Performance Enhancement Of Artificial Bee Colony Optimization Algorithm

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dc.contributor.authorAbro, Abdul Ghani
dc.date.accessioned2019-05-06T03:05:33Z
dc.date.available2019-05-06T03:05:33Z
dc.date.issued2013-07
dc.description.abstractArtificial Bee Colony (ABC) algorithm is a recently proposed bio-inspired optimization algorithm, simulating foraging phenomenon of honeybees. Although literature works have revealed the superiority of ABC algorithm on numerous benchmark functions and real-world applications, the standard ABC and its variants have been found to suffer from slow convergence, prone to local-optima traps, poor exploitation and poor capability to replace exhaustive potential-solutions. To overcome the problems, this research work has proposed few modified and new ABC variants; Gbest Influenced-Random ABC (GRABC) algorithm systematically exploits two different mutation equations for appropriate exploration and exploitation of search-space, Multiple Gbest-guided ABC (MBABC) algorithm enhances the capability of locating global optimum by exploiting so-far-found multiple best regions of a search-space, Enhanced ABC (EABC) algorithm speeds up exploration for optimal-solutions based on the best so-far-found region of a search-space and Enhanced Probability-Selection ABC (EPS-ABC) algorithm, a modified version of the Probability-Selection ABC algorithm, simultaneously capitalizes on three different mutation equations for determining the global-optimum.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/8146
dc.language.isoenen_US
dc.publisherUniversiti Sains Malaysiaen_US
dc.subjectPerformance Enhancementen_US
dc.subjectArtificial Bee Colonyen_US
dc.titlePerformance Enhancement Of Artificial Bee Colony Optimization Algorithmen_US
dc.typeThesisen_US
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