遗传算法解决配送选址问题python代码

下面是用Python实现遗传算法解决配送选址问题的代码：

import random

# 定义配送选址问题的目标函数
def objective_function(x, y, demand, supply, transport_cost):
    total_cost = 0
    for i in range(len(x)):
        for j in range(len(y)):
            total_cost += transport_cost[i][j] * supply[i][j] * demand[i][j]
    return total_cost

# 定义遗传算法的参数
population_size = 50
num_generations = 100
mutation_rate = 0.1

# 定义配送选址问题的参数
x = [0, 1, 2, 3] # 配送中心的位置
y = [4, 5, 6, 7] # 客户的位置
demand = [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15]] # 每个客户的需求量
supply = [[100, 200, 300, 400], [500, 600, 700, 800], [900, 1000, 1100, 1200], [1300, 1400, 1500, 1600]] # 每个配送中心的供应量
transport_cost = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]] # 运输成本

# 定义种群初始化函数
def initialize_population():
    population = []
    for i in range(population_size):
        chromosome = []
        for j in range(len(y)):
            chromosome.append(random.choice(x))
        population.append(chromosome)
    return population

# 定义选择函数
def selection(population):
    parents = []
    fitnesses = []
    total_fitness = 0
    for i in range(len(population)):
        chromosome = population[i]
        fitness = 1 / objective_function(x, y, demand, supply, transport_cost)
        fitnesses.append(fitness)
        total_fitness += fitness
    for i in range(len(population)):
        probability = fitnesses[i] / total_fitness
        for j in range(int(probability * population_size)):
            parents.append(population[i])
    return parents

# 定义交叉函数
def crossover(parents):
    offspring = []
    for i in range(int(population_size / 2)):
        parent1 = random.choice(parents)
        parent2 = random.choice(parents)
        split_point = random.randint(1, len(y) - 1)
        child1 = parent1[:split_point] + parent2[split_point:]
        child2 = parent2[:split_point] + parent1[split_point:]
        offspring.append(child1)
        offspring.append(child2)
    return offspring

# 定义变异函数
def mutation(offspring):
    for i in range(len(offspring)):
        for j in range(len(y)):
            if random.uniform(0, 1) < mutation_rate:
                offspring[i][j] = random.choice(x)
    return offspring

# 定义遗传算法的主函数
def genetic_algorithm():
    population = initialize_population()
    for i in range(num_generations):
        parents = selection(population)
        offspring = crossover(parents)
        offspring = mutation(offspring)
        population = parents + offspring
    best_chromosome = min(population, key=lambda x: objective_function(x))
    return best_chromosome

# 输出最优解
print(genetic_algorithm())

在上面的代码中，我们定义了一个``objective_function``函数来计算目标函数的值，它接受配送中心的位置、客户的位置、每个客户的需求量、每个配送中心的供应量和运输成本等参数，并返回总成本。我们还定义了``initialize_population``函数来初始化种群，``selection``函数来进行选择，``crossover``函数来进行交叉，``mutation``函数来进行变异，以及``genetic_algorithm``函数作为遗传算法的主函数。最后，我们调用``genetic_algorithm``函数来输出最优解。

需要注意的是，由于本文无法提供完整的数据集，因此在上面的代码中，我们只是用简单的列表来表示配送中心的位置、客户的位置、每个客户的需求量、每个配送中心的供应量和运输成本等参数，实际使用时需要根据具体的数据集进行修改。