说明：  经典粒子群算法，经过多次优化，演示出图， 待优化的目标函数：N 粒子数目：N 惯性权重：w 学习因子：c1,c2 最大迭代次数：M 问题的维数：D 目标函数取最小值时自变量值：xm 目标函数的最小值：fv
(Classical particle swarm algorithm, optimized for many times, demonstrates plotting objective function to be optimized: N of the number of particles: N inertia weight weight: w learning factor: c1, c2 maximum number of iterations: M dimension independent variables: D objective function is to take the minimum value: xm minimum value of the objective function: fv)