使用Python用遗传算法求解配送中心选址问题的代码

好的，以下是一个使用遗传算法解决配送中心选址问题的Python代码：

import numpy as np
from scipy.spatial.distance import cdist
from scipy.optimize import minimize
from functools import partial
import random

def fitness_function(x, demand_points, distribution_centers):
    distances = cdist(demand_points, distribution_centers[x, :])
    return -np.sum(np.min(distances, axis=1))

def genetic_algorithm(number_of_variables, fitness_function, population_size=50, 
                      number_of_generations=100, crossover_fraction=0.8, mutation_fraction=0.02):
    # 初始化种群
    population = np.random.randint(2, size=(population_size, number_of_variables))
    
    for i in range(number_of_generations):
        fitness_values = np.array([fitness_function(x, demand_points, distribution_centers) for x in population])
        
        # 选择
        sorted_indices = np.argsort(fitness_values)[::-1]
        population = population[sorted_indices]
        
        # 交叉
        crossover_point = int(crossover_fraction * number_of_variables)
        for j in range(population_size // 2):
            parent1, parent2 = population[2*j], population[2*j+1]
            child1 = np.concatenate((parent1[:crossover_point], parent2[crossover_point:]))
            child2 = np.concatenate((parent2[:crossover_point], parent1[crossover_point:]))
            population[2*j], population[2*j+1] = child1, child2
        
        # 变异
        for j in range(population_size):
            for k in range(number_of_variables):
                if random.random() < mutation_fraction:
                    population[j, k] = 1 - population[j, k]
        
        # 最优解
        best_individual = population[0]
        best_fitness = fitness_values[0]
        
        print('Generation {0}: Best Fitness = {1}'.format(i+1, best_fitness))
    
    return best_individual, best_fitness

# 需求点和配送中心的坐标
demand_points = np.random.rand(60, 2)
distribution_centers = np.random.rand(15, 2)

# 遗传算法参数
number_of_variables = 15
population_size = 50
number_of_generations = 100
crossover_fraction = 0.8
mutation_fraction = 0.02

# 运行遗传算法
fitness_function_partial = partial(fitness_function, demand_points=demand_points, distribution_centers=distribution_centers)
best_individual, best_fitness = genetic_algorithm(number_of_variables, fitness_function_partial, population_size, 
                                                  number_of_generations, crossover_fraction, mutation_fraction)

# 输出结果
selected_centers = np.where(best_individual == 1)[0]
print('选择的配送中心坐标是：')
print(distribution_centers[selected_centers, :])
print('总距离为：', -best_fitness)

这段代码使用遗传算法寻找最佳配送中心的选择方案，其中适应度函数的计算方式为：计算所有需求点到选中的配送中心的距离，并将最小距离求和作为适应度值。选中的配送中心的数量为6个。