关键词: 粒子群算法, 混沌权重, 平均粒子速度, 加速系数

Abstract:

A modified particle swarm optimization algorithm is proposed which aims to solving the flaws of easy plunging into local optimum  and losing search ability in the last period for the fast particle velocity decrease. The paper introduces chaos mapping into the particle swarm optimization instead of the linear reduction inertia weight,and  prevents the velocity decrease early,the inertia weight is regulated according to the average particle velocity.In the last period of the algorithm,the constraint relation between the acceleration coefficient and the inertia weight is used to improve the local search ability. Simulations show that the search performance of the proposed method is much better than the traditional PSO algorithm.

Key words: particle swarm optimization;chaos inertia weight;average particle velocity;acceleration factor