MOVPSO: Vortex Multi-Objective Particle Swarm Optimization

dc.creatorMeza, Joaquín
dc.creatorEspitia, Helbert
dc.creatorMontenegro, Carlos Enrique
dc.creatorGiménez de Ory, Elena (1)
dc.creatorGonzález-Crespo, Rubén (1)
dc.date.accessioned2017-08-09T14:27:57Z
dc.date.accessioned2023-03-07T19:13:27Z
dc.date.available2017-08-09T14:27:57Z
dc.date.available2023-03-07T19:13:27Z
dc.date.created2017-08-09T14:27:57Z
dc.identifier1872-9681
dc.identifierhttps://reunir.unir.net/handle/123456789/5381
dc.identifierhttp://dx.doi.org/10.1016/j.asoc.2016.09.026
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5900160
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.
dc.languageeng
dc.publisherApplied Soft Computing
dc.relation;vol, 52
dc.relationhttp://www.sciencedirect.com/science/article/pii/S1568494616304859?via%3Dihub
dc.rightsrestrictedAccess
dc.subjectmulti-objective optimization
dc.subjectparticle swarm
dc.subjectvorticity
dc.subjectJCR
dc.subjectScopus
dc.titleMOVPSO: Vortex Multi-Objective Particle Swarm Optimization
dc.typeArticulo Revista Indexada


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