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    MOVPSO: Vortex Multi-Objective Particle Swarm Optimization

    Autor: 
    Meza, Joaquín
    ;
    Espitia, Helbert
    ;
    Montenegro, Carlos Enrique
    ;
    Giménez de Ory, Elena
    ;
    González-Crespo, Rubén
    Fecha: 
    03/2017
    Palabra clave: 
    multi-objective optimization; particle swarm; vorticity; JCR; Scopus
    Revista / editorial: 
    Applied Soft Computing
    Tipo de Ítem: 
    Articulo Revista Indexada
    URI: 
    https://reunir.unir.net/handle/123456789/5381
    DOI: 
    http://dx.doi.org/10.1016/j.asoc.2016.09.026
    Dirección web: 
    http://www.sciencedirect.com/science/article/pii/S1568494616304859?via%3Dihub
    Resumen:
    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.
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