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A review on the ant colony optimization metaheuristic: basis, models and new trends
dc.contributor.author | Cordón García, Oscar |
dc.contributor.author | Herrera Triguero, Francisco |
dc.contributor.author | Stützle, Thomas |
dc.date.accessioned | 2007-10-02T11:33:25Z |
dc.date.available | 2007-10-02T11:33:25Z |
dc.date.issued | 2002 |
dc.identifier.issn | 1134-5632 |
dc.identifier.uri | http://hdl.handle.net/2099/3624 |
dc.description.abstract | 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. |
dc.format.extent | 141-175 |
dc.language.iso | eng |
dc.publisher | Universitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica |
dc.relation.ispartof | Mathware & soft computing . 2002 Vol. 9 Núm. 2 [ -3 ] |
dc.rights | Reconeixement-NoComercial-CompartirIgual 3.0 Espanya |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject.other | Ant Colony Optimization (ACO) |
dc.title | A review on the ant colony optimization metaheuristic: basis, models and new trends |
dc.type | Article |
dc.subject.lemac | Intel·ligència artificial |
dc.subject.lemac | Algorismes -- Anàlisi |
dc.subject.ams | Classificació AMS::68 Computer science::68T Artificial intelligence |
dc.rights.access | Open Access |