dc.creator | Meza, Joaquín | |
dc.creator | Espitia, Helbert | |
dc.creator | Montenegro, Carlos Enrique | |
dc.creator | Giménez de Ory, Elena (1) | |
dc.creator | González-Crespo, Rubén (1) | |
dc.date.accessioned | 2017-08-09T14:27:57Z | |
dc.date.accessioned | 2023-03-07T19:13:27Z | |
dc.date.available | 2017-08-09T14:27:57Z | |
dc.date.available | 2023-03-07T19:13:27Z | |
dc.date.created | 2017-08-09T14:27:57Z | |
dc.identifier | 1872-9681 | |
dc.identifier | https://reunir.unir.net/handle/123456789/5381 | |
dc.identifier | http://dx.doi.org/10.1016/j.asoc.2016.09.026 | |
dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/5900160 | |
dc.description.abstract | This 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.language | eng | |
dc.publisher | Applied Soft Computing | |
dc.relation | ;vol, 52 | |
dc.relation | http://www.sciencedirect.com/science/article/pii/S1568494616304859?via%3Dihub | |
dc.rights | restrictedAccess | |
dc.subject | multi-objective optimization | |
dc.subject | particle swarm | |
dc.subject | vorticity | |
dc.subject | JCR | |
dc.subject | Scopus | |
dc.title | MOVPSO: Vortex Multi-Objective Particle Swarm Optimization | |
dc.type | Articulo Revista Indexada | |