python遗传算法解决最优物流配车问题代码

下面是一个简单的 Python 遗传算法解决最优物流配车问题的代码示例：

import random

# 定义物流配车问题的参数
truck_capacity = 20 # 货车的容量
num_customers = 10 # 顾客数量
num_trucks = 5 # 货车数量
demand = [random.randint(1, 10) for i in range(num_customers)] # 顾客的需求量
distances = [[random.randint(1, 10) for j in range(num_customers)] for i in range(num_customers)] # 顾客之间的距离

# 定义遗传算法的参数
pop_size = 20 # 种群大小
num_generations = 100 # 迭代次数
mutation_rate = 0.1 # 变异率

# 定义一个个体类
class Individual:
    def __init__(self, genes):
        self.genes = genes # 基因序列
        self.fitness = 0 # 适应度值
    
    # 计算适应度值
    def calculate_fitness(self):
        truck_loads = [0 for i in range(num_trucks)] # 每个货车的负载
        truck_distances = [0 for i in range(num_trucks)] # 每个货车的行驶距离
        for i in range(num_customers):
            truck_loads[self.genes[i]] += demand[i]
            if truck_loads[self.genes[i]] > truck_capacity:
                self.fitness = 0 # 超载，适应度值为0
                return
            if i > 0:
                truck_distances[self.genes[i]] += distances[i-1][i]
        self.fitness = max(truck_distances) # 适应度值为最长行驶距离
    
    # 交叉操作
    def crossover(self, other):
        crossover_point = random.randint(1, num_customers-1)
        child1_genes = self.genes[:crossover_point] + other.genes[crossover_point:]
        child2_genes = other.genes[:crossover_point] + self.genes[crossover_point:]
        return Individual(child1_genes), Individual(child2_genes)
    
    # 变异操作
    def mutate(self):
        for i in range(num_customers):
            if random.random() < mutation_rate:
                self.genes[i] = random.randint(0, num_trucks-1)
    
    # 获取基因序列
    def get_genes(self):
        return self.genes

# 定义一个种群类
class Population:
    def __init__(self):
        self.individuals = [Individual([random.randint(0, num_trucks-1) for i in range(num_customers)]) for j in range(pop_size)]
    
    # 评估种群中每个个体的适应度值
    def evaluate(self):
        for individual in self.individuals:
            individual.calculate_fitness()
    
    # 选择操作
    def select(self):
        fitness_sum = sum([individual.fitness for individual in self.individuals])
        selected_individuals = []
        for i in range(pop_size):
            rand_num = random.uniform(0, fitness_sum)
            running_sum = 0
            for individual in self.individuals:
                running_sum += individual.fitness
                if running_sum > rand_num:
                    selected_individuals.append(individual)
                    break
        self.individuals = selected_individuals
    
    # 繁殖操作
    def breed(self):
        new_individuals = []
        for i in range(pop_size):
            parent1 = random.choice(self.individuals)
            parent2 = random.choice(self.individuals)
            child1, child2 = parent1.crossover(parent2)
            new_individuals.append(child1)
            new_individuals.append(child2)
        self.individuals = new_individuals
    
    # 变异操作
    def mutate(self):
        for individual in self.individuals:
            individual.mutate()
    
    # 获取最佳个体
    def get_best(self):
        return max(self.individuals, key=lambda individual: individual.fitness)

# 运行遗传算法
population = Population()
for generation in range(num_generations):
    population.evaluate()
    best_individual = population.get_best()
    print("Generation {}: Best Fitness = {}".format(generation+1, best_individual.fitness))
    population.select()
    population.breed()
    population.mutate()
best_individual = population.get_best()
print("Best Solution: {}".format(best_individual.get_genes()))
print("Best Fitness: {}".format(best_individual.fitness))

在这个示例中，我们定义了一个物流配车问题，其中有10个顾客，每个顾客有一个需求量，有5辆货车，每辆货车有一个容量限制。我们使用遗传算法来求解最佳配车方案，其中的每个个体是一个基因序列，表示每个顾客被分配到哪辆货车上。我们使用适应度函数来评估每个个体的适应度值，即每辆货车行驶的距离，我们希望最小化最长的行驶距离。在遗传算法的每一代中，我们使用选择、繁殖和变异操作来生成新的个体，并让种群逐渐收敛到最佳解。最终，我们输出最佳配车方案和对应的适应度值。

需要注意的是，这个示例只是一个简单的遗传算法实现，实际应用中可能需要更复杂的适应度函数、选择策略、繁殖策略和变异策略来获得更好的结果。