将共享机制引入微粒群算法,把群体的粒子适应度更新为共享适应度,对共享适应度高的粒子进行处罚,保留低适应值的粒子为记忆粒子,当全局最好值连续进化若干代无变化时,用记忆粒子和克隆选择来更新粒子。这样既增加了群体的多样性,同时又保存了群体中最好的粒子,从而有效克服了由于微粒群算法多样性差而造成的易陷于局部最优和对多峰值函数搜索效果不佳的缺点,仿真实验验证了该算法的有效性。 Particle swarm optimization algorithm is introduced to share the fitness of the population updated to share the fitness of the sharing of high fitness particles to be penalized to retain low fitness particles for the memory particles, when the global best value for several generations of continuous evolution When changing, particles are updated with memory particles and clone selection. This not only increases the diversity of the population but also saves the best particles in the population and thus effectively overcomes the shortcomings of being easily trapped in the local optimum due to the poor diversity of the PSO and the search effect of the multi-peak function being poor, Simulation results show the effectiveness of the algorithm.