Reference: Mirjalili S, Mirjalili S M, Lewis A. Grey wolf optimizer[J]. Advances in Engineering Software, 2014, 69: 46-61.
| Variables |
Meaning |
| pop |
The number of population |
| lb |
List, the lower bound of the i-th component is lb[i] |
| ub |
List, the upper bound of the i-th component is ub[i] |
| iter |
The maximum number of iterations |
| dim |
The dimension, dim = len(lb) = len(ub) |
| pos |
List, the position of each wolf |
| score |
List, the score of each wolf |
| iter_best |
List, the best so-far score of each iteration |
| alpha_pos |
List, the position of the alpha wolf |
| alpha_score |
The score of alpha wolf |
| beta_pos |
List, the position of the beta wolf |
| beta_score |
The score of the beta wolf |
| delta_pos |
List, the position of the delta wolf |
| delta_score |
The score of the delta wolf |
Test problem: Pressure vessel design
$$
\begin{align}
&\text{min}\ f(x)=0.6224x_1x_3x_4+1.7781x_2x_3^2+3.1661x_1^2x_4+19.84x_1^2x_3,\\
&\text{s.t.} \\
&-x_1+0.0193x_3\leq0,\\
&-x_3+0.0095x_3\leq0,\\
&-\pi x_3^2x_4-\frac{4}{3}\pi x_3^3+1296000\leq0,\\
&x_4-240\leq0,\\
&0\leq x_1\leq99,\\
&0\leq x_2 \leq99,\\
&10\leq x_3 \leq 200,\\
&10\leq x_4 \leq 200.
\end{align}
$$
if __name__ == '__main__':
pop = 200
lb = [0, 0, 10, 10]
ub = [99, 99, 200, 200]
iter = 100
print(main(pop, lb, ub, iter))
{
'best solution': [1.3021065176429443, 0.6432278697383198, 67.39566614500494, 10.432203586211408],
'best score': 8087.875089101558
}
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent