Topsis decision on approximate pareto fronts by using evolutionary algorithms: Application to an engineering design problem

Autores
Méndez Babey, Máximo; Frutos, Mariano; Miguel, Fabio Maximiliano; Aguasca Colomo, Ricardo
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A common technique used to solve multi-objective optimization problems consists of first generating the set of all Pareto-optimal solutions and then ranking and/or choosing the most interesting solution for a human decision maker (DM). Sometimes this technique is referred to as generate first–choose later. In this context, this paper proposes a two-stage methodology: a first stage using a multi-objective evolutionary algorithm (MOEA) to generate an approximate Pareto-optimal front of non-dominated solutions and a second stage, which uses the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) devoted to rank the potential solutions to be proposed to the DM. The novelty of this paper lies in the fact that it is not necessary to know the ideal and nadir solutions of the problem in the TOPSIS method in order to determine the ranking of solutions. To show the utility of the proposed methodology, several original experiments and comparisons between different recognized MOEAs were carried out on a welded beam engineering design benchmark problem. The problem was solved with two and three objectives and it is characterized by a lack of knowledge about ideal and nadir values.
Fil: Méndez Babey, Máximo. Universidad de Las Palmas de Gran Canaria; España
Fil: Frutos, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; Argentina
Fil: Miguel, Fabio Maximiliano. Universidad Nacional de Río Negro; Argentina
Fil: Aguasca Colomo, Ricardo. Universidad de Las Palmas de Gran Canaria; España
Materia
ENGINEERING DESIGN
MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS
MULTIPLE CRITERIA DECISION-MAKING
OPTIMIZATION
PREFERENCES
TOPSIS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/129472
Acceder
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spelling Topsis decision on approximate pareto fronts by using evolutionary algorithms: Application to an engineering design problemMéndez Babey, MáximoFrutos, MarianoMiguel, Fabio MaximilianoAguasca Colomo, RicardoENGINEERING DESIGNMULTI-OBJECTIVE EVOLUTIONARY ALGORITHMSMULTIPLE CRITERIA DECISION-MAKINGOPTIMIZATIONPREFERENCESTOPSIShttps://purl.org/becyt/ford/2.11https://purl.org/becyt/ford/2A common technique used to solve multi-objective optimization problems consists of first generating the set of all Pareto-optimal solutions and then ranking and/or choosing the most interesting solution for a human decision maker (DM). Sometimes this technique is referred to as generate first–choose later. In this context, this paper proposes a two-stage methodology: a first stage using a multi-objective evolutionary algorithm (MOEA) to generate an approximate Pareto-optimal front of non-dominated solutions and a second stage, which uses the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) devoted to rank the potential solutions to be proposed to the DM. The novelty of this paper lies in the fact that it is not necessary to know the ideal and nadir solutions of the problem in the TOPSIS method in order to determine the ranking of solutions. To show the utility of the proposed methodology, several original experiments and comparisons between different recognized MOEAs were carried out on a welded beam engineering design benchmark problem. The problem was solved with two and three objectives and it is characterized by a lack of knowledge about ideal and nadir values.Fil: Méndez Babey, Máximo. Universidad de Las Palmas de Gran Canaria; EspañaFil: Frutos, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; ArgentinaFil: Miguel, Fabio Maximiliano. Universidad Nacional de Río Negro; ArgentinaFil: Aguasca Colomo, Ricardo. Universidad de Las Palmas de Gran Canaria; EspañaMDPI2020-11-20info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/129472Méndez Babey, Máximo; Frutos, Mariano; Miguel, Fabio Maximiliano; Aguasca Colomo, Ricardo; Topsis decision on approximate pareto fronts by using evolutionary algorithms: Application to an engineering design problem; MDPI; Mathematics; 8; 11; 20-11-2020; 1-272227-7390CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2227-7390/8/11/2072info:eu-repo/semantics/altIdentifier/doi//10.3390/math8112072info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:39:27Zoai:ri.conicet.gov.ar:11336/129472instacron: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:34982025-09-29 09:39:27.321CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Topsis decision on approximate pareto fronts by using evolutionary algorithms: Application to an engineering design problem
title Topsis decision on approximate pareto fronts by using evolutionary algorithms: Application to an engineering design problem
spellingShingle Topsis decision on approximate pareto fronts by using evolutionary algorithms: Application to an engineering design problem
Méndez Babey, Máximo
ENGINEERING DESIGN
MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS
MULTIPLE CRITERIA DECISION-MAKING
OPTIMIZATION
PREFERENCES
TOPSIS
title_short Topsis decision on approximate pareto fronts by using evolutionary algorithms: Application to an engineering design problem
title_full Topsis decision on approximate pareto fronts by using evolutionary algorithms: Application to an engineering design problem
title_fullStr Topsis decision on approximate pareto fronts by using evolutionary algorithms: Application to an engineering design problem
title_full_unstemmed Topsis decision on approximate pareto fronts by using evolutionary algorithms: Application to an engineering design problem
title_sort Topsis decision on approximate pareto fronts by using evolutionary algorithms: Application to an engineering design problem
dc.creator.none.fl_str_mv Méndez Babey, Máximo
Frutos, Mariano
Miguel, Fabio Maximiliano
Aguasca Colomo, Ricardo
author Méndez Babey, Máximo
author_facet Méndez Babey, Máximo
Frutos, Mariano
Miguel, Fabio Maximiliano
Aguasca Colomo, Ricardo
author_role author
author2 Frutos, Mariano
Miguel, Fabio Maximiliano
Aguasca Colomo, Ricardo
author2_role author
author
author
dc.subject.none.fl_str_mv ENGINEERING DESIGN
MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS
MULTIPLE CRITERIA DECISION-MAKING
OPTIMIZATION
PREFERENCES
TOPSIS
topic ENGINEERING DESIGN
MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS
MULTIPLE CRITERIA DECISION-MAKING
OPTIMIZATION
PREFERENCES
TOPSIS
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.11
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv A common technique used to solve multi-objective optimization problems consists of first generating the set of all Pareto-optimal solutions and then ranking and/or choosing the most interesting solution for a human decision maker (DM). Sometimes this technique is referred to as generate first–choose later. In this context, this paper proposes a two-stage methodology: a first stage using a multi-objective evolutionary algorithm (MOEA) to generate an approximate Pareto-optimal front of non-dominated solutions and a second stage, which uses the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) devoted to rank the potential solutions to be proposed to the DM. The novelty of this paper lies in the fact that it is not necessary to know the ideal and nadir solutions of the problem in the TOPSIS method in order to determine the ranking of solutions. To show the utility of the proposed methodology, several original experiments and comparisons between different recognized MOEAs were carried out on a welded beam engineering design benchmark problem. The problem was solved with two and three objectives and it is characterized by a lack of knowledge about ideal and nadir values.
Fil: Méndez Babey, Máximo. Universidad de Las Palmas de Gran Canaria; España
Fil: Frutos, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; Argentina
Fil: Miguel, Fabio Maximiliano. Universidad Nacional de Río Negro; Argentina
Fil: Aguasca Colomo, Ricardo. Universidad de Las Palmas de Gran Canaria; España
description A common technique used to solve multi-objective optimization problems consists of first generating the set of all Pareto-optimal solutions and then ranking and/or choosing the most interesting solution for a human decision maker (DM). Sometimes this technique is referred to as generate first–choose later. In this context, this paper proposes a two-stage methodology: a first stage using a multi-objective evolutionary algorithm (MOEA) to generate an approximate Pareto-optimal front of non-dominated solutions and a second stage, which uses the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) devoted to rank the potential solutions to be proposed to the DM. The novelty of this paper lies in the fact that it is not necessary to know the ideal and nadir solutions of the problem in the TOPSIS method in order to determine the ranking of solutions. To show the utility of the proposed methodology, several original experiments and comparisons between different recognized MOEAs were carried out on a welded beam engineering design benchmark problem. The problem was solved with two and three objectives and it is characterized by a lack of knowledge about ideal and nadir values.
publishDate 2020
dc.date.none.fl_str_mv 2020-11-20
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/11336/129472
Méndez Babey, Máximo; Frutos, Mariano; Miguel, Fabio Maximiliano; Aguasca Colomo, Ricardo; Topsis decision on approximate pareto fronts by using evolutionary algorithms: Application to an engineering design problem; MDPI; Mathematics; 8; 11; 20-11-2020; 1-27
2227-7390
CONICET Digital
CONICET
url http://hdl.handle.net/11336/129472
identifier_str_mv Méndez Babey, Máximo; Frutos, Mariano; Miguel, Fabio Maximiliano; Aguasca Colomo, Ricardo; Topsis decision on approximate pareto fronts by using evolutionary algorithms: Application to an engineering design problem; MDPI; Mathematics; 8; 11; 20-11-2020; 1-27
2227-7390
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://www.mdpi.com/2227-7390/8/11/2072
info:eu-repo/semantics/altIdentifier/doi//10.3390/math8112072
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
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 13.070432

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