Particle swarm optimization with crossover : a review and empirical analysis

dc.contributor.authorEngelbrecht, Andries P.
dc.contributor.emailengel@cs.up.ac.zaen_ZA
dc.date.accessioned2015-11-20T09:19:37Z
dc.date.issued2016-02
dc.description.abstractSince its inception in 1995, many improvements to the original particle swarm optimization (PSO) algorithm have been developed. This paper reviews one class of such PSO variations, i.e. PSO algorithms that make use of crossover operators. The review is supplemented with a more extensive sensitivity analysis of the crossover PSO algorithms than provided in the original publications. Two adaptations of a parent-centric crossover PSO algorithm are provided, resulting in improvements with respect to solution accuracy compared to the original parent-centric PSO algorithms. The paper then provides an extensive empirical analysis on a large benchmark of minimization problems, with the objective to identify those crossover PSO algorithms that perform best with respect to accuracy, success rate, and efficiency.en_ZA
dc.description.embargo2017-02-20
dc.description.librarianhb2015en_ZA
dc.description.urihttp://link.springer.com/journal/10462en_ZA
dc.identifier.citationEngelbrecht, AP 2016, 'Particle swarm optimization with crossover : a review and empirical analysis', Artificial Intelligence Review, vol. 45, no. 2, pp. 131-165.en_ZA
dc.identifier.issn0269-2821(print)
dc.identifier.issn1573-7462 (online)
dc.identifier.other10.1007/s10462-015-9445-7
dc.identifier.urihttp://hdl.handle.net/2263/50542
dc.language.isoenen_ZA
dc.publisherSpringeren_ZA
dc.rights© Springer Science+Business Media Dordrecht 2015. The original publication is available at : http://link.springer.com/journal/10462.en_ZA
dc.subjectSwarm intelligenceen_ZA
dc.subjectCrossoveren_ZA
dc.subjectBoundary constrained optimizationen_ZA
dc.subjectParticle swarm optimization (PSO)en_ZA
dc.titleParticle swarm optimization with crossover : a review and empirical analysisen_ZA
dc.typePostprint Articleen_ZA

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