惯性权值和学习因子对标准PSO算法性能的影响

基于标准PSO算法，通过分析惯性权值和学习因子2类参数不同的取值策略对常用测试函数优化结果的影响，来探究2类参数对算法性能的影响.实验结果表明，2类参数恰当的动态改变不仅能明显提高单峰函数的寻优精度和收敛速度，而且能提高双峰和多峰函数的寻优概率；惯性权值主要影响算法的收敛速度，随着惯性权值的递增，算法收敛速度逐渐加快；学习因子主要影响算法的寻优精度，当反映粒子的自我学习能力和向群体最优粒子学习的能力的学习因子同增同减变化时，寻优精度提高；惯性权值递增结合2种学习因子的同增同减变化，或惯性权值递减结合2种学习因子的一增一减变化，均可使标准PSO算法性能得到显著提高.

Influence of Inertia Weight and Learning Factor on Performance of Standard PSO Algorithm

Two types of parameters, inertia weight and learning factor, are the main factors affecting the accuracy and convergence speed of the standard PSO algorithm. Based on the standard PSO algorithm, the influence of different value-taking strategies of two types of parameters on the optimization results of commonly used test functions is analyzed to explore its impact on the performance of the standard PSO algorithm. The experimental results show that the proper dynamic change of the two types of parameters can significantly improve the optimization accuracy and convergence speed of the unimodal function, and can improve the optimization probability of the bimodal and multimodal functions. The inertia weight mainly affects the convergence speed of the algorithm. As the inertia weight increases, the convergence speed of the algorithm increases. The learning factor mainly affects the optimization precision of the algorithm. When the two learning factors increase and decrease, the precision of the optimization increases. The inertia weight increases with the two learning factors. The increase and decrease of the change, or the decrease of the inertia weight combined with the increase and decrease of the two learning factors, can significantly improve the performance of the standard PSO algorithm.