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dc.contributor.authorMeza, Joaquín
dc.contributor.authorEspitia, Helbert
dc.contributor.authorMontenegro, Carlos Enrique
dc.contributor.authorGiménez de Ory, Elena (1)
dc.contributor.authorGonzález-Crespo, Rubén (1)
dc.date2017-03
dc.date.accessioned2017-08-09T14:27:57Z
dc.date.available2017-08-09T14:27:57Z
dc.identifier.issn1872-9681
dc.identifier.urihttps://reunir.unir.net/handle/123456789/5381
dc.description.abstractThis paper presents the Multi-Objective Vortex Particle Swarm Optimization MOVPSO as a strategy based on the behavior of a particle swarm using rotational and translational motions. The MOVPSO strategy is based upon the emulation of the emerging property performed by a swarm (flock), achieving a successful motion with diversity control, via collaborative, using linear and circular movements. The proposed algorithm is tested through several multi-objective optimization functions and is compared with standard Multi-Objective Particle Swarm Optimization (MOPSO). The qualitative results show that particle swarms behave as expected. Finally, statistical analysis allows to appreciate that the MOVPSO algorithm has a favorable performance compared to traditional MOPSO algorithm.es_ES
dc.language.isoenges_ES
dc.publisherApplied Soft Computinges_ES
dc.relation.ispartofseries;vol, 52
dc.relation.urihttp://www.sciencedirect.com/science/article/pii/S1568494616304859?via%3Dihubes_ES
dc.rightsrestrictedAccesses_ES
dc.subjectmulti-objective optimizationes_ES
dc.subjectparticle swarmes_ES
dc.subjectvorticityes_ES
dc.subjectJCRes_ES
dc.subjectScopuses_ES
dc.titleMOVPSO: Vortex Multi-Objective Particle Swarm Optimizationes_ES
dc.typeArticulo Revista Indexadaes_ES
reunir.tag~ARIes_ES
dc.identifier.doihttp://dx.doi.org/10.1016/j.asoc.2016.09.026


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