经典差分进化算法代码
时间: 2023-02-09 18:42:18 浏览: 88
经典差分进化算法(Differential Evolution, DE) 是一种用于优化多元函数的算法。它通常用于优化无约束的问题,并且能够适用于离散和连续的变量。
下面是一个用 Python 实现的经典差分进化算法的代码示例:
```
import numpy as np
def differential_evolution(f, bounds, population_size=100, max_iterations=1000,
crossover_probability=0.5, differential_weight=0.5):
# 初始化种群
population = []
for i in range(population_size):
individual = []
for j in bounds:
individual.append(np.random.uniform(j[0], j[1]))
population.append(individual)
population = np.array(population)
# 迭代搜索
for i in range(max_iterations):
for j in range(population_size):
# 选择三个个体
indices = [idx for idx in range(population_size) if idx != j]
x1, x2, x3 = population[np.random.choice(indices, 3, replace=False)]
# 计算变异后的个体
mutant = x1 + differential_weight * (x2 - x3)
# 进行交叉
cross_points = np.random.rand(len(bounds)) < crossover_probability
if not np.any(cross_points):
cross_points[np.random.randint(0, len(bounds))] = True
trial = np.where(cross_points, mutant, population[j])
# 取代原来的个体
trial_cost = f(trial)
if trial_cost < f(population[j]):
population[j] = trial
# 返回最优解
return population[np.argmin(f(population))]
```
在这个代码中,`f` 是待优化的函数,`bounds` 是变量的取值范围,`population_size` 是种群大小,`max_iterations` 是最大迭代次数,`crossover_probability` 是交叉概率,`differential_weight` 是差分系数
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