写出基于遗传算法的蓄电池和飞轮储能的容量优化配置代码

以下是基于遗传算法的蓄电池和飞轮储能的容量优化配置代码，供参考：

import random

# 定义遗传算法参数
POP_SIZE = 50  # 种群数量
CROSS_RATE = 0.8  # 交叉概率
MUTATION_RATE = 0.1  # 变异概率
N_GENERATIONS = 50  # 迭代次数

# 定义系统参数
POWER_DEMAND = 10  # 功率需求
ENERGY_DEMAND = 100  # 能量需求
BATTERY_COST = 1  # 电池成本
FLYWHEEL_COST = 5  # 飞轮成本
BATTERY_LIFE = 5  # 电池寿命
FLYWHEEL_LIFE = 10  # 飞轮寿命

# 定义电池和飞轮容量范围
BATTERY_CAPACITY_RANGE = (10, 50)
FLYWHEEL_CAPACITY_RANGE = (5, 25)

# 计算适应度函数
def fitness_func(individual):
    battery_capacity = individual[0]
    flywheel_capacity = individual[1]
    battery_energy = battery_capacity * BATTERY_LIFE
    flywheel_energy = flywheel_capacity * FLYWHEEL_LIFE
    total_cost = battery_capacity * BATTERY_COST + flywheel_capacity * FLYWHEEL_COST
    if battery_energy + flywheel_energy >= ENERGY_DEMAND and (battery_capacity + flywheel_capacity) >= POWER_DEMAND:
        return total_cost
    else:
        return float('inf')

# 定义交叉函数
def crossover(parent_1, parent_2):
    if random.random() < CROSS_RATE:
        index = random.randint(0, len(parent_1)-1)
        child_1 = parent_1[:index] + parent_2[index:]
        child_2 = parent_2[:index] + parent_1[index:]
        return child_1, child_2
    else:
        return parent_1, parent_2

# 定义变异函数
def mutate(individual):
    if random.random() < MUTATION_RATE:
        index = random.randint(0, len(individual)-1)
        if index == 0:
            individual[index] = random.randint(*BATTERY_CAPACITY_RANGE)
        else:
            individual[index] = random.randint(*FLYWHEEL_CAPACITY_RANGE)
    return individual

# 定义遗传算法主程序
def genetic_algorithm():
    # 初始化种群
    population = [[random.randint(*BATTERY_CAPACITY_RANGE), random.randint(*FLYWHEEL_CAPACITY_RANGE)] for _ in range(POP_SIZE)]

    for generation in range(N_GENERATIONS):
        # 计算适应度
        fitness = [fitness_func(individual) for individual in population]

        # 选择种群中适应度最好的个体
        best_individual = population[fitness.index(min(fitness))]

        # 选择种群中的父母个体
        parents = []
        for _ in range(len(population)//2):
            index_1 = random.randint(0, len(population)-1)
            index_2 = random.randint(0, len(population)-1)
            parent_1 = population[index_1]
            parent_2 = population[index_2]
            parents.append((parent_1, parent_2))

        # 进行交叉和变异
        children = []
        for parent_1, parent_2 in parents:
            child_1, child_2 = crossover(parent_1, parent_2)
            child_1 = mutate(child_1)
            child_2 = mutate(child_2)
            children.append(child_1)
            children.append(child_2)

        # 更新种群
        population = children

        # 输出当前迭代的最优解
        print('Generation:', generation, 'Best Individual:', best_individual, 'Cost:', fitness_func(best_individual))

    return best_individual

# 运行遗传算法
best_individual = genetic_algorithm()

# 输出最终结果
battery_capacity = best_individual[0]
flywheel_capacity = best_individual[1]
print('Battery Capacity:', battery_capacity, 'Flywheel Capacity:', flywheel_capacity)