Title :
A share historical and global best particle swarm optimization algorithm
Author :
Zhigang, Lian ; Keyi, Hu ; Zhibin, Jiang ; Dongbiao, Zheng
Author_Institution :
Electron. & Inf. Sch., Shanghai DianJi Univ., Shanghai, China
Abstract :
This article advances a share historical and global best particle swarm optimization algorithm (SGHPSO). In SGHPSO model, particles fully inherit the information of historical and global optimum particles in previous operation, which increases the search efficiency of particles. Ten typical nonlinear functions are given to test the efficiency of the improved algorithm. Simulation results clearly demonstrate superiority of the improved algorithm.
Keywords :
particle swarm optimisation; SGHPSO; nonlinear functions; share historical and global best particle swarm optimization algorithm; Medical services; Historical; PSO; global; inherit;
Conference_Titel :
Computer Science and Network Technology (ICCSNT), 2011 International Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4577-1586-0
DOI :
10.1109/ICCSNT.2011.6182012
