中文摘要：

标准粒子群算法易陷入局部最优值。根据粒子群算法中的不确定性因素，提出自适应模糊的粒子群优化算法（AFPSO）。在该算法中，对惯性权值和位置更新采用模糊控制，用所有粒子的个体最优的加权平均替代全局最优值，增强了粒子之间相互学习的能力。仿真实验表明，AFPSO算法简单，可灵活地调节全局搜索和局部搜索能力，与已有相关算法比较，较好地解决了粒子群早熟问题，并提高了搜索精度。

英文摘要：

Standard particle swarm algorithm is easy to fall into local optimum.An Adaptive Fuzzy Particle Swarm Optimization（AFPSO） based on the tmcertainty of the PSO is proposed.In the improved algorithm,the inertia weight and the particle position update are controlled by fuzzy membership function, and the global optimal value is replaced by the weighted average of all particles optimal value to enhance the learning ability ,among particles.The experimental results show that, compared with the correlation algorithm, the proposed method is simple and more flexible to adjust global and local search, which also avoids the premature convergence problem,and improves the search accuracy.