有几个超参数可以让进化算法发散。
https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.differential_evolution.html
例如：

import numpy as np
from scipy.optimize import rosen, differential_evolution
bounds = [(0,2), (1, 3), (1, 2), (4, 7), (500, 900)]

result = differential_evolution(rosen, bounds, seed=234)
print(result.x, result.fun)

result = differential_evolution(rosen, bounds, seed=42)
print(result.x, result.fun)

result = differential_evolution(rosen, bounds, seed=42)
print(result.x, result.fun)

result = differential_evolution(rosen, bounds, seed=23, mutation=(0.9, 1), recombination=0.8)
print(result.x, result.fun)

result = differential_evolution(rosen, bounds, seed=23, maxiter=2, mutation=(0.9, 1), recombination=0.8)
print(result.x, result.fun)

result = differential_evolution(rosen, bounds, seed=23, maxiter=2, mutation=(0.9, 1), recombination=0.8)
print(result.x, result.fun)

[out]：

(array([  1.1880044 ,   1.41300298,   2.        ,   7.        ,
        500.        ]),
 20341037.207360283)

(array([  1.18838044,   1.41362179,   2.        ,   7.        ,
        500.        ]),
 20341037.207038924)
​
(array([  1.18891057,   1.41438122,   2.        ,   7.        ,
        500.        ]),
 20341037.207497682)

(array([  1.1885353 ,   1.41414795,   2.        ,   7.        ,
        500.        ]),
 20341037.207302164)

在边界中具有较大的方差

但由于罗森函数是公式化的，因此边界的方差需要足够大，才能看到结果的显著变化。

import numpy as np
from scipy.optimize import rosen, differential_evolution
bounds = [(0,221529234), (123121, 31231232), (1231, 291231235), (30434, 1232317), (500, 900)]

result = differential_evolution(rosen, bounds, seed=234)
print(result.x, result.fun)

result = differential_evolution(rosen, bounds, seed=42)
print(result.x, result.fun)

[out]：

(array([  8141.41766062, 123121.        ,   1231.        ,  30434.        ,
           813.59702423]),
 2.3065086995396433e+22)

(array([     0.        , 123121.        ,   3838.30391681,  30434.        ,
           881.09558529]),
 2.30646627657552e+22)

在rosen函数中只有一个全局最小值。不清楚为什么你期望有多个解。differential_evolution被设计成有更大的概率找到全局最小值。如果你有一个具有多个最小值的解，并且你想记录它们，那么shgo可能是你最好的选择。或者你可以跟踪微分的进度_进化群体以识别能量表面中的不同最小值。