing-Chuan Wang Jing-Jing Guo Pei-Qiu Huang Xian-Bing Meng |
| author_facet | Bing-Chuan Wang Jing-Jing Guo Pei-Qiu Huang Xian-Bing Meng |
| author_sort | Bing-Chuan Wang |
| collection | DOAJ |
| description | Abstract Penalty function method is popular for constrained evolutionary optimization. However, it is non-trivial to set a proper penalty factor for a constrained optimization problem. This paper takes advantage of co-evolution to adjust the penalty factor and proposes a two-stage adaptive penalty method. In the co-evolution stage, the population is divided into multiple subpopulations, each of which is associated with a penalty factor. Through the co-evolution of these subpopulations, the performance of penalty factors can be evaluated. Since different penalty factors are used, the subpopulations will evolve along different directions. Thus, exploration can be enhanced. In the shuffle stage, all subpopulations are merged into a population and the best penalty factor from the co-evolution stage is used to guide the evolution. In this manner, the information interaction among subpopulations can be facilitated; thus, exploitation can be promoted. By executing these two stages iteratively, the feasible optimum could be obtained finally. In the two-stage evolutionary process, the search algorithm is designed based on two trial vector generation strategies of differential evolution. Additionally, a restart mechanism is designed to help the population avoid stagnating in the infeasible region. Extensive experiments demonstrate the effectiveness of the proposed method. |
| first_indexed | 2024-03-12T21:05:55Z |
| format | Article |
| id | doaj.art-4837ed8cad5a4159a7e8cf7a5bb03e86 |
| institution | Directory Open Access Journal |
| issn | 2199-4536 2198-6053 |
| language | English |
| last_indexed | 2024-03-12T21:05:55Z |
| publishDate | 2023-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj.art-4837ed8cad5a4159a7e8cf7a5bb03e862023-07-30T11:27:53ZengSpringerComplex & Intelligent Systems2199-45362198-60532023-01-01944615462710.1007/s40747-022-00965-6A two-stage adaptive penalty method based on co-evolution for constrained evolutionary optimizationBing-Chuan Wang0Jing-Jing Guo1Pei-Qiu Huang2Xian-Bing Meng3School of Automation, Central South UniversitySchool of Automation, Central South UniversitySchool of Automation, Central South UniversitySchool of Electromechanical Engineering, Guangdong University of TechnologyAbstract Penalty function method is popular for constrained evolutionary optimization. However, it is non-trivial to set a proper penalty factor for a constrained optimization problem. This paper takes advantage of co-evolution to adjust the penalty factor and proposes a two-stage adaptive penalty method. In the co-evolution stage, the population is divided into multiple subpopulations, each of which is associated with a penalty factor. Through the co-evolution of these subpopulations, the performance of penalty factors can be evaluated. Since different penalty factors are used, the subpopulations will evolve along different directions. Thus, exploration can be enhanced. In the shuffle stage, all subpopulations are merged into a population and the best penalty factor from the co-evolution stage is used to guide the evolution. In this manner, the information interaction among subpopulations can be facilitated; thus, exploitation can be promoted. By executing these two stages iteratively, the feasible optimum could be obtained finally. In the two-stage evolutionary process, the search algorithm is designed based on two trial vector generation strategies of differential evolution. Additionally, a restart mechanism is designed to help the population avoid stagnating in the infeasible region. Extensive experiments demonstrate the effectiveness of the proposed method.https://doi.org/10.1007/s40747-022-00965-6Constrained evolutionary optimizationPenalty functionCo-evolutionSubpopulationShuffle |
| spellingShingle | Bing-Chuan Wang Jing-Jing Guo Pei-Qiu Huang Xian-Bing Meng A two-stage adaptive penalty method based on co-evolution for constrained evolutionary optimization Complex & Intelligent Systems Constrained evolutionary optimization Penalty function Co-evolution Subpopulation Shuffle |
| title | A two-stage adaptive penalty method based on co-evolution for constrained evolutionary optimization |
| title_full | A two-stage adaptive penalty method based on co-evolution for constrained evolutionary optimization |
| title_fullStr | A two-stage adaptive penalty method based on co-evolution for constrained evolutionary optimization |
| title_full_unstemmed | A two-stage adaptive penalty method based on co-evolution for constrained evolutionary optimization |
| title_short | A two-stage adaptive penalty method based on co-evolution for constrained evolutionary optimization |
| title_sort | two stage adaptive penalty method based on co evolution for constrained evolutionary optimization |
| topic | Constrained evolutionary optimization Penalty function Co-evolution Subpopulation Shuffle |
| url | https://doi.org/10.1007/s40747-022-00965-6 |
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