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dc.contributor.authorAbraham, A.-
dc.contributor.authorJatoth, R.K.-
dc.contributor.authorRajasekhar, A.-
dc.date.accessioned2025-01-18T05:25:08Z-
dc.date.available2025-01-18T05:25:08Z-
dc.date.issued2012-02-
dc.identifier.citation10.1166/jctn.2012.2019en_US
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/2758-
dc.descriptionNITWen_US
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.en_US
dc.language.isoenen_US
dc.publisherJournal of Computational and Theoretical Nanoscienceen_US
dc.subjectArtificial Bee Colonyen_US
dc.subjectDifferential Evolutionen_US
dc.subjectOptimization Modelsen_US
dc.titleHybrid Differential Artificial Bee Colony Algorithmen_US
dc.typeArticleen_US
Appears in Collections:Electronics and Communications Engineering

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