python车间布局优化代码

以下是使用遗传算法进行车间布局优化的Python代码示例：

import numpy as np
import random

# 定义问题的适应度函数
def fitness_function(individual):
    # 计算设备间距离总和
    dist_sum = 0
    for i in range(len(individual)):
        for j in range(i+1, len(individual)):
            dist_sum += distance(individual[i], individual[j])
    return dist_sum

# 计算两个设备之间的距离
def distance(device1, device2):
    return np.sqrt((device1[0]-device2[0])**2 + (device1[1]-device2[1])**2)

# 定义染色体表示
def create_individual(num_devices, width, height):
    individual = []
    for i in range(num_devices):
        individual.append((random.uniform(0, width), random.uniform(0, height)))
    return individual

# 初始化种群
def create_population(population_size, num_devices, width, height):
    population = []
    for i in range(population_size):
        population.append(create_individual(num_devices, width, height))
    return population

# 选择操作
def selection(population, fitness_values):
    index1 = random.randint(0, len(population)-1)
    index2 = random.randint(0, len(population)-1)
    if fitness_values[index1] < fitness_values[index2]:
        return population[index1]
    else:
        return population[index2]

# 交叉操作
def crossover(individual1, individual2):
    index = random.randint(0, len(individual1)-1)
    child1 = individual1[:index] + individual2[index:]
    child2 = individual2[:index] + individual1[index:]
    return child1, child2

# 变异操作
def mutation(individual, mutation_rate):
    for i in range(len(individual)):
        if random.random() < mutation_rate:
            individual[i] = (random.uniform(0, width), random.uniform(0, height))
    return individual

# 遗传算法求解
def genetic_algorithm(num_devices, width, height, population_size, mutation_rate, num_generations):
    # 初始化种群
    population = create_population(population_size, num_devices, width, height)
    # 迭代更新
    for generation in range(num_generations):
        # 计算适应度函数值
        fitness_values = [fitness_function(individual) for individual in population]
        # 选择操作
        new_population = []
        for i in range(population_size):
            parent1 = selection(population, fitness_values)
            parent2 = selection(population, fitness_values)
            child1, child2 = crossover(parent1, parent2)
            child1 = mutation(child1, mutation_rate)
            child2 = mutation(child2, mutation_rate)
            new_population.append(child1)
            new_population.append(child2)
        population = new_population
    # 输出最优解
    best_individual = min(population, key=fitness_function)
    best_fitness = fitness_function(best_individual)
    print("Best individual:", best_individual)
    print("Best fitness:", best_fitness)

# 测试代码
num_devices = 5
width = 10
height = 5
population_size = 50
mutation_rate = 0.1
num_generations = 100
genetic_algorithm(num_devices, width, height, population_size, mutation_rate, num_generations)

需要根据具体问题进行适当的调整和优化，例如修改适应度函数、染色体表示和变异操作等。