An Adaptive Parameter Tuning of Particle Swarm Optimization Algorithm
Although particle swarm optimization (PSO) algorithm has shown some important advances by providing high convergence speed in specific problems, it has also been reported that the algorithm has a tendency to get stuck in local optimal solution. In this paper, the relationship between the average absolute value of velocity of all of the particles and the search failures is pointed out. An adaptive parameter tuning of particle swarm optimization based on velocity information (APSO-VI) algorithm is put forward. The average absolute value of velocity of all of the particles changes along with a given ideal velocity of cosine by feedback control for tuning inertia weight to improve the search ability in the multidimensional space. Experimental results show that the proposed algorithm remarkably improves the ability of PSO to jump out of the local optima and significantly enhances the convergence precision.
Particle swarm optimization average absolute value of velocity ideal velocity inertia weight adaptation
Gang Xu Yihong Xu
Department of Mathematics Nanchang University Nanchang China
国际会议
秦皇岛
英文
97-101
2010-11-05（万方平台首次上网日期，不代表论文的发表时间）