Lagrange Interpolation Learning Particle Swarm OptimizationReportar como inadecuado

Lagrange Interpolation Learning Particle Swarm Optimization - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

In recent years, comprehensive learning particle swarm optimization CLPSO has attracted the attention of many scholars for using in solving multimodal problems, as it is excellent in preserving the particles’ diversity and thus preventing premature convergence. However, CLPSO exhibits low solution accuracy. Aiming to address this issue, we proposed a novel algorithm called LILPSO. First, this algorithm introduced a Lagrange interpolation method to perform a local search for the global best point gbest. Second, to gain a better exemplar, one gbest, another two particle’s historical best points pbest are chosen to perform Lagrange interpolation, then to gain a new exemplar, which replaces the CLPSO’s comparison method. The numerical experiments conducted on various functions demonstrate the superiority of this algorithm, and the two methods are proven to be efficient for accelerating the convergence without leading the particle to premature convergence.

Autor: Zhang Kai , Song Jinchun , Ni Ke , Li Song



Documentos relacionados