python实现双种群遗传算法源代码

双种群遗传算法（Differential Evolution Algorithm, DEA）是一种优化算法，它通过模拟自然界中生物进化的原理，在两个独立的种群中进行搜索，以寻找问题的最优解。Python是一种流行的编程语言，有许多库可以方便地实现这种算法，比如`deap`库就是其中之一。

下面是一个简单的DEA在Python中使用`deap`库实现的例子：

from deap import base, creator, tools

# 定义适应度函数和个体类
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)

def evaluate(individual):
    # 这里替换为实际的问题评价函数
    return individual,

toolbox = base.Toolbox()
toolbox.register("attr_float", random.random)
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, n=problem_dim)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)

# 双种群设置
pop1 = toolbox.population(n=pop_size1)
pop2 = toolbox.population(n=pop_size2)

# 主循环
for gen in range(num_generations):
    # 更新种群1
    offspring1 = toolbox.select(pop1, len(pop1))
    offspring1 = toolbox.clone(offspring1)
    for i in range(len(offspring1)):
        mutant = toolbox.clone(random.choice(pop1))
        for j in range(problem_dim):
            mutant[j] = mutant[j] + toolbox.rand() * (offspring1[i][j] - pop1[random.randint(0, len(pop1)-1)][j])
        if toolbox.evaluate(mutant) > toolbox.evaluate(offspring1[i]):
            offspring1[i] = mutant
    pop1 = toolbox.mutate(toolbox.select(pop1, len(pop1)), mutpb)

    # 更新种群2
    # 类似于种群1的操作，这里省略代码以保持简洁

    # 交叉、选择等操作也可以在此处进行
    # ...

    # 统计并打印当前代的信息
    fits = [ind.fitness.values[0] for ind in pop1]
    mean_fitness = sum(fits) / len(fits)
    print(f"Generation {gen+1}: Mean Fitness = {mean_fitness:.4f}")

# 返回最适个体作为结果
best_indiv = tools.selBest(pop1, k=1)[0]
print("Best Individual:", best_indiv, "Fitness:", best_indiv.fitness.values[0])