关键词: 粒子群, 变异, 函数优化

Abstract: Aiming at the shortcoming of the standard PSO algorithm, that is easily plunging into local minimum, we oppose a new multi-mutation particle swarm optimization algorithm(MMPSO). In each iteration, the position of particles which is satisfied the mutation condition are mutated with many functions,and each function is endowed a probability. The probability distribution is relied on the specific optimization problem.The experimental results show that the MMPSO enhance the global searching ability and the probability of successful searching, and overcome the standard PSO’s liability to convergence to local optimum. It is also superior to single mutation particle swarm optimization algorithm(SMPSO).

Key words: particle swarm, mutation, function optimization