差分进化算法Python

差分进化算法（Differential Evolution）是一种常用的全局优化算法，用于求解复杂的非线性优化问题。以下是一个使用Python实现差分进化算法的简单示例：

import numpy as np

def differential_evolution(objective_func, bounds, population_size=50, max_generations=100, F=0.5, CR=0.7):
    # 初始化种群
    population = np.random.uniform(bounds[0], bounds[1], (population_size, len(bounds)))
    best_solution = None

    for generation in range(max_generations):
        for i in range(population_size):
            # 选择3个不同的个体作为父代
            candidates = [j for j in range(population_size) if j != i]
            a, b, c = np.random.choice(candidates, 3, replace=False)

            # 生成变异向量
            mutant = population[a] + F * (population[b] - population[c])

            # 交叉操作
            crossover = np.random.rand(len(bounds)) < CR
            trial = np.where(crossover, mutant, population[i])

            # 评估新解
            if objective_func(trial) < objective_func(population[i]):
                population[i] = trial

                # 更新最优解
                if best_solution is None or objective_func(population[i]) < objective_func(best_solution):
                    best_solution = population[i]

    return best_solution

# 示例目标函数，求解最小值的一维函数
def objective(x):
    return x**2 + 2*x + 1

bounds = [(-10, 10)]  # 自变量范围

best_solution = differential_evolution(objective, bounds)
print("最优解：", best_solution)
print("最优值：", objective(best_solution))

以上代码实现了一个简单的差分进化算法，用于求解一个一维函数的最小值。你可以根据自己的需求来修改目标函数、自变量的范围、种群大小、迭代次数等参数。希望对你有帮助！