Convergence analysis of particle swarm optimizer and its improved algorithm based on velocity differential evolution.
This paper presents an analysis of the relationship of particle velocity and convergence of the particle swarm optimization. Its premature convergence is due to the decrease of particle velocity in search space that leads to a total implosion and ultimately fitness stagnation of the swarm. An improved algorithm which introduces a velocity differential evolution (DE) strategy for the hierarchical particle swarm optimization (H-PSO) is proposed to improve its performance. The DE is employed to regulate the particle velocity rather than the traditional particle position in case that the optimal result has not improved after several iterations. The benchmark functions will be illustrated to demonstrate the effectiveness of the proposed method.
School of Electrical and Information Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China. yehongtao@126.com
Hongtao
H
Ye
Wenguang
W
Luo
Zhenqiang
Z
Li
D000465:Algorithms; D010363:Pattern Recognition, Automated / Q000379:methods*