Surrogate-Assisted Evolutionary Algorithms with Adaptive Constraint Evaluation
Many real-world optimization problems have not only expensive objectives but also expensive constraints. However, most existing surrogate-assisted evolutionary algorithms (SAEAs) evaluate all constraints of the candidates. With limited number of evaluations, it is wasteful to constantly evaluate con...
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2023-06-01
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| Series: | Jisuanji kexue yu tansuo |
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| Online Access: | http://fcst.ceaj.org/fileup/1673-9418/PDF/2203078.pdf |
| _version_ | 1797809625804832768 |
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| author | WEI Fengfeng, CHEN Weineng |
| author_facet | WEI Fengfeng, CHEN Weineng |
| author_sort | WEI Fengfeng, CHEN Weineng |
| collection | DOAJ |
| description | Many real-world optimization problems have not only expensive objectives but also expensive constraints. However, most existing surrogate-assisted evolutionary algorithms (SAEAs) evaluate all constraints of the candidates. With limited number of evaluations, it is wasteful to constantly evaluate constraints whose feasible area is large. To solve this problem, this paper studies SAEAs for expensive constrained optimization problems, and proposes an adaptive constraint evaluation strategy. It can adaptively evaluate constraints with less feasible information according to the population evolution, saving expensive evaluations wasted on constraints with more feasible information. The algorithm can adaptively select and evaluate constraints within limited budget of expensive evaluation to better evolve the population. To verify the effectiveness and scalability of this strategy, this paper designs two Gaussian regression model-assisted differential evolution algorithms cooperated with the adaptive constraint evaluation strategy. Experiments demonstrate the proposed SAEAs perform better in 11 out of 15 problems. Besides, they can achieve more than 94% efficiency improvement with the time delay on evaluations. Specifically, the efficiency improvement is larger than 98% in 91.67% test cases. Experiments in 4 engineering optimization problems demonstrate that SAEAs with the adaptive constraint evaluation strategy have promising applications in real-world optimization problems. |
| first_indexed | 2024-03-13T06:55:29Z |
| format | Article |
| id | doaj.art-0dbeae556777459fabcc12f4953eda28 |
| institution | Directory Open Access Journal |
| issn | 1673-9418 |
| language | zho |
| last_indexed | 2024-03-13T06:55:29Z |
| publishDate | 2023-06-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj.art-0dbeae556777459fabcc12f4953eda282023-06-07T07:58:32ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182023-06-011761301132010.3778/j.issn.1673-9418.2203078Surrogate-Assisted Evolutionary Algorithms with Adaptive Constraint EvaluationWEI Fengfeng, CHEN Weineng01. School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China 2. Ministry of Education Key Laboratory for Big Data and Intelligent Robot, South China University of Technology, Guangzhou 510006, ChinaMany real-world optimization problems have not only expensive objectives but also expensive constraints. However, most existing surrogate-assisted evolutionary algorithms (SAEAs) evaluate all constraints of the candidates. With limited number of evaluations, it is wasteful to constantly evaluate constraints whose feasible area is large. To solve this problem, this paper studies SAEAs for expensive constrained optimization problems, and proposes an adaptive constraint evaluation strategy. It can adaptively evaluate constraints with less feasible information according to the population evolution, saving expensive evaluations wasted on constraints with more feasible information. The algorithm can adaptively select and evaluate constraints within limited budget of expensive evaluation to better evolve the population. To verify the effectiveness and scalability of this strategy, this paper designs two Gaussian regression model-assisted differential evolution algorithms cooperated with the adaptive constraint evaluation strategy. Experiments demonstrate the proposed SAEAs perform better in 11 out of 15 problems. Besides, they can achieve more than 94% efficiency improvement with the time delay on evaluations. Specifically, the efficiency improvement is larger than 98% in 91.67% test cases. Experiments in 4 engineering optimization problems demonstrate that SAEAs with the adaptive constraint evaluation strategy have promising applications in real-world optimization problems.http://fcst.ceaj.org/fileup/1673-9418/PDF/2203078.pdfsurrogate model; differential evolution algorithm; expensive constrained optimization; adaptive constraint evaluation strategy |
| spellingShingle | WEI Fengfeng, CHEN Weineng Surrogate-Assisted Evolutionary Algorithms with Adaptive Constraint Evaluation Jisuanji kexue yu tansuo surrogate model; differential evolution algorithm; expensive constrained optimization; adaptive constraint evaluation strategy |
| title | Surrogate-Assisted Evolutionary Algorithms with Adaptive Constraint Evaluation |
| title_full | Surrogate-Assisted Evolutionary Algorithms with Adaptive Constraint Evaluation |
| title_fullStr | Surrogate-Assisted Evolutionary Algorithms with Adaptive Constraint Evaluation |
| title_full_unstemmed | Surrogate-Assisted Evolutionary Algorithms with Adaptive Constraint Evaluation |
| title_short | Surrogate-Assisted Evolutionary Algorithms with Adaptive Constraint Evaluation |
| title_sort | surrogate assisted evolutionary algorithms with adaptive constraint evaluation |
| topic | surrogate model; differential evolution algorithm; expensive constrained optimization; adaptive constraint evaluation strategy |
| url | http://fcst.ceaj.org/fileup/1673-9418/PDF/2203078.pdf |
| work_keys_str_mv | AT weifengfengchenweineng surrogateassistedevolutionaryalgorithmswithadaptiveconstraintevaluation |