Multiple source transfer learning for dynamic multiobjective optimization

Recently, dynamic multiobjective evolutionary algorithms (DMOEAs) with transfer learning have become popular for solving dynamic multiobjective optimization problems (DMOPs), as the used transfer learning methods in DMOEAs can effectively generate a good initial population for the new environment. H...

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Detalles Bibliográficos
Autores: Ye, Y., Lin, Q., Ma, L., Wong, K. C., 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:20.500.11824/1562
Acceso en línea:http://hdl.handle.net/20.500.11824/1562
Access Level:acceso abierto
Palabra clave:Domain adaptation
Dynamic optimization
Evolutionary algorithm
Multiobjective optimization
Transfer learning
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spelling Multiple source transfer learning for dynamic multiobjective optimizationYe, Y.Lin, Q.Ma, L.Wong, K. C.Gong, M.Coello, C.A.Domain adaptationDynamic optimizationEvolutionary algorithmMultiobjective optimizationTransfer learningRecently, dynamic multiobjective evolutionary algorithms (DMOEAs) with transfer learning have become popular for solving dynamic multiobjective optimization problems (DMOPs), as the used transfer learning methods in DMOEAs can effectively generate a good initial population for the new environment. However, most of them only transfer non-dominated solutions from the previous one or two environments, which cannot fully exploit all historical information and may easily induce negative transfer as only limited knowledge is available. To address this problem, this paper presents a multiple source transfer learning method for DMOEA, called MSTL-DMOEA, which runs two transfer learning procedures to fully exploit the historical information from all previous environments. First, to select some representative solutions for knowledge transfer, one clustering-based manifold transfer learning is run to cluster non-dominated solutions of the last environment to obtain their centroids, which are then fed into the manifold transfer learning model to predict the corresponding centroids for the new environment. After that, multiple source transfer learning is further run by using multisource TrAdaboost, which can fully exploit information from the above centroids in new environment and old centroids from all previous environments, aiming to construct a more accurate prediction model. This way, MSTL-DMOEA can predict an initial population with better quality for the new environment. The experimental results also validate the superiority of MSTL-DMOEA over several competitive state-of-the-art DMOEAs in solving various kinds of DMOPs.202320232022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/20.500.11824/1562reponame:BIRD. BCAM's Institutional Repository Datainstname:Basque Center for Applied Mathematics (BCAM)Ingléshttps://www.sciencedirect.com/science/article/abs/pii/S0020025522005527info: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:20.500.11824/15622026-06-19T12:47:47Z
dc.title.none.fl_str_mv Multiple source transfer learning for dynamic multiobjective optimization
title Multiple source transfer learning for dynamic multiobjective optimization
spellingShingle Multiple source transfer learning for dynamic multiobjective optimization
Ye, Y.
Domain adaptation
Dynamic optimization
Evolutionary algorithm
Multiobjective optimization
Transfer learning
title_short Multiple source transfer learning for dynamic multiobjective optimization
title_full Multiple source transfer learning for dynamic multiobjective optimization
title_fullStr Multiple source transfer learning for dynamic multiobjective optimization
title_full_unstemmed Multiple source transfer learning for dynamic multiobjective optimization
title_sort Multiple source transfer learning for dynamic multiobjective optimization
dc.creator.none.fl_str_mv Ye, Y.
Lin, Q.
Ma, L.
Wong, K. C.
Gong, M.
Coello, C.A.
author Ye, Y.
author_facet Ye, Y.
Lin, Q.
Ma, L.
Wong, K. C.
Gong, M.
Coello, C.A.
author_role author
author2 Lin, Q.
Ma, L.
Wong, K. C.
Gong, M.
Coello, C.A.
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Domain adaptation
Dynamic optimization
Evolutionary algorithm
Multiobjective optimization
Transfer learning
topic Domain adaptation
Dynamic optimization
Evolutionary algorithm
Multiobjective optimization
Transfer learning
description Recently, dynamic multiobjective evolutionary algorithms (DMOEAs) with transfer learning have become popular for solving dynamic multiobjective optimization problems (DMOPs), as the used transfer learning methods in DMOEAs can effectively generate a good initial population for the new environment. However, most of them only transfer non-dominated solutions from the previous one or two environments, which cannot fully exploit all historical information and may easily induce negative transfer as only limited knowledge is available. To address this problem, this paper presents a multiple source transfer learning method for DMOEA, called MSTL-DMOEA, which runs two transfer learning procedures to fully exploit the historical information from all previous environments. First, to select some representative solutions for knowledge transfer, one clustering-based manifold transfer learning is run to cluster non-dominated solutions of the last environment to obtain their centroids, which are then fed into the manifold transfer learning model to predict the corresponding centroids for the new environment. After that, multiple source transfer learning is further run by using multisource TrAdaboost, which can fully exploit information from the above centroids in new environment and old centroids from all previous environments, aiming to construct a more accurate prediction model. This way, MSTL-DMOEA can predict an initial population with better quality for the new environment. The experimental results also validate the superiority of MSTL-DMOEA over several competitive state-of-the-art DMOEAs in solving various kinds of DMOPs.
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/20.500.11824/1562
url http://hdl.handle.net/20.500.11824/1562
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://www.sciencedirect.com/science/article/abs/pii/S0020025522005527
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
repository.name.fl_str_mv
repository.mail.fl_str_mv
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