A novel hybrid multi-objective metamodel-based evolutionary optimization algorithm

Autores
Baquela, Enrique Gabriel; Olivera, Ana Carolina
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Optimization via Simulation (OvS) is an useful optimization tool to find a solution to an optimization problem that is difficult to model analytically. OvS consists in evaluating potential solutions through simulation executions; however, its high computational cost is a factor that can make its implementation infeasible. This issue also occurs in multi-objective problems, which tend to be expensive to solve. In this work, we present a new hybrid multi-objective OvS algorithm, which uses Kriging-type metamodels to estimate the simulations results and a multi-objective evolutionary algorithm to manage the optimization process. Our proposal succeeds in reducing the computational cost significantly without affecting the quality of the results obtained. The evolutionary part of the hybrid algorithm is based on the popular NSGA-II. The hybrid method is compared to the canonical NSGA-II and other hybrid approaches, showing a good performance not only in the quality of the solutions but also as computational cost saving.
Fil: Baquela, Enrique Gabriel. Universidad Tecnológica Nacional. Facultad Regional San Nicolás; Argentina
Fil: Olivera, Ana Carolina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
KRIGING
METAMODEL
MULTI-OBJECTIVE OPTIMIZATION
NSGA-II
OPTIMIZATION VIA SIMULATION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/124621
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A novel hybrid multi-objective metamodel-based evolutionary optimization algorithmBaquela, Enrique GabrielOlivera, Ana CarolinaKRIGINGMETAMODELMULTI-OBJECTIVE OPTIMIZATIONNSGA-IIOPTIMIZATION VIA SIMULATIONhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Optimization via Simulation (OvS) is an useful optimization tool to find a solution to an optimization problem that is difficult to model analytically. OvS consists in evaluating potential solutions through simulation executions; however, its high computational cost is a factor that can make its implementation infeasible. This issue also occurs in multi-objective problems, which tend to be expensive to solve. In this work, we present a new hybrid multi-objective OvS algorithm, which uses Kriging-type metamodels to estimate the simulations results and a multi-objective evolutionary algorithm to manage the optimization process. Our proposal succeeds in reducing the computational cost significantly without affecting the quality of the results obtained. The evolutionary part of the hybrid algorithm is based on the popular NSGA-II. The hybrid method is compared to the canonical NSGA-II and other hybrid approaches, showing a good performance not only in the quality of the solutions but also as computational cost saving.Fil: Baquela, Enrique Gabriel. Universidad Tecnológica Nacional. Facultad Regional San Nicolás; ArgentinaFil: Olivera, Ana Carolina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaElsevier2019-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/124621Baquela, Enrique Gabriel; Olivera, Ana Carolina; A novel hybrid multi-objective metamodel-based evolutionary optimization algorithm; Elsevier; Operations Research Perspectives; 6; 100098; 1-2019; 1-142214-7160CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S221471601830068Xinfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.orp.2019.100098info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:58:32Zoai:ri.conicet.gov.ar:11336/124621instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:58:32.867CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A novel hybrid multi-objective metamodel-based evolutionary optimization algorithm
title A novel hybrid multi-objective metamodel-based evolutionary optimization algorithm
spellingShingle A novel hybrid multi-objective metamodel-based evolutionary optimization algorithm
Baquela, Enrique Gabriel
KRIGING
METAMODEL
MULTI-OBJECTIVE OPTIMIZATION
NSGA-II
OPTIMIZATION VIA SIMULATION
title_short A novel hybrid multi-objective metamodel-based evolutionary optimization algorithm
title_full A novel hybrid multi-objective metamodel-based evolutionary optimization algorithm
title_fullStr A novel hybrid multi-objective metamodel-based evolutionary optimization algorithm
title_full_unstemmed A novel hybrid multi-objective metamodel-based evolutionary optimization algorithm
title_sort A novel hybrid multi-objective metamodel-based evolutionary optimization algorithm
dc.creator.none.fl_str_mv Baquela, Enrique Gabriel
Olivera, Ana Carolina
author Baquela, Enrique Gabriel
author_facet Baquela, Enrique Gabriel
Olivera, Ana Carolina
author_role author
author2 Olivera, Ana Carolina
author2_role author
dc.subject.none.fl_str_mv KRIGING
METAMODEL
MULTI-OBJECTIVE OPTIMIZATION
NSGA-II
OPTIMIZATION VIA SIMULATION
topic KRIGING
METAMODEL
MULTI-OBJECTIVE OPTIMIZATION
NSGA-II
OPTIMIZATION VIA SIMULATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Optimization via Simulation (OvS) is an useful optimization tool to find a solution to an optimization problem that is difficult to model analytically. OvS consists in evaluating potential solutions through simulation executions; however, its high computational cost is a factor that can make its implementation infeasible. This issue also occurs in multi-objective problems, which tend to be expensive to solve. In this work, we present a new hybrid multi-objective OvS algorithm, which uses Kriging-type metamodels to estimate the simulations results and a multi-objective evolutionary algorithm to manage the optimization process. Our proposal succeeds in reducing the computational cost significantly without affecting the quality of the results obtained. The evolutionary part of the hybrid algorithm is based on the popular NSGA-II. The hybrid method is compared to the canonical NSGA-II and other hybrid approaches, showing a good performance not only in the quality of the solutions but also as computational cost saving.
Fil: Baquela, Enrique Gabriel. Universidad Tecnológica Nacional. Facultad Regional San Nicolás; Argentina
Fil: Olivera, Ana Carolina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description Optimization via Simulation (OvS) is an useful optimization tool to find a solution to an optimization problem that is difficult to model analytically. OvS consists in evaluating potential solutions through simulation executions; however, its high computational cost is a factor that can make its implementation infeasible. This issue also occurs in multi-objective problems, which tend to be expensive to solve. In this work, we present a new hybrid multi-objective OvS algorithm, which uses Kriging-type metamodels to estimate the simulations results and a multi-objective evolutionary algorithm to manage the optimization process. Our proposal succeeds in reducing the computational cost significantly without affecting the quality of the results obtained. The evolutionary part of the hybrid algorithm is based on the popular NSGA-II. The hybrid method is compared to the canonical NSGA-II and other hybrid approaches, showing a good performance not only in the quality of the solutions but also as computational cost saving.
publishDate 2019
dc.date.none.fl_str_mv 2019-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/124621
Baquela, Enrique Gabriel; Olivera, Ana Carolina; A novel hybrid multi-objective metamodel-based evolutionary optimization algorithm; Elsevier; Operations Research Perspectives; 6; 100098; 1-2019; 1-14
2214-7160
CONICET Digital
CONICET
url http://hdl.handle.net/11336/124621
identifier_str_mv Baquela, Enrique Gabriel; Olivera, Ana Carolina; A novel hybrid multi-objective metamodel-based evolutionary optimization algorithm; Elsevier; Operations Research Perspectives; 6; 100098; 1-2019; 1-14
2214-7160
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S221471601830068X
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.orp.2019.100098
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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