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...

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Detalles Bibliográficos
Autores: Lin, Q., Wu, X., Ma, L., Li, J., Gong, M., Coello, C.A.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Basque Center for Applied Mathematics (BCAM)
Repositorio:BIRD. BCAM's Institutional Repository Data
OAI Identifier:oai:bird.bcamath.org:20.500.11824/1563
Acceso en línea:http://hdl.handle.net/20.500.11824/1563
Access Level:acceso abierto
Palabra clave:Ensemble surrogate
evolutionary algorithms
model management
multiobjective optimization
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spelling An Ensemble Surrogate-Based Framework for Expensive Multiobjective Evolutionary OptimizationLin, Q.Wu, X.Ma, L.Li, J.Gong, M.Coello, C.A.Ensemble surrogateevolutionary algorithmsmodel managementmultiobjective optimizationSurrogate-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.202320232022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/20.500.11824/1563reponame:BIRD. BCAM's Institutional Repository Datainstname:Basque Center for Applied Mathematics (BCAM)Ingléshttps://ieeexplore.ieee.org/document/9509584info:eu-repo/grantAgreement/Gobierno Vasco/BERC/BERC.2018-2021Reconocimiento-NoComercial-CompartirIgual 3.0 Españahttp://creativecommons.org/licenses/by-nc-sa/3.0/es/info:eu-repo/semantics/openAccessoai:bird.bcamath.org:20.500.11824/15632026-06-19T12:47:47Z
dc.title.none.fl_str_mv An Ensemble Surrogate-Based Framework for Expensive Multiobjective Evolutionary Optimization
title An Ensemble Surrogate-Based Framework for Expensive Multiobjective Evolutionary Optimization
spellingShingle An Ensemble Surrogate-Based Framework for Expensive Multiobjective Evolutionary Optimization
Lin, Q.
Ensemble surrogate
evolutionary algorithms
model management
multiobjective optimization
title_short 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_sort An Ensemble Surrogate-Based Framework for Expensive Multiobjective Evolutionary Optimization
dc.creator.none.fl_str_mv Lin, Q.
Wu, X.
Ma, L.
Li, J.
Gong, M.
Coello, C.A.
author Lin, Q.
author_facet Lin, Q.
Wu, X.
Ma, L.
Li, J.
Gong, M.
Coello, C.A.
author_role author
author2 Wu, X.
Ma, L.
Li, J.
Gong, M.
Coello, C.A.
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Ensemble surrogate
evolutionary algorithms
model management
multiobjective optimization
topic Ensemble surrogate
evolutionary algorithms
model management
multiobjective optimization
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.
publishDate 2022
dc.date.none.fl_str_mv 2022
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.11824/1563
url http://hdl.handle.net/20.500.11824/1563
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://ieeexplore.ieee.org/document/9509584
info:eu-repo/grantAgreement/Gobierno Vasco/BERC/BERC.2018-2021
dc.rights.none.fl_str_mv Reconocimiento-NoComercial-CompartirIgual 3.0 España
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Reconocimiento-NoComercial-CompartirIgual 3.0 España
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:BIRD. BCAM's Institutional Repository Data
instname:Basque Center for Applied Mathematics (BCAM)
instname_str Basque Center for Applied Mathematics (BCAM)
reponame_str BIRD. BCAM's Institutional Repository Data
collection BIRD. BCAM's Institutional Repository Data
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