遗传算法求解路径规划的代码

遗传算法是一种优化算法，可以用于求解路径规划问题。下面是一个使用遗传算法求解路径规划的示例代码（假设路径规划问题是在一个二维空间中寻找最短路径）：

import random

# 生成随机个体
def generate_individual(length):
    return [random.randint(0, 100) for _ in range(length)]

# 生成初始种群
def generate_population(pop_size, ind_length):
    return [generate_individual(ind_length) for _ in range(pop_size)]

# 计算个体适应度（路径长度）
def calculate_fitness(individual):
    # 此处根据具体问题定义适应度函数
    # 这里假设路径长度越短适应度越高
    fitness = 0
    for i in range(len(individual)-1):
        x1, y1 = individual[i]
        x2, y2 = individual[i+1]
        dist = ((x2 - x1)**2 + (y2 - y1)**2)**0.5
        fitness += dist
    return fitness

# 选择操作（轮盘赌算法）
def selection(population):
    fitness_values = [calculate_fitness(individual) for individual in population]
    total_fitness = sum(fitness_values)
    probabilities = [fitness / total_fitness for fitness in fitness_values]
    selected_indices = random.choices(range(len(population)), probabilities, k=2)
    return [population[i] for i in selected_indices]

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

# 变异操作（随机交换两个位置的值）
def mutation(individual, mutation_rate):
    for i in range(len(individual)):
        if random.random() < mutation_rate:
            swap_index = random.randint(0, len(individual)-1)
            individual[i], individual[swap_index] = individual[swap_index], individual[i]
    return individual

# 遗传算法主程序
def genetic_algorithm(pop_size, ind_length, num_generations, crossover_rate, mutation_rate):
    population = generate_population(pop_size, ind_length)
    for _ in range(num_generations):
        next_generation = []
        while len(next_generation) < pop_size:
            parent1, parent2 = selection(population)
            if random.random() < crossover_rate:
                offspring1, offspring2 = crossover(parent1, parent2)
                next_generation.append(mutation(offspring1, mutation_rate))
                next_generation.append(mutation(offspring2, mutation_rate))
            else:
                next_generation.append(mutation(parent1, mutation_rate))
                next_generation.append(mutation(parent2, mutation_rate))
        population = next_generation
    best_individual = min(population, key=calculate_fitness)
    return best_individual

# 使用示例
pop_size = 100
ind_length = 10
num_generations = 100
crossover_rate = 0.8
mutation_rate = 0.1

best_path = genetic_algorithm(pop_size, ind_length, num_generations, crossover_rate, mutation_rate)
print("Best path:", best_path)

以上是一个简单的使用遗传算法求解路径规划问题的示例代码，代码中仅包含了基本的算法流程，实际应用中需根据具体问题进行适当的调整和优化。