A Many-objective Ant Colony Optimization applied to the Traveling Salesman Problem

Autores
Riveros, Francisco; Benítez, Néstor; Paciello, Julio; Barán, Benjamín
Año de publicación
2016
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Evolutionary algorithms present performance drawbacks when applied to Many-objective Optimization Problems (MaOPs). In this work, a novel approach based on Ant Colony Optimization theory (ACO), denominated ACO λ base-p algorithm, is proposed in order to handle Manyobjective instances of the well-known Traveling Salesman Problem (TSP). The proposed algorithm was applied to several Many-objective TSP instances, verifying the quality of the experimental results using the Hypervolume metric. A comparison with other state-of-the-art Multi Objective ACO algorithms as MAS, M3AS and MOACS as well as NSGA2 evolutionary algorithm was made, verifying that the best experimental results were obtained when the proposed algorithm was used, proving a good applicability to MaOPs.
Facultad de Informática
Materia
Ciencias Informáticas
ant colony optimization
traveling salesman problem
many-objective optimization
hypervolume
NSGA2
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/57269
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/57269
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling A Many-objective Ant Colony Optimization applied to the Traveling Salesman ProblemRiveros, FranciscoBenítez, NéstorPaciello, JulioBarán, BenjamínCiencias Informáticasant colony optimizationtraveling salesman problemmany-objective optimizationhypervolumeNSGA2Evolutionary algorithms present performance drawbacks when applied to Many-objective Optimization Problems (MaOPs). In this work, a novel approach based on Ant Colony Optimization theory (ACO), denominated ACO λ base-p algorithm, is proposed in order to handle Manyobjective instances of the well-known Traveling Salesman Problem (TSP). The proposed algorithm was applied to several Many-objective TSP instances, verifying the quality of the experimental results using the Hypervolume metric. A comparison with other state-of-the-art Multi Objective ACO algorithms as MAS, M3AS and MOACS as well as NSGA2 evolutionary algorithm was made, verifying that the best experimental results were obtained when the proposed algorithm was used, proving a good applicability to MaOPs.Facultad de Informática2016-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf89-94http://sedici.unlp.edu.ar/handle/10915/57269enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/2016/12/JCST-43-Paper-4.pdfinfo:eu-repo/semantics/altIdentifier/issn/1666-6038info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/3.0/Creative Commons Attribution 3.0 Unported (CC BY 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-15T10:58:47Zoai:sedici.unlp.edu.ar:10915/57269Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 10:58:48.18SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv A Many-objective Ant Colony Optimization applied to the Traveling Salesman Problem
title A Many-objective Ant Colony Optimization applied to the Traveling Salesman Problem
spellingShingle A Many-objective Ant Colony Optimization applied to the Traveling Salesman Problem
Riveros, Francisco
Ciencias Informáticas
ant colony optimization
traveling salesman problem
many-objective optimization
hypervolume
NSGA2
title_short A Many-objective Ant Colony Optimization applied to the Traveling Salesman Problem
title_full A Many-objective Ant Colony Optimization applied to the Traveling Salesman Problem
title_fullStr A Many-objective Ant Colony Optimization applied to the Traveling Salesman Problem
title_full_unstemmed A Many-objective Ant Colony Optimization applied to the Traveling Salesman Problem
title_sort A Many-objective Ant Colony Optimization applied to the Traveling Salesman Problem
dc.creator.none.fl_str_mv Riveros, Francisco
Benítez, Néstor
Paciello, Julio
Barán, Benjamín
author Riveros, Francisco
author_facet Riveros, Francisco
Benítez, Néstor
Paciello, Julio
Barán, Benjamín
author_role author
author2 Benítez, Néstor
Paciello, Julio
Barán, Benjamín
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
ant colony optimization
traveling salesman problem
many-objective optimization
hypervolume
NSGA2
topic Ciencias Informáticas
ant colony optimization
traveling salesman problem
many-objective optimization
hypervolume
NSGA2
dc.description.none.fl_txt_mv Evolutionary algorithms present performance drawbacks when applied to Many-objective Optimization Problems (MaOPs). In this work, a novel approach based on Ant Colony Optimization theory (ACO), denominated ACO λ base-p algorithm, is proposed in order to handle Manyobjective instances of the well-known Traveling Salesman Problem (TSP). The proposed algorithm was applied to several Many-objective TSP instances, verifying the quality of the experimental results using the Hypervolume metric. A comparison with other state-of-the-art Multi Objective ACO algorithms as MAS, M3AS and MOACS as well as NSGA2 evolutionary algorithm was made, verifying that the best experimental results were obtained when the proposed algorithm was used, proving a good applicability to MaOPs.
Facultad de Informática
description Evolutionary algorithms present performance drawbacks when applied to Many-objective Optimization Problems (MaOPs). In this work, a novel approach based on Ant Colony Optimization theory (ACO), denominated ACO λ base-p algorithm, is proposed in order to handle Manyobjective instances of the well-known Traveling Salesman Problem (TSP). The proposed algorithm was applied to several Many-objective TSP instances, verifying the quality of the experimental results using the Hypervolume metric. A comparison with other state-of-the-art Multi Objective ACO algorithms as MAS, M3AS and MOACS as well as NSGA2 evolutionary algorithm was made, verifying that the best experimental results were obtained when the proposed algorithm was used, proving a good applicability to MaOPs.
publishDate 2016
dc.date.none.fl_str_mv 2016-11
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
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://sedici.unlp.edu.ar/handle/10915/57269
url http://sedici.unlp.edu.ar/handle/10915/57269
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/2016/12/JCST-43-Paper-4.pdf
info:eu-repo/semantics/altIdentifier/issn/1666-6038
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/3.0/
Creative Commons Attribution 3.0 Unported (CC BY 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/3.0/
Creative Commons Attribution 3.0 Unported (CC BY 3.0)
dc.format.none.fl_str_mv application/pdf
89-94
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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