关键词: 粒子群算法, 局部收敛, 自适应, 变异操作, 群体智能

Abstract: In order to deal with the problems that the diversity of particle swarm is low and it is easy for particle swarm to fall in local optimum solution, this paper proposes a novel Particle Swarm Optimization （PSO） algorithm based on self-adaptive mutation, which combines with the optimal and other particles’ different role in the population. In the proposed algorithm, according to the evolution degree, the optimal particle can adaptively adjust its adjacent search domain size so as to strengthen the local search capacity and for the non-optimal particles, their locations can initialize randomly in low probability in order to increase the diversity of particle swarm and enhance the global search capacity when its speed is zero. In simulation, the algorithm is applied to the optimization problems of six typical complex functions, and comparing its performance with the other mutation PSO algorithms. The simulation results show that the proposed algorithm not only enhances population diversity, but also strengthens the local search capacity.

Key words: Particle Swarm Optimization（PSO）, local convergence, self-adaptive, mutation, swarm intelligence