Proposal and Comparative Study of Evolutionary Algorithms for Optimum Design of a Gear System

Autores
Méndez Babey, Máximo; Rossit, Daniel Alejandro; González, Begoña; Frutos, Mariano
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper proposes a novel metaheuristic framework using a Differential Evolution (DE) algorithm with the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Both algorithms are combined employing a collaborative strategy with sequential execution, which is called DE-NSGA-II. The DE-NSGA-II takes advantage of the exploration abilities of the multi-objective evolutionary algorithms strengthened with the ability to search global mono-objective optimum of DE, that enhances the capability of finding those extreme solutions of Pareto Optimal Front (POF) difficult to achieve. Numerous experiments and performance comparisons between different evolutionary algorithms were performed on a referent problem for the mono-objective and multi-objective literature, which consists of the design of a double reduction gear train. A preliminary study of the problem, solved in an exhaustive way, discovers the low density of solutions in the vicinity of the optimal solution (mono-objective case) as well as in some areas of the POF of potential interest to a decision maker (multi-objective case). This characteristic of the problem would explain the considerable difficulties for its resolution when exact methods and/or metaheuristics are used, especially in the multi-objective case. However, the DE-NSGA-II framework exceeds these difficulties and obtains the whole POF which significantly improves the few previous multi-objective studies.
Fil: Méndez Babey, Máximo. Universidad de Las Palmas de Gran Canaria; España
Fil: Rossit, Daniel Alejandro. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina
Fil: González, Begoña. Universidad de Las Palmas de Gran Canaria; España
Fil: Frutos, Mariano. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina. 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
Materia
DIFFERENTIAL EVOLUTION
EVOLUTIONARY COMPUTATION
GEAR TRAIN OPTIMIZATION
GENETIC ALGORITHMS
MECHANICAL ENGINEERING
MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS
NON-DOMINATED SORTING GENETIC ALGORITHM-II
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:11336/100379
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/100379
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Proposal and Comparative Study of Evolutionary Algorithms for Optimum Design of a Gear SystemMéndez Babey, MáximoRossit, Daniel AlejandroGonzález, BegoñaFrutos, MarianoDIFFERENTIAL EVOLUTIONEVOLUTIONARY COMPUTATIONGEAR TRAIN OPTIMIZATIONGENETIC ALGORITHMSMECHANICAL ENGINEERINGMULTI-OBJECTIVE EVOLUTIONARY ALGORITHMSNON-DOMINATED SORTING GENETIC ALGORITHM-IIhttps://purl.org/becyt/ford/2.11https://purl.org/becyt/ford/2This paper proposes a novel metaheuristic framework using a Differential Evolution (DE) algorithm with the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Both algorithms are combined employing a collaborative strategy with sequential execution, which is called DE-NSGA-II. The DE-NSGA-II takes advantage of the exploration abilities of the multi-objective evolutionary algorithms strengthened with the ability to search global mono-objective optimum of DE, that enhances the capability of finding those extreme solutions of Pareto Optimal Front (POF) difficult to achieve. Numerous experiments and performance comparisons between different evolutionary algorithms were performed on a referent problem for the mono-objective and multi-objective literature, which consists of the design of a double reduction gear train. A preliminary study of the problem, solved in an exhaustive way, discovers the low density of solutions in the vicinity of the optimal solution (mono-objective case) as well as in some areas of the POF of potential interest to a decision maker (multi-objective case). This characteristic of the problem would explain the considerable difficulties for its resolution when exact methods and/or metaheuristics are used, especially in the multi-objective case. However, the DE-NSGA-II framework exceeds these difficulties and obtains the whole POF which significantly improves the few previous multi-objective studies.Fil: Méndez Babey, Máximo. Universidad de Las Palmas de Gran Canaria; EspañaFil: Rossit, Daniel Alejandro. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; ArgentinaFil: González, Begoña. Universidad de Las Palmas de Gran Canaria; EspañaFil: Frutos, Mariano. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina. 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; ArgentinaInstitute of Electrical and Electronics Engineers2019-12info: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/11336/100379Méndez Babey, Máximo; Rossit, Daniel Alejandro; González, Begoña; Frutos, Mariano; Proposal and Comparative Study of Evolutionary Algorithms for Optimum Design of a Gear System; Institute of Electrical and Electronics Engineers; IEEE Access; 8; 12-2019; 3482-34972169-3536CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://ieeexplore.ieee.org/document/8945204info:eu-repo/semantics/altIdentifier/doi/10.1109/ACCESS.2019.2962906info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:22:46Zoai:ri.conicet.gov.ar:11336/100379instacron: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 10:22:46.816CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Proposal and Comparative Study of Evolutionary Algorithms for Optimum Design of a Gear System
title Proposal and Comparative Study of Evolutionary Algorithms for Optimum Design of a Gear System
spellingShingle Proposal and Comparative Study of Evolutionary Algorithms for Optimum Design of a Gear System
Méndez Babey, Máximo
DIFFERENTIAL EVOLUTION
EVOLUTIONARY COMPUTATION
GEAR TRAIN OPTIMIZATION
GENETIC ALGORITHMS
MECHANICAL ENGINEERING
MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS
NON-DOMINATED SORTING GENETIC ALGORITHM-II
title_short Proposal and Comparative Study of Evolutionary Algorithms for Optimum Design of a Gear System
title_full Proposal and Comparative Study of Evolutionary Algorithms for Optimum Design of a Gear System
title_fullStr Proposal and Comparative Study of Evolutionary Algorithms for Optimum Design of a Gear System
title_full_unstemmed Proposal and Comparative Study of Evolutionary Algorithms for Optimum Design of a Gear System
title_sort Proposal and Comparative Study of Evolutionary Algorithms for Optimum Design of a Gear System
dc.creator.none.fl_str_mv Méndez Babey, Máximo
Rossit, Daniel Alejandro
González, Begoña
Frutos, Mariano
author Méndez Babey, Máximo
author_facet Méndez Babey, Máximo
Rossit, Daniel Alejandro
González, Begoña
Frutos, Mariano
author_role author
author2 Rossit, Daniel Alejandro
González, Begoña
Frutos, Mariano
author2_role author
author
author
dc.subject.none.fl_str_mv DIFFERENTIAL EVOLUTION
EVOLUTIONARY COMPUTATION
GEAR TRAIN OPTIMIZATION
GENETIC ALGORITHMS
MECHANICAL ENGINEERING
MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS
NON-DOMINATED SORTING GENETIC ALGORITHM-II
topic DIFFERENTIAL EVOLUTION
EVOLUTIONARY COMPUTATION
GEAR TRAIN OPTIMIZATION
GENETIC ALGORITHMS
MECHANICAL ENGINEERING
MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS
NON-DOMINATED SORTING GENETIC ALGORITHM-II
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.11
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv This paper proposes a novel metaheuristic framework using a Differential Evolution (DE) algorithm with the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Both algorithms are combined employing a collaborative strategy with sequential execution, which is called DE-NSGA-II. The DE-NSGA-II takes advantage of the exploration abilities of the multi-objective evolutionary algorithms strengthened with the ability to search global mono-objective optimum of DE, that enhances the capability of finding those extreme solutions of Pareto Optimal Front (POF) difficult to achieve. Numerous experiments and performance comparisons between different evolutionary algorithms were performed on a referent problem for the mono-objective and multi-objective literature, which consists of the design of a double reduction gear train. A preliminary study of the problem, solved in an exhaustive way, discovers the low density of solutions in the vicinity of the optimal solution (mono-objective case) as well as in some areas of the POF of potential interest to a decision maker (multi-objective case). This characteristic of the problem would explain the considerable difficulties for its resolution when exact methods and/or metaheuristics are used, especially in the multi-objective case. However, the DE-NSGA-II framework exceeds these difficulties and obtains the whole POF which significantly improves the few previous multi-objective studies.
Fil: Méndez Babey, Máximo. Universidad de Las Palmas de Gran Canaria; España
Fil: Rossit, Daniel Alejandro. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina
Fil: González, Begoña. Universidad de Las Palmas de Gran Canaria; España
Fil: Frutos, Mariano. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina. 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
description This paper proposes a novel metaheuristic framework using a Differential Evolution (DE) algorithm with the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Both algorithms are combined employing a collaborative strategy with sequential execution, which is called DE-NSGA-II. The DE-NSGA-II takes advantage of the exploration abilities of the multi-objective evolutionary algorithms strengthened with the ability to search global mono-objective optimum of DE, that enhances the capability of finding those extreme solutions of Pareto Optimal Front (POF) difficult to achieve. Numerous experiments and performance comparisons between different evolutionary algorithms were performed on a referent problem for the mono-objective and multi-objective literature, which consists of the design of a double reduction gear train. A preliminary study of the problem, solved in an exhaustive way, discovers the low density of solutions in the vicinity of the optimal solution (mono-objective case) as well as in some areas of the POF of potential interest to a decision maker (multi-objective case). This characteristic of the problem would explain the considerable difficulties for its resolution when exact methods and/or metaheuristics are used, especially in the multi-objective case. However, the DE-NSGA-II framework exceeds these difficulties and obtains the whole POF which significantly improves the few previous multi-objective studies.
publishDate 2019
dc.date.none.fl_str_mv 2019-12
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/11336/100379
Méndez Babey, Máximo; Rossit, Daniel Alejandro; González, Begoña; Frutos, Mariano; Proposal and Comparative Study of Evolutionary Algorithms for Optimum Design of a Gear System; Institute of Electrical and Electronics Engineers; IEEE Access; 8; 12-2019; 3482-3497
2169-3536
CONICET Digital
CONICET
url http://hdl.handle.net/11336/100379
identifier_str_mv Méndez Babey, Máximo; Rossit, Daniel Alejandro; González, Begoña; Frutos, Mariano; Proposal and Comparative Study of Evolutionary Algorithms for Optimum Design of a Gear System; Institute of Electrical and Electronics Engineers; IEEE Access; 8; 12-2019; 3482-3497
2169-3536
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://ieeexplore.ieee.org/document/8945204
info:eu-repo/semantics/altIdentifier/doi/10.1109/ACCESS.2019.2962906
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 Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
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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