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dc.contributor.authorEl Abd, Mohammed
dc.date.accessioned2016-04-07T08:38:38Z
dc.date.available2016-04-07T08:38:38Z
dc.date.issued2011
dc.identifier.urihttp://hdl.handle.net/11675/922
dc.description.abstractIn this paper we investigate the hybridization of two swarm intelligence algorithms; namely, the Artificial Bee Colony Algorithm (ABC) and Particle Swarm Optimization (PSO). The hybridization technique is a component-based one where the PSO algorithm is augmented with an ABC component to improve the personal bests of the particles. Two different hybrid algorithms are tested in this work based on the method in which the ABC component is applied to the different particles. All the algorithms are applied to the well-known CEC05 benchmark functions and compared based on three different metrics.
dc.relation.journalIEEE Swarm Intelligence Symposium
dc.titleAn ABC-SPSO Hybrid Algorithm for Continuous Function Optimization
dc.typeConference Paper
dc.article.pages96-101


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