装配线平衡优化使用模拟退火改进遗传算法，写出完整代码

装配线平衡优化结合模拟退火（Simulated Annealing）和遗传算法（Genetic Algorithm），可以提供更全局且鲁棒的解决方案。这是一个简化版本的Python示例，使用了`deap`库（一种用于并行遗传算法的Python框架）和自定义的模拟退火模块。注意这只是一个基础示例，并未涵盖所有细节，例如初始化种群、适应度评估、交叉和变异等。

首先，安装必要的库：

pip install deap numpy

然后编写代码：

import random
import numpy as np
from deap import base, creator, tools, algorithms
from simulated_annealing import SimulatedAnnealing

# 定义基因编码
creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
creator.create("Individual", list, fitness=creator.FitnessMin)

def evaluate_fitness(individual):
    # 假设任务完成时间构成个体的适应度值
    completion_times = [individual[i] for i in range(len(tasks))]
    total_wait_time = sum(completion_times)
    return (-total_wait_time,)

def create_initial_population(n):
    return [Individual(random.sample(range(stations), stations)) for _ in range(n)]

# 模拟退火参数
anneal_params = {
    "T": 1000,
    "cooling_rate": 0.95,
    "acceptance_probability": 0.8,
}

def main():
    n_individuals = 50
    population = create_initial_population(n_individuals)

    toolbox = base.Toolbox()
    toolbox.register("evaluate", evaluate_fitness)
    toolbox.register("mate", tools.cxTwoPoint)
    toolbox.register("mutate", tools.mutShuffleIndexes, indpb=0.05)
    toolbox.register("select", tools.selNSGA2)

    pop, logbook = algorithms.eaSimple(population, toolbox, cxpb=0.8, mutpb=0.2,ngen=100, verbose=True)

    # 结合模拟退火进行迭代优化
    best_solution = pop[0]
    for _ in range(100):  # 可以调整迭代次数
        improved_solution = SimulatedAnnealing(evaluate_fitness, **anneal_params).run(best_solution)
        if improve_solution > best_solution.fitness.values:
            best_solution = improved_solution

    print("最优解决方案:", best_solution)
    print("最小总等待时间:", -best_solution.fitness.values[0])

if __name__ == "__main__":
    main()