遗传算法流水线调度问题python

遗传算法可以用于解决流水线调度问题。下面是一个用Python实现的基本遗传算法流水线调度问题的示例代码：

import random

# 定义工件和机器的数量
num_jobs = 5
num_machines = 3

# 生成随机的流水线
def generate_pipeline(num_jobs, num_machines):
    pipeline = []
    for _ in range(num_jobs):
        operations = [random.randint(1, num_machines) for _ in range(num_machines)]
        pipeline.append(operations)
    return pipeline

# 计算每个工件的完成时间
def calculate_completion_time(pipeline):
    completion_times = [0] * num_machines
    for operations in pipeline:
        for machine, operation_time in enumerate(operations):
            completion_times[machine] += operation_time
    return max(completion_times)

# 生成初始种群
def generate_population(population_size):
    population = []
    for _ in range(population_size):
        individual = generate_pipeline(num_jobs, num_machines)
        population.append(individual)
    return population

# 评估种群中每个个体的适应度
def evaluate_population(population):
    fitness_scores = []
    for individual in population:
        completion_time = calculate_completion_time(individual)
        fitness_scores.append(1 / completion_time) # 适应度为完成时间的倒数，越小越好
    return fitness_scores

# 选择操作：轮盘赌选择法
def roulette_wheel_selection(population, fitness_scores):
    total_fitness = sum(fitness_scores)
    probabilities = [score / total_fitness for score in fitness_scores]
    selected_index = random.choices(range(len(population)), probabilities)[0]
    return population[selected_index]

# 交叉操作：单点交叉
def crossover(parent1, parent2):
    crossover_point = random.randint(1, num_jobs - 1)
    child1 = parent1[:crossover_point] + parent2[crossover_point:]
    child2 = parent2[:crossover_point] + parent1[crossover_point:]
    return child1, child2

# 变异操作：随机变异
def mutate(individual):
    for i in range(num_jobs):
        if random.random() < mutation_rate:
            individual[i] = [random.randint(1, num_machines) for _ in range(num_machines)]
    return individual

# 遗传算法主函数
def genetic_algorithm(population_size, num_generations, mutation_rate):
    population = generate_population(population_size)

    for generation in range(num_generations):
        fitness_scores = evaluate_population(population)
        new_population = []

        for _ in range(population_size // 2):
            parent1 = roulette_wheel_selection(population, fitness_scores)
            parent2 = roulette_wheel_selection(population, fitness_scores)
            child1, child2 = crossover(parent1, parent2)
            new_population.append(mutate(child1))
            new_population.append(mutate(child2))

        population = new_population

    best_individual = max(population, key=lambda x: 1 / calculate_completion_time(x))
    best_completion_time = calculate_completion_time(best_individual)

    return best_individual, best_completion_time

# 设置参数并运行遗传算法
population_size = 100
num_generations = 50
mutation_rate = 0.01

best_individual, best_completion_time = genetic_algorithm(population_size, num_generations, mutation_rate)

print("Best individual:", best_individual)
print("Best completion time:", best_completion_time)

请注意，这只是一个简化的示例代码，实际应用中可能还需要考虑更多的约束和优化方法。希望对你有帮助！如果有任何问题，请随时提问。