Expensive Multiobjective Optimization Algorithm Based on Equivariate Component Analysis
In order to reduce the cost of candidate solution evaluation in the process of solving expensive optimization problems, an expensive multi-objective optimization algorithm based on equivalence component analysis was proposed to study the influence of decision space equivalence components on the pred...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9815070/ |
| _version_ | 1798040429325713408 |
|---|---|
| author | Li Wenbin Chai Zi'an Gang Liu |
| author_facet | Li Wenbin Chai Zi'an Gang Liu |
| author_sort | Li Wenbin |
| collection | DOAJ |
| description | In order to reduce the cost of candidate solution evaluation in the process of solving expensive optimization problems, an expensive multi-objective optimization algorithm based on equivalence component analysis was proposed to study the influence of decision space equivalence components on the prediction accuracy of agent models. Based on the analysis of the equivalence of decision space attributes, a limit learning network based on the equivalence components was constructed for Pareto dominance prediction among candidate solutions. A multi-objective test problem with equivalent components was selected in Pareto dominance prediction experiments, the results of which showed that the algorithm can effectively improve the accuracy of Pareto dominance prediction among candidate solutions. Successively the candidate solutions were scored with multiple ELM (Extreme Learning Machine) models, selected for evaluation and updated, and integrated into the Pareto-based multi-objective evolutionary algorithm. Through comparative experiments on the test problem, the method could achieve a better Pareto approximation solution under the limitation of a limited number of evaluations, and the goal of reducing the cost of expensive multi-objective optimization calculations. |
| first_indexed | 2024-04-11T22:07:30Z |
| format | Article |
| id | doaj.art-d815c41af91a4964a3de9ad9ddb60d2b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T22:07:30Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d815c41af91a4964a3de9ad9ddb60d2b2022-12-22T04:00:40ZengIEEEIEEE Access2169-35362022-01-0110738357384610.1109/ACCESS.2022.31882489815070Expensive Multiobjective Optimization Algorithm Based on Equivariate Component AnalysisLi Wenbin0https://orcid.org/0000-0002-2317-3495Chai Zi'an1https://orcid.org/0000-0001-6409-6935Gang Liu2School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, ChinaSchool of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, ChinaSchool of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, ChinaIn order to reduce the cost of candidate solution evaluation in the process of solving expensive optimization problems, an expensive multi-objective optimization algorithm based on equivalence component analysis was proposed to study the influence of decision space equivalence components on the prediction accuracy of agent models. Based on the analysis of the equivalence of decision space attributes, a limit learning network based on the equivalence components was constructed for Pareto dominance prediction among candidate solutions. A multi-objective test problem with equivalent components was selected in Pareto dominance prediction experiments, the results of which showed that the algorithm can effectively improve the accuracy of Pareto dominance prediction among candidate solutions. Successively the candidate solutions were scored with multiple ELM (Extreme Learning Machine) models, selected for evaluation and updated, and integrated into the Pareto-based multi-objective evolutionary algorithm. Through comparative experiments on the test problem, the method could achieve a better Pareto approximation solution under the limitation of a limited number of evaluations, and the goal of reducing the cost of expensive multi-objective optimization calculations.https://ieeexplore.ieee.org/document/9815070/Expensive multi-objective optimizationequivalent componentsPareto dominancesurrogate model |
| spellingShingle | Li Wenbin Chai Zi'an Gang Liu Expensive Multiobjective Optimization Algorithm Based on Equivariate Component Analysis IEEE Access Expensive multi-objective optimization equivalent components Pareto dominance surrogate model |
| title | Expensive Multiobjective Optimization Algorithm Based on Equivariate Component Analysis |
| title_full | Expensive Multiobjective Optimization Algorithm Based on Equivariate Component Analysis |
| title_fullStr | Expensive Multiobjective Optimization Algorithm Based on Equivariate Component Analysis |
| title_full_unstemmed | Expensive Multiobjective Optimization Algorithm Based on Equivariate Component Analysis |
| title_short | Expensive Multiobjective Optimization Algorithm Based on Equivariate Component Analysis |
| title_sort | expensive multiobjective optimization algorithm based on equivariate component analysis |
| topic | Expensive multi-objective optimization equivalent components Pareto dominance surrogate model |
| url | https://ieeexplore.ieee.org/document/9815070/ |
| work_keys_str_mv | AT liwenbin expensivemultiobjectiveoptimizationalgorithmbasedonequivariatecomponentanalysis AT chaizian expensivemultiobjectiveoptimizationalgorithmbasedonequivariatecomponentanalysis AT gangliu expensivemultiobjectiveoptimizationalgorithmbasedonequivariatecomponentanalysis |