A review on the ant colony optimization metaheuristic: basis, models and new trends

Ant Colony Optimization (ACO) is a recent metaheuristic method that is inspired by the behavior of real ant colonies. In this paper, we review the underlying ideas of this approach that lead from the biological inspiration to the ACO metaheuristic, which gives a set of rules of how to apply ACO algo...

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Detalles Bibliográficos
Autores: Cordón García, Oscar, Herrera Triguero, Francisco, Stützle, Thomas
Tipo de recurso: artículo
Fecha de publicación:2002
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2099/3624
Acceso en línea:https://hdl.handle.net/2099/3624
Access Level:acceso abierto
Palabra clave:Ant Colony Optimization (ACO)
Intel·ligència artificial
Algorismes -- Anàlisi
Classificació AMS::68 Computer science::68T Artificial intelligence
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spelling A review on the ant colony optimization metaheuristic: basis, models and new trendsCordón García, OscarHerrera Triguero, FranciscoStützle, ThomasAnt Colony Optimization (ACO)Intel·ligència artificialAlgorismes -- AnàlisiClassificació AMS::68 Computer science::68T Artificial intelligenceAnt Colony Optimization (ACO) is a recent metaheuristic method that is inspired by the behavior of real ant colonies. In this paper, we review the underlying ideas of this approach that lead from the biological inspiration to the ACO metaheuristic, which gives a set of rules of how to apply ACO algorithms to challenging combinatorial problems. We present some of the algorithms that were developed under this framework, give an overview of current applications, and analyze the relationship between ACO and some of the best known metaheuristics. In addition, we describe recent theoretical developments in the field and we conclude by showing several new trends and new research directions in this field.Universitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica20022002-01-0120072007-10-02journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2099/3624reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2http://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2099/36242026-05-27T15:37:01Z
dc.title.none.fl_str_mv A review on the ant colony optimization metaheuristic: basis, models and new trends
title A review on the ant colony optimization metaheuristic: basis, models and new trends
spellingShingle A review on the ant colony optimization metaheuristic: basis, models and new trends
Cordón García, Oscar
Ant Colony Optimization (ACO)
Intel·ligència artificial
Algorismes -- Anàlisi
Classificació AMS::68 Computer science::68T Artificial intelligence
title_short A review on the ant colony optimization metaheuristic: basis, models and new trends
title_full A review on the ant colony optimization metaheuristic: basis, models and new trends
title_fullStr A review on the ant colony optimization metaheuristic: basis, models and new trends
title_full_unstemmed A review on the ant colony optimization metaheuristic: basis, models and new trends
title_sort A review on the ant colony optimization metaheuristic: basis, models and new trends
dc.creator.none.fl_str_mv Cordón García, Oscar
Herrera Triguero, Francisco
Stützle, Thomas
author Cordón García, Oscar
author_facet Cordón García, Oscar
Herrera Triguero, Francisco
Stützle, Thomas
author_role author
author2 Herrera Triguero, Francisco
Stützle, Thomas
author2_role author
author
dc.subject.none.fl_str_mv Ant Colony Optimization (ACO)
Intel·ligència artificial
Algorismes -- Anàlisi
Classificació AMS::68 Computer science::68T Artificial intelligence
topic Ant Colony Optimization (ACO)
Intel·ligència artificial
Algorismes -- Anàlisi
Classificació AMS::68 Computer science::68T Artificial intelligence
description Ant Colony Optimization (ACO) is a recent metaheuristic method that is inspired by the behavior of real ant colonies. In this paper, we review the underlying ideas of this approach that lead from the biological inspiration to the ACO metaheuristic, which gives a set of rules of how to apply ACO algorithms to challenging combinatorial problems. We present some of the algorithms that were developed under this framework, give an overview of current applications, and analyze the relationship between ACO and some of the best known metaheuristics. In addition, we describe recent theoretical developments in the field and we conclude by showing several new trends and new research directions in this field.
publishDate 2002
dc.date.none.fl_str_mv 2002
2002-01-01
2007
2007-10-02
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2099/3624
url https://hdl.handle.net/2099/3624
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica
publisher.none.fl_str_mv Universitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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