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dc.contributor.authorAbraham, Ajith
dc.contributor.authorJatoth, Ravi Kumar
dc.contributor.authorRajasekhar, A.
dc.date.accessioned2012-05-11T12:19:13Z
dc.date.available2012-05-11T12:19:13Z
dc.date.issued2012
dc.identifier.citationJournal of Computational and Theoretical Nanoscience. 2012, vol 9, no. 2, p. 249-257.cs
dc.identifier.issn1546-1955
dc.identifier.urihttp://hdl.handle.net/10084/90440
dc.description.abstractArtificial Bee Colony Algorithm (ABCA) is a new population-based meta-heuristic approach inspired by the foraging behaviour of bees. This article describes an application of a novel Hybrid Differential Artificial Bee Colony Algorithm (HDABCA), which combines Differential Evolution strategy with Artificial Bee Colony algorithm. We illustrate the proposed method using several test functions and also compared with classical differential evolution algorithm and artificial bee colony algorithm. Simulation results illustrate that the proposed method is very efficient.cs
dc.language.isoencs
dc.publisherElseviercs
dc.relation.ispartofseriesJournal of Computational and Theoretical Nanosciencecs
dc.relation.urihttps://doi.org/10.1166/jctn.2012.2019cs
dc.subjectartificial bee colonycs
dc.subjectdifferential evolutioncs
dc.subjectoptimization modelscs
dc.titleHybrid differential artificial bee colony algorithmcs
dc.typearticlecs
dc.identifier.locationNení ve fondu ÚKcs
dc.identifier.doi10.1166/jctn.2012.2019
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume9cs
dc.description.issue2cs
dc.description.lastpage257cs
dc.description.firstpage249cs
dc.identifier.wos000302837800010


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