本文主要介绍了粒子群(Praticle Swarm Optimizer,PSO)算法,它是一种新的基于群体智能的优化算法,是在鸟群觅食行为规律的基础上提出的。他其结构简单、参数调整简单易行,更适合计算机编程处理,但在该算法中,如果粒子速度始终保持较大,容易“飞越”解空间中的最优区域,造成发散现象,收敛不到最优解,如果从惯性权重的自适应方面来调整,就可以很好的解决该问题。 This paper mainly introduces the Particle Swarm Optimizer (PSO) algorithm, which is a new optimization algorithm based on swarm intelligence, which is based on the foraging behavior of birds. Because of its simple structure and easy adjustment of parameters, it is more suitable for computer programming. However, in this algorithm, if the particle velocity remains relatively large, it is easy to overfly the optimal region in the solution space to cause divergence and convergence Less than the optimal solution, if we adjust from the adaptive aspect of inertia weight, we can solve the problem well.