Incorporation of region of interest in a decomposition-based multi-objective evolutionary algorithm

Detalhes bibliográficos
Autor(a) principal: Reinaldo Meneghini, Ivan
Data de Publicação: 2021
Outros Autores: Gadelha Guimarães, Frederico, Gaspar-Cunha, A., Weiss Cohen, Miri
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/1822/68643
Resumo: Preference-based Multi-Objective Evolutionary Algorithm (MOEA) restrict the search to a given region of the Pareto front preferred by the Decision Maker (DM), called the Region of Interest (ROI). In this paper, a new preference-guided MOEA is proposed. In this method, we define the ROI as a preference cone in the objective space. The preferential direction and the aperture of the cone are parameters that the DM has to provide to define the ROI. Given the preference cone, we employ a weight vector generation method that is based on a steady-state evolutionary algorithm. The main idea of our method is to evolve a population of weight vectors towards the characteristics that are desirable for a set of weight vectors in a decomposition-based MOEA framework. The main advantage is that the DM can define the number of weight vectors and thus can control the population size. Once the ROI is defined and the set of weight vectors are generated within the preference cone, we start a decomposition-based MOEA using the provided set of weights in its initialization. Therefore, this enforces the algorithm to converge to the ROI. The results show the benefit and adequacy of the preference cone MOEA/D for preference-guided many-objective optimization.
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spelling Incorporation of region of interest in a decomposition-based multi-objective evolutionary algorithmMOEA/DMulti-Objective OptimizationROIWeight VectorsCiências Naturais::Ciências da Computação e da InformaçãoPreference-based Multi-Objective Evolutionary Algorithm (MOEA) restrict the search to a given region of the Pareto front preferred by the Decision Maker (DM), called the Region of Interest (ROI). In this paper, a new preference-guided MOEA is proposed. In this method, we define the ROI as a preference cone in the objective space. The preferential direction and the aperture of the cone are parameters that the DM has to provide to define the ROI. Given the preference cone, we employ a weight vector generation method that is based on a steady-state evolutionary algorithm. The main idea of our method is to evolve a population of weight vectors towards the characteristics that are desirable for a set of weight vectors in a decomposition-based MOEA framework. The main advantage is that the DM can define the number of weight vectors and thus can control the population size. Once the ROI is defined and the set of weight vectors are generated within the preference cone, we start a decomposition-based MOEA using the provided set of weights in its initialization. Therefore, this enforces the algorithm to converge to the ROI. The results show the benefit and adequacy of the preference cone MOEA/D for preference-guided many-objective optimization.This work was supported by the Brazilian funding agencies CAPES and CNPq.SpringerUniversidade do MinhoReinaldo Meneghini, IvanGadelha Guimarães, FredericoGaspar-Cunha, A.Weiss Cohen, Miri20212021-01-01T00:00:00Zbook partinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/1822/68643engReinaldo Meneghini I., Gadelha Guimarães F., Gaspar-Cunha A., Weiss Cohen M. (2021) Incorporation of Region of Interest in a Decomposition-Based Multi-objective Evolutionary Algorithm. In: Gaspar-Cunha A., Periaux J., Giannakoglou K.C., Gauger N.R., Quagliarella D., Greiner D. (eds) Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences. Computational Methods in Applied Sciences, vol 55. Springer, Cham. https://doi.org/10.1007/978-3-030-57422-2_3978-3-030-57421-51871-303310.1007/978-3-030-57422-2_3978-3-030-57422-2https://link.springer.com/chapter/10.1007%2F978-3-030-57422-2_3info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-05-11T06:16:06Zoai:repositorium.sdum.uminho.pt:1822/68643Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-11T06:16:06Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Incorporation of region of interest in a decomposition-based multi-objective evolutionary algorithm
title Incorporation of region of interest in a decomposition-based multi-objective evolutionary algorithm
spellingShingle Incorporation of region of interest in a decomposition-based multi-objective evolutionary algorithm
Reinaldo Meneghini, Ivan
MOEA/D
Multi-Objective Optimization
ROI
Weight Vectors
Ciências Naturais::Ciências da Computação e da Informação
title_short Incorporation of region of interest in a decomposition-based multi-objective evolutionary algorithm
title_full Incorporation of region of interest in a decomposition-based multi-objective evolutionary algorithm
title_fullStr Incorporation of region of interest in a decomposition-based multi-objective evolutionary algorithm
title_full_unstemmed Incorporation of region of interest in a decomposition-based multi-objective evolutionary algorithm
title_sort Incorporation of region of interest in a decomposition-based multi-objective evolutionary algorithm
author Reinaldo Meneghini, Ivan
author_facet Reinaldo Meneghini, Ivan
Gadelha Guimarães, Frederico
Gaspar-Cunha, A.
Weiss Cohen, Miri
author_role author
author2 Gadelha Guimarães, Frederico
Gaspar-Cunha, A.
Weiss Cohen, Miri
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Reinaldo Meneghini, Ivan
Gadelha Guimarães, Frederico
Gaspar-Cunha, A.
Weiss Cohen, Miri
dc.subject.por.fl_str_mv MOEA/D
Multi-Objective Optimization
ROI
Weight Vectors
Ciências Naturais::Ciências da Computação e da Informação
topic MOEA/D
Multi-Objective Optimization
ROI
Weight Vectors
Ciências Naturais::Ciências da Computação e da Informação
description Preference-based Multi-Objective Evolutionary Algorithm (MOEA) restrict the search to a given region of the Pareto front preferred by the Decision Maker (DM), called the Region of Interest (ROI). In this paper, a new preference-guided MOEA is proposed. In this method, we define the ROI as a preference cone in the objective space. The preferential direction and the aperture of the cone are parameters that the DM has to provide to define the ROI. Given the preference cone, we employ a weight vector generation method that is based on a steady-state evolutionary algorithm. The main idea of our method is to evolve a population of weight vectors towards the characteristics that are desirable for a set of weight vectors in a decomposition-based MOEA framework. The main advantage is that the DM can define the number of weight vectors and thus can control the population size. Once the ROI is defined and the set of weight vectors are generated within the preference cone, we start a decomposition-based MOEA using the provided set of weights in its initialization. Therefore, this enforces the algorithm to converge to the ROI. The results show the benefit and adequacy of the preference cone MOEA/D for preference-guided many-objective optimization.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-01-01T00:00:00Z
dc.type.driver.fl_str_mv book part
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/1822/68643
url http://hdl.handle.net/1822/68643
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Reinaldo Meneghini I., Gadelha Guimarães F., Gaspar-Cunha A., Weiss Cohen M. (2021) Incorporation of Region of Interest in a Decomposition-Based Multi-objective Evolutionary Algorithm. In: Gaspar-Cunha A., Periaux J., Giannakoglou K.C., Gauger N.R., Quagliarella D., Greiner D. (eds) Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences. Computational Methods in Applied Sciences, vol 55. Springer, Cham. https://doi.org/10.1007/978-3-030-57422-2_3
978-3-030-57421-5
1871-3033
10.1007/978-3-030-57422-2_3
978-3-030-57422-2
https://link.springer.com/chapter/10.1007%2F978-3-030-57422-2_3
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv mluisa.alvim@gmail.com
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