CLPSO综合学习的粒子群算法的伪代码
时间: 2023-07-23 18:11:00 浏览: 39
以下是 CLPSO 综合学习的粒子群算法的伪代码:
```
Initialize the swarm
For each particle i in the swarm:
Initialize the particle's position x_i and velocity v_i
Evaluate the fitness of the particle
Set the particle's best-known position pbest_i = x_i
If pbest_i is better than the swarm's best-known position gbest, set gbest = pbest_i
End for
While stopping criterion is not met:
For each particle i in the swarm:
For each dimension d in the particle's position:
Update the velocity of the particle:
v_i,d = w*v_i,d + c1*r1*(pbest_i,d - x_i,d) + c2*r2*(gbest,d - x_i,d) + c3*r3*(s_i,d - x_i,d)
where w is the inertia weight, c1, c2, c3 are acceleration coefficients, r1, r2, r3 are random numbers in [0, 1], pbest_i,d is the d-th dimension of the particle's best-known position, gbest,d is the d-th dimension of the swarm's best-known position, and s_i,d is the d-th dimension of the social learning component
Update the position of the particle:
x_i,d = x_i,d + v_i,d
Evaluate the fitness of the particle
If the particle's fitness is better than its previous best-known fitness, update the particle's best-known position:
pbest_i = x_i
If pbest_i is better than the swarm's best-known position gbest, set gbest = pbest_i
End for
Update the social learning component s_i,d by aggregating the values of the d-th dimension of the best-known positions of the k nearest particles to particle i
End while
```
其中,综合学习中的社会学习组成部分 s_i,d 是通过聚合粒子 i 的 k 个最近邻粒子的最佳已知位置来计算的。聚合方式可以是简单的平均或加权平均等。