Cultural-based particle swarm optimization algorithm
Teng Hong-fei · Dalian Ligong Daxue xuebao · 2007
A cultural-based particle swarm optimization(CBPSO) algorithm is proposed to improve the computational accuracy and efficiency of PSO and avoid premature.This algorithm model consists of a PSO-based main population space and a knowledge space,which respectively has its own population to evolve independently and parallel.The lower level main population space(PSO population) contributes elite individuals to the upper level space(knowledge population) periodically,and the upper level space continually evolves these elite individuals and then contributes elite individuals to the lower level space.The mechanism of dual evolution and dual promotion improves the population diversity,and avoids premature.Two examples originated from the layout design of satellite module and integrated circuit show that CBPSO exhibits better computational efficiency and accuracy than GA and PSO.