遗传算法进行第一类生产线平衡优化的代码

在遗传算法中应用到生产线平衡优化时，通常涉及到分配工作任务给不同的工作站，以最小化生产时间或成本。这里以一个简化版本的Python代码为例，使用DEAP（Distributed Evolutionary Algorithms in Python）库，假设我们有若干个工作站和任务，目标是最优地分配任务给各个工作站以达成平衡：

from deap import base, creator, tools
import random

# 定义基因编码（每个基因代表一个工作站的任务）
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)

def evaluate(individual):
    # 模拟生产线的实际计算，例如任务完成时间和成本
    total_time, total_cost = calculate_production_time_and_cost(individual)
    return (-total_time, -total_cost), 

def assign_task(workstations, tasks):
    # 你的任务分配策略
    ...

def mutate(individual):
    # 生成随机变异，例如交换两个工作站的任务
    ...

def crossover(parent1, parent2):
    # 简单的二点交叉
    ...

toolbox = base.Toolbox()
toolbox.register("population", tools.initRepeat, list, creator.Individual)
toolbox.register("evaluate", evaluate)
toolbox.register("mate", crossover)
toolbox.register("mutate", mutate)

# 初始化种群、设置参数等
...

for gen in range(num_generations):
    offspring = toolbox.select(population, len(population))  # 选择优秀的个体
    offspring = [toolbox.clone(ind) for ind in offspring]  # 克隆避免直接修改原种群
    for child1, child2 in zip(offspring[::2], offspring[1::2]):
        if random.random() < crossover_probability:
            toolbox.mate(child1, child2)
            del child1.fitness.values
            del child2.fitness.values

    for mutant in offspring:
        if random.random() < mutation_probability:
            toolbox.mutate(mutant)
            del mutant.fitness.values

    invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
    fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
    for ind, fit in zip(invalid_ind, fitnesses):
        ind.fitness.values = fit
    
    population[:] = offspring
    best_solution = tools.selBest(population, 1)[0]

# 输出最佳解
best_solution, best_fitness = best_solution[0], best_solution[1]
print("最优解：", best_solution)
print("总时间：", -best_fitness[0])  # 因为我们返回的是负的生产时间