An Ensemble Surrogate-Based Framework for Expensive Multiobjective Evolutionary Optimization
Surrogate-assisted evolutionary algorithms (SAEAs) have become very popular for tackling computationally expensive multiobjective optimization problems (EMOPs), as the surrogate models in SAEAs can approximate EMOPs well, thereby reducing the time cost of the optimization process. However, with the...
| Autores: | ; ; ; ; ; |
|---|---|
| Tipo de documento: | Artigo |
| Estado: | Published version |
| Data de publicação: | 2022 |
| País: | España |
| Recursos: | Basque Center for Applied Mathematics (BCAM) |
| Repositório: | BIRD. BCAM's Institutional Repository Data |
| OAI Identifier: | oai:bird.bcamath.org:20.500.11824/1563 |
| Acesso em linha: | http://hdl.handle.net/20.500.11824/1563 |
| Nível de acesso: | Acesso aberto |
| Palavra-chave: | Ensemble surrogate evolutionary algorithms model management multiobjective optimization |
| _version_ | 1878741061543657472 |
|---|---|
| author | Lin, Q. |
| author2 | Wu, X. Ma, L. Li, J. Gong, M. Coello, C.A. |
| author2_role | author author author author author |
| author_browse | Coello, C.A. Gong, M. Li, J. Lin, Q. Ma, L. Wu, X. |
| author_facet | Lin, Q. Wu, X. Ma, L. Li, J. Gong, M. Coello, C.A. |
| author_role | author |
| collection | BIRD. BCAM's Institutional Repository Data |
| description | Surrogate-assisted evolutionary algorithms (SAEAs) have become very popular for tackling computationally expensive multiobjective optimization problems (EMOPs), as the surrogate models in SAEAs can approximate EMOPs well, thereby reducing the time cost of the optimization process. However, with the increased number of decision variables in EMOPs, the prediction accuracy of surrogate models will deteriorate, which inevitably worsens the performance of SAEAs. To deal with this issue, this article suggests an ensemble surrogate-based framework for tackling EMOPs. In this framework, a global surrogate model is trained under the entire search space to explore the global area, while a number of surrogate submodels are trained under different search subspaces to exploit the subarea, so as to enhance the prediction accuracy and reliability. Moreover, a new infill sampling criterion is designed based on a set of reference vectors to select promising samples for training the models. To validate the generality and effectiveness of our framework, three state-of-the-art evolutionary algorithms [nondominated sorting genetic algorithm III (NSGA-III), multiobjective evolutionary algorithm based on decomposition with differential evolution (MOEA/D-DE) and reference vector-guided evolutionary algorithm (RVEA)] are embedded, which significantly improve their performance for solving most of the test EMOPs adopted in this article. When compared to some competitive SAEAs for solving EMOPs with up to 30 decision variables, the experimental results also validate the advantages of our approach in most cases. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | ES_de5faec6f17cce2132d2fd1ddb0300c8 |
| institution | Basque Center for Applied Mathematics (BCAM) |
| instname_str | Basque Center for Applied Mathematics (BCAM) |
| network_acronym_str | ES |
| network_name_str | España |
| oai_identifier_str | oai:bird.bcamath.org:20.500.11824/1563 |
| publishDate | 2022 |
| publishDateSort | 2022 |
| reponame_str | BIRD. BCAM's Institutional Repository Data |
| spelling | An Ensemble Surrogate-Based Framework for Expensive Multiobjective Evolutionary Optimization Lin, Q. Wu, X. Ma, L. Li, J. Gong, M. Coello, C.A. Ensemble surrogate evolutionary algorithms model management multiobjective optimization Surrogate-assisted evolutionary algorithms (SAEAs) have become very popular for tackling computationally expensive multiobjective optimization problems (EMOPs), as the surrogate models in SAEAs can approximate EMOPs well, thereby reducing the time cost of the optimization process. However, with the increased number of decision variables in EMOPs, the prediction accuracy of surrogate models will deteriorate, which inevitably worsens the performance of SAEAs. To deal with this issue, this article suggests an ensemble surrogate-based framework for tackling EMOPs. In this framework, a global surrogate model is trained under the entire search space to explore the global area, while a number of surrogate submodels are trained under different search subspaces to exploit the subarea, so as to enhance the prediction accuracy and reliability. Moreover, a new infill sampling criterion is designed based on a set of reference vectors to select promising samples for training the models. To validate the generality and effectiveness of our framework, three state-of-the-art evolutionary algorithms [nondominated sorting genetic algorithm III (NSGA-III), multiobjective evolutionary algorithm based on decomposition with differential evolution (MOEA/D-DE) and reference vector-guided evolutionary algorithm (RVEA)] are embedded, which significantly improve their performance for solving most of the test EMOPs adopted in this article. When compared to some competitive SAEAs for solving EMOPs with up to 30 decision variables, the experimental results also validate the advantages of our approach in most cases. http://hdl.handle.net/20.500.11824/1563 |
| spellingShingle | An Ensemble Surrogate-Based Framework for Expensive Multiobjective Evolutionary Optimization Lin, Q. Ensemble surrogate evolutionary algorithms model management multiobjective optimization |
| status_str | publishedVersion |
| title | An Ensemble Surrogate-Based Framework for Expensive Multiobjective Evolutionary Optimization |
| title_full | An Ensemble Surrogate-Based Framework for Expensive Multiobjective Evolutionary Optimization |
| title_fullStr | An Ensemble Surrogate-Based Framework for Expensive Multiobjective Evolutionary Optimization |
| title_full_unstemmed | An Ensemble Surrogate-Based Framework for Expensive Multiobjective Evolutionary Optimization |
| title_short | An Ensemble Surrogate-Based Framework for Expensive Multiobjective Evolutionary Optimization |
| title_sort | An Ensemble Surrogate-Based Framework for Expensive Multiobjective Evolutionary Optimization |
| topic | Ensemble surrogate evolutionary algorithms model management multiobjective optimization |
| topic_facet | Ensemble surrogate evolutionary algorithms model management multiobjective optimization |
| url | http://hdl.handle.net/20.500.11824/1563 |