python遗传算法代码实现典型的物流配送问题

好的，下面是一个 Python 实现的遗传算法，用于解决典型的物流配送问题：

import random

# 工地和料场的位置和需求量
locations = [{'name': 'A', 'x': 5, 'y': 1, 'demand': 5},
             {'name': 'B', 'x': 2, 'y': 7, 'demand': 7},
             {'name': 'C', 'x': 6, 'y': 6, 'demand': 8},
             {'name': 'D', 'x': 1, 'y': 3, 'demand': 6},
             {'name': 'E', 'x': 7, 'y': 2, 'demand': 3},
             {'name': 'F', 'x': 2, 'y': 5, 'demand': 2}]
# 料场的位置和储量
depots = [{'name': 'A', 'x': 5, 'y': 1, 'supply': 20},
          {'name': 'B', 'x': 2, 'y': 7, 'supply': 20}]

# 遗传算法参数
POP_SIZE = 100  # 种群大小
GENE_SIZE = len(locations) * len(depots)  # 基因长度
CROSS_RATE = 0.8  # 交叉概率
MUTATION_RATE = 0.01  # 变异概率
N_GENERATIONS = 200  # 迭代次数


# 生成随机个体
def generate_individual():
    individual = []
    for i in range(GENE_SIZE):
        # 随机生成每个基因的值，即从一个节点到另一个节点运输的吨数
        individual.append(random.randint(0, 20))
    return individual


# 计算个体的适应度
def calculate_fitness(individual):
    # 将个体的基因编码转换成矩阵形式，方便计算
    matrix = []
    for i in range(len(depots)):
        row = []
        for j in range(len(locations)):
            row.append(individual[i * len(locations) + j])
        matrix.append(row)

    # 计算每个工地的需求量和每个料场的供应量
    demands = [loc['demand'] for loc in locations]
    supplies = [depot['supply'] for depot in depots]

    # 初始化每个节点的剩余需求量和供应量
    remaining_demands = list(demands)
    remaining_supplies = list(supplies)

    # 计算每个节点之间的距离和运输量
    total_distance = 0
    for i in range(len(matrix)):
        for j in range(len(matrix[i])):
            distance = ((depots[i]['x'] - locations[j]['x']) ** 2 + (depots[i]['y'] - locations[j]['y']) ** 2) ** 0.5
            total_distance += distance * matrix[i][j]
            remaining_demands[j] -= matrix[i][j]
            remaining_supplies[i] -= matrix[i][j]

    # 计算剩余供应量和需求量的总和
    total_remaining_demand = sum([max(0, d) for d in remaining_demands])
    total_remaining_supply = sum([max(0, s) for s in remaining_supplies])

    # 计算适应度，即总吨千米数
    fitness = total_distance + total_remaining_demand + total_remaining_supply

    return fitness


# 选择操作
def selection(population):
    # 计算每个个体的适应度
    fitnesses = [calculate_fitness(individual) for individual in population]

    # 选择适应度最高的前一半个体
    sorted_population = [x for _, x in sorted(zip(fitnesses, population))]
    half = len(sorted_population) // 2
    selected_population = sorted_population[:half]

    return selected_population


# 交叉操作
def crossover(parent1, parent2):
    # 随机选择一个交叉点
    crossover_point = random.randint(0, GENE_SIZE - 1)

    # 交叉操作
    child1 = parent1[:crossover_point] + parent2[crossover_point:]
    child2 = parent2[:crossover_point] + parent1[crossover_point:]

    return child1, child2


# 变异操作
def mutation(individual):
    # 随机选择一个基因进行变异
    mutation_point = random.randint(0, GENE_SIZE - 1)

    # 变异操作
    individual[mutation_point] = random.randint(0, 20)

    return individual


# 遗传算法主程序
def genetic_algorithm():
    # 生成初始种群
    population = [generate_individual() for _ in range(POP_SIZE)]

    # 迭代 N_GENERATIONS 次
    for generation in range(N_GENERATIONS):
        print('Generation:', generation)

        # 选择操作
        selected_population = selection(population)

        # 交叉操作
        for i in range(0, len(selected_population), 2):
            parent1 = selected_population[i]
            parent2 = selected_population[i + 1]
            if random.random() < CROSS_RATE:
                child1, child2 = crossover(parent1, parent2)
                selected_population[i] = child1
                selected_population[i + 1] = child2

        # 变异操作
        for i in range(len(selected_population)):
            if random.random() < MUTATION_RATE:
                selected_population[i] = mutation(selected_population[i])

        # 生成新种群
        new_population = selected_population + [generate_individual() for _ in range(POP_SIZE - len(selected_population))]

        # 更新种群
        population = new_population

    # 选择适应度最高的个体
    fitnesses = [calculate_fitness(individual) for individual in population]
    fittest_individual = population[fitnesses.index(min(fitnesses))]

    return fittest_individual


# 测试
best_individual = genetic_algorithm()
print('Best individual:', best_individual)

在这个例子中，我们首先定义了工地和料场的位置、需求量、储量等参数。然后，我们定义了遗传算法的参数，如种群大小、基因长度等。接着，我们实现了生成随机个体、计算适应度、选择、交叉和变异等遗传算子，以及遗传算法的主程序。最后，我们通过调用主程序，得到最优解。

需要注意的是，由于 Python 的运行速度较慢，因此在处理大规模的问题时可能需要更长的运行时间。同时，由于遗传算法本身的随机性，可能会得到不同的结果。因此，在实际应用中，需要多次运行算法，取多次结果的平均值作为最终结果。