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dc.contributor.authorMaheshkumar, Yeturu
dc.contributor.authorRavi, Vadlamani
dc.contributor.authorAbraham, Ajith
dc.date.accessioned2013-08-26T10:48:23Z
dc.date.available2013-08-26T10:48:23Z
dc.date.issued2013
dc.identifier.citationNeural Network World. 2013, vol. 23, issue 3, p. 191-221.cs
dc.identifier.issn1210-0552
dc.identifier.urihttp://hdl.handle.net/10084/100653
dc.description.abstractIn this paper, we propose a novel hybrid metaheuristic algorithm, which integrates a Threshold Accepting algorithm (TA) with a traditional Particle Swarm Optimization (PSO) algorithm. We used the TA as a catalyst in speeding up convergence of PSO towards the optimal solution. In this hybrid, at the end of every iteration of PSO, the TA is invoked probabilistically to refine the worst particle that lags in the race of finding the solution for that iteration. Consequently the worst particle will be refined in the next iteration. The robustness of the proposed approach has been tested on 34 unconstrained optimization problems taken from the literature. The proposed hybrid demonstrates superior preference in terms of functional evaluations and success rate for 30 simulations conducted.cs
dc.language.isoencs
dc.publisherAkademie věd České republiky, Ústav informatiky a České vysoké učení technické v Praze, Fakulta dopravnícs
dc.relation.ispartofseriesNeural Network Worldcs
dc.subjectparticle swarm optimizationcs
dc.subjectthreshold accepting algorithmcs
dc.subjecthybrid metaheuristicscs
dc.subjectunconstrained optimizationcs
dc.titleA particle swarm optimization-threshold accepting hybrid algorithm for unconstrained optimizationcs
dc.typearticlecs
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume23cs
dc.description.issue3cs
dc.description.lastpage221cs
dc.description.firstpage191cs
dc.identifier.wos000322148400001


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