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dc.contributor.editorBarbosa, Helio J.C.
dc.date.accessioned2021-04-20T15:43:00Z
dc.date.available2021-04-20T15:43:00Z
dc.date.issued2013
dc.identifierONIX_20210420_9789535110019_1787
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/66429
dc.description.abstractAnt Colony Optimization (ACO) is the best example of how studies aimed at understanding and modeling the behavior of ants and other social insects can provide inspiration for the development of computational algorithms for the solution of difficult mathematical problems. Introduced by Marco Dorigo in his PhD thesis (1992) and initially applied to the travelling salesman problem, the ACO field has experienced a tremendous growth, standing today as an important nature-inspired stochastic metaheuristic for hard optimization problems. This book presents state-of-the-art ACO methods and is divided into two parts: (I) Techniques, which includes parallel implementations, and (II) Applications, where recent contributions of ACO to diverse fields, such as traffic congestion and control, structural optimization, manufacturing, and genomics are presented.
dc.languageEnglish
dc.subject.classificationthema EDItEUR::U Computing and Information Technology::UM Computer programming / software engineeringen_US
dc.subject.otherComputer programming / software development
dc.titleAnt Colony Optimization
dc.title.alternativeTechniques and Applications
dc.typebook
oapen.identifier.doi10.5772/3423
oapen.relation.isPublishedBy78a36484-2c0c-47cb-ad67-2b9f5cd4a8f6
oapen.relation.isbn9789535110019
oapen.relation.isbn9789535157175
oapen.imprintIntechOpen
oapen.pages214


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