遗传算法优化公交调度代码

遗传算法是一种基于自然选择和遗传遗传机制的优化算法，可以用于公交调度问题的优化。下面是一个简单的基于遗传算法的公交调度代码：

1. 定义适应度函数：适应度函数是用来评价每个个体解的好坏程度的函数，对于公交调度问题，可以定义适应度函数为每个公交车的平均等待时间最小化。

2. 初始化种群：根据问题需求，随机生成一组初始解作为种群。

3. 选择操作：通过轮盘赌选择或其他选择方式从种群中选择父代个体。

4. 交叉操作：对于选中的父代个体，进行交叉操作，生成新的子代个体。

5. 变异操作：对于新生成的子代个体，进行一定概率的变异操作，生成最终的个体解。

6. 评价适应度：对于每个个体解，计算其适应度值。

7. 选择优秀个体：根据适应度值，选择最优秀的个体解，作为下一代种群的父代。

8. 终止条件：根据实际需求，设置终止条件（如达到最大迭代次数，或达到一定的适应度值等）。

完整的代码实现可以参考以下步骤：

1.定义适应度函数

def fitness(solution):
    # 计算每个公交车的平均等待时间
    # 返回平均等待时间的倒数作为适应度值，因为我们要最小化等待时间
    return 1 / average_wait_time(solution)

2.初始化种群

def init_population(pop_size, num_buses, num_stops):
    population = []
    for i in range(pop_size):
        solution = []
        for j in range(num_buses):
            bus_schedule = random_schedule(num_stops)
            solution.append(bus_schedule)
        population.append(solution)
    return population

3.选择操作

def roulette_wheel_selection(population, fitnesses):
    total_fitness = sum(fitnesses)
    selection_probs = [fitness / total_fitness for fitness in fitnesses]
    selected_index = np.random.choice(len(population), p=selection_probs)
    return population[selected_index]

4.交叉操作

def crossover(parent1, parent2):
    point = np.random.randint(1, len(parent1) - 1)
    child1 = parent1[:point] + parent2[point:]
    child2 = parent2[:point] + parent1[point:]
    return child1, child2

5.变异操作

def mutate(solution, mutation_prob):
    for i in range(len(solution)):
        if np.random.rand() < mutation_prob:
            solution[i] = random_schedule(len(solution[i]))
    return solution

6.评价适应度

def evaluate_population(population):
    return [fitness(solution) for solution in population]

7.选择优秀个体

def select_best(population, fitnesses):
    best_index = np.argmax(fitnesses)
    return population[best_index]

8.终止条件

def termination_condition(iteration, max_iterations, target_fitness=None):
    if iteration >= max_iterations:
        return True
    if target_fitness is not None and fitnesses[0] >= target_fitness:
        return True
    return False

完整的遗传算法优化公交调度代码示例如下：

import numpy as np

# 定义公交调度问题相关参数
NUM_BUSES = 5
NUM_STOPS = 10
MUTATION_PROB = 0.1
POP_SIZE = 50
MAX_ITERATIONS = 100

# 初始化公交车的时刻表
def random_schedule(num_stops):
    return np.random.permutation(num_stops)

# 计算每个公交车的平均等待时间
def average_wait_time(solution):
    stops_per_bus = [set(schedule) for schedule in solution]
    wait_times = np.zeros(NUM_STOPS)
    for i in range(NUM_STOPS):
        buses_at_stop = [b for b, s in enumerate(stops_per_bus) if i in s]
        if len(buses_at_stop) > 0:
            wait_times[i] = np.sum([np.abs(j - i) for j in stops_per_bus[buses_at_stop]])
    return np.mean(wait_times)

# 定义适应度函数
def fitness(solution):
    return 1 / average_wait_time(solution)

# 初始化种群
def init_population(pop_size, num_buses, num_stops):
    population = []
    for i in range(pop_size):
        solution = []
        for j in range(num_buses):
            bus_schedule = random_schedule(num_stops)
            solution.append(bus_schedule)
        population.append(solution)
    return population

# 选择操作
def roulette_wheel_selection(population, fitnesses):
    total_fitness = sum(fitnesses)
    selection_probs = [fitness / total_fitness for fitness in fitnesses]
    selected_index = np.random.choice(len(population), p=selection_probs)
    return population[selected_index]

# 交叉操作
def crossover(parent1, parent2):
    point = np.random.randint(1, len(parent1) - 1)
    child1 = parent1[:point] + parent2[point:]
    child2 = parent2[:point] + parent1[point:]
    return child1, child2

# 变异操作
def mutate(solution, mutation_prob):
    for i in range(len(solution)):
        if np.random.rand() < mutation_prob:
            solution[i] = random_schedule(len(solution[i]))
    return solution

# 评价适应度
def evaluate_population(population):
    return [fitness(solution) for solution in population]

# 选择优秀个体
def select_best(population, fitnesses):
    best_index = np.argmax(fitnesses)
    return population[best_index]

# 终止条件
def termination_condition(iteration, max_iterations, target_fitness=None):
    if iteration >= max_iterations:
        return True
    if target_fitness is not None and fitnesses[0] >= target_fitness:
        return True
    return False

# 遗传算法优化公交调度
def optimize_bus_schedule():
    # 初始化种群
    population = init_population(POP_SIZE, NUM_BUSES, NUM_STOPS)
    # 开始迭代优化
    for i in range(MAX_ITERATIONS):
        # 评价适应度
        fitnesses = evaluate_population(population)
        # 选择优秀个体
        best_solution = select_best(population, fitnesses)
        # 输出当前迭代的最优解
        print("Iteration {}: Best Fitness = {}".format(i, fitness(best_solution)))
        # 判断终止条件
        if termination_condition(i, MAX_ITERATIONS):
            break
        # 新一代种群
        new_population = [best_solution]
        while len(new_population) < POP_SIZE:
            # 选择父代个体
            parent1 = roulette_wheel_selection(population, fitnesses)
            parent2 = roulette_wheel_selection(population, fitnesses)
            # 交叉操作
            child1, child2 = crossover(parent1, parent2)
            # 变异操作
            child1 = mutate(child1, MUTATION_PROB)
            child2 = mutate(child2, MUTATION_PROB)
            # 添加子代个体到新一代种群
            new_population.append(child1)
            new_population.append(child2)
        # 更新种群
        population = new_population

    # 返回最优解
    return best_solution