Particle Swarm Optimization Algorithm Based on Space Mutation and its Application
Shengli Song, Li Kong, Pu Zhang, Rijian Su · 2009
According to characteristics of particle swarm optimization algorithm, a novel particle swarm optimization algorithm based on adaptive space mutation (ASM-PSO) is proposed. During the searching process, the convergence speed and globally convergence ability is greatly improved by the adaptive space mutation based on the variance of the population's fitness. Experiment results show that the new method, with both a better stability and a steady convergence, not only enhances the local searching efficiency and global searching performance greatly, but also has faster convergence speed and higher precision, and can avoid the premature convergence problem effectively. Most importantly, results demonstrate that ASM-PSO is more feasible and efficient for quality monitoring of laser welding process.