水库调度遗传算法python

水库调度问题可以使用遗传算法进行求解。以下是一个简单的遗传算法求解水库调度问题的Python代码：

import random

# 定义水库调度问题的目标函数
def objective_function(x):
    # x是一个长度为24的列表，表示24小时内每个时段的水位调度方案
    # 计算水库的出水量和尾水位
    outflow = [0] * 24
    water_level = [0] * 24
    water_level[0] = 20  # 初始水位为20米
    for i in range(1, 24):
        outflow[i] = min(x[i], 1000 * (water_level[i - 1] - 15))
        water_level[i] = water_level[i - 1] + 0.5 * (x[i] - outflow[i])
    # 计算目标函数值
    obj_value = 0
    for i in range(24):
        obj_value += 10 * (water_level[i] - 15) ** 2 + outflow[i] ** 2
    return obj_value

# 定义遗传算法的相关参数
population_size = 50  # 种群大小
chromosome_length = 24  # 染色体长度
mutation_probability = 0.01  # 变异概率
crossover_probability = 0.6  # 交叉概率
max_generation = 100  # 最大迭代次数

# 初始化种群
population = []
for i in range(population_size):
    chromosome = [random.randint(0, 100) for j in range(chromosome_length)]
    population.append(chromosome)

# 开始遗传算法的迭代过程
for generation in range(max_generation):
    # 评估种群中每个染色体的适应度
    fitness_values = [1 / objective_function(chromosome) for chromosome in population]
    # 找出当前种群中最优秀的染色体
    best_chromosome = population[fitness_values.index(max(fitness_values))]
    # 输出当前迭代次数和最优解
    print("Generation:", generation, "Best solution:", best_chromosome, "Objective function value:", objective_function(best_chromosome))
    # 生成新的种群
    new_population = []
    while len(new_population) < population_size:
        # 选择父代染色体
        parent1 = population[roulette_wheel_selection(fitness_values)]
        parent2 = population[roulette_wheel_selection(fitness_values)]
        # 交叉产生子代染色体
        if random.random() < crossover_probability:
            offspring1, offspring2 = crossover(parent1, parent2)
        else:
            offspring1, offspring2 = parent1, parent2
        # 变异产生新的染色体
        if random.random() < mutation_probability:
            offspring1 = mutation(offspring1)
        if random.random() < mutation_probability:
            offspring2 = mutation(offspring2)
        # 将新的染色体加入新的种群中
        new_population.append(offspring1)
        new_population.append(offspring2)
    population = new_population

# 定义选择操作（轮盘赌选择）
def roulette_wheel_selection(fitness_values):
    total_fitness = sum(fitness_values)
    selection_probabilities = [fitness_value / total_fitness for fitness_value in fitness_values]
    cum_probabilities = [sum(selection_probabilities[:i+1]) for i in range(len(selection_probabilities))]
    random_value = random.random()
    for i in range(len(cum_probabilities)):
        if random_value <= cum_probabilities[i]:
            return i

# 定义交叉操作（单点交叉）
def crossover(parent1, parent2):
    crossover_point = random.randint(1, chromosome_length - 1)
    offspring1 = parent1[:crossover_point] + parent2[crossover_point:]
    offspring2 = parent2[:crossover_point] + parent1[crossover_point:]
    return offspring1, offspring2

# 定义变异操作（随机重置）
def mutation(chromosome):
    mutated_chromosome = chromosome.copy()
    mutation_point = random.randint(0, chromosome_length - 1)
    mutated_chromosome[mutation_point] = random.randint(0, 100)
    return mutated_chromosome

在上面的代码中，目标函数是根据24小时内的水位调度方案计算水库的出水量和尾水位，并计算目标函数值。遗传算法的迭代过程包括种群初始化、评估适应度、选择、交叉、变异等操作。其中选择操作采用轮盘赌选择，交叉操作采用单点交叉，变异操作采用随机重置。最终输出的是迭代过程中每一代的最优解和目标函数值。