|
import numpy as np
import random
def chaotic_optimization(func, bounds, maxiter, popsize):
# 初始化随机种群
population = np.random.uniform(bounds[0], bounds[1], (popsize, len(bounds[0])))
# 记录当前最优解
best = None
for i in range(maxiter):
for j in range(popsize):
x = population[j]
# 混沌变换
x = x + np.logistic(x) * np.sin(2 * np.pi * x)
x = np.clip(x, bounds[0], bounds[1])
# 评估新解
fx = func(x)
if best is None or fx < func(best):
best = x
# 更新种群
population = x
return best
# 示例代码,使用函数 sphere 作为目标函数
def sphere(x):
return np.sum(x**2)
bounds = [(-100, 100), (-100, 100)]
result = chaotic_optimization(sphere, bounds, maxiter=1000, popsize=50)
print(&#34;Best solution found: &#34;, result) |
|