Show simple item record

dc.contributor.authorCordón García, Oscar
dc.contributor.authorHerrera Triguero, Francisco
dc.contributor.authorStützle, Thomas
dc.date.accessioned2007-10-02T11:33:25Z
dc.date.available2007-10-02T11:33:25Z
dc.date.issued2002
dc.identifier.issn1134-5632
dc.identifier.urihttp://hdl.handle.net/2099/3624
dc.description.abstractAnt 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.
dc.format.extent141-175
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica
dc.relation.ispartofMathware & soft computing . 2002 Vol. 9 Núm. 2 [ -3 ]
dc.rightsReconeixement-NoComercial-CompartirIgual 3.0 Espanya
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.otherAnt Colony Optimization (ACO)
dc.titleA review on the ant colony optimization metaheuristic: basis, models and new trends
dc.typeArticle
dc.subject.lemacIntel·ligència artificial
dc.subject.lemacAlgorismes -- Anàlisi
dc.subject.amsClassificació AMS::68 Computer science::68T Artificial intelligence
dc.rights.accessOpen Access


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Except where otherwise noted, content on this work is licensed under a Creative Commons license: Attribution-NonCommercial-NoDerivs 3.0 Spain