中文摘要：

针对传统粒子群寻优速度慢的缺点，引进了种群平均速度的定义。用平均速度表征粒子群的活跃程度，并作为粒子群惯性权重和学习因子调节的依据，加快了粒子群的寻优速度。针对粒子群容易陷入局部极值的缺点，提出将模拟退火算法引入粒子群算法，将粒子群的平行快速寻优能力和模拟退火的概率突跳特性相结合，保持了群体多样性，有效地避免了局部收敛。对2个典型测试函数的寻优问题进行仿真实验，实验结果验证了该算法的有效性。将改进的粒子群算法用于风电场风速概率分布模型的优化，与常规的统计方法相比，该方法具有更高的拟合精度。

英文摘要：

Aiming at the shortcoming of slower optimization （PSO）, this paper introduces an average which is utilized to regulate the inertia weight accelerated. Besides, this paper incorporates sim parallel and SA＇ s probability sudden jump, th convergence is effectively avoided. Moreover, t functions. Finally, this proposed method is ap and searching speed speed to describe learning factors in the traditional particle swarm the such activeness of particle swarm, that the searching speed is ulated annealing （SA） into PSO. By means of PSO＇s fast e population diversity may be maintained and the local he improved algorithm is verified through 4 typical test plied to the optimization on the model for wind speed probability distribution of wind farm. Compared with traditional statistic strategy, the present algorithm may attain higher precision.