基本粒子群优化算法

1:初始化粒子群，初始位置：X_i, 初始速度：V_i, i = 1....M

2:更具每个栗子初始位置和速度生成新的。     

3: 计算每个例子的适应度都。                     

4: 比较每个粒子的适应度和群体所经历过的最好位置p_id. 如果适应度值比p_id好，update

5:比较群体经历的最好p_gd

6: Vid(t+1) = wVid(t) + m1 r1[Pid -Xid(t)] +

m2r2[Pid -Xid(t)]

Xid（t+1） = Xid (t) + Vid(t+1)

Vid(t+1) : ith 粒子在t+1interation dth dimension.

Parameter: w,  m1 m2 r1 r2 (0-1) depends on experience. It will results in the process fast but no most optimized solution. And there is a max verticity. [-Vmax, Vmax]

7: reach the goal, stop. Otherwise, return to step 3