Particle Swarm Optimization Algorithm with Self Adapting Inertia Weight
一种单个粒子自适应修正的粒子群算法

In the process of solving all kinds of optimization problems,local searching and global searching performance of swarm optimization algorithm play an important role.In particle swarm optimization(PSO) algorithm,the inertia weight has a certain effect on convergence and stability.Inspired by the effect of inertia weight on convergence of PSO,a new modified strategy for inertia weight is proposed based on fitness value.Comparative experiments of benchmark functions indicate that this new strategy could make the particles various to get the strong ability to keep from plunging local optimum and improve the astringency speed in the end of searching.Experiment results show that it is effective for prematurity and improve the ability of convergence.