遗传算法路径规划python

遗传算法是一种优化算法，可用于解决路径规划问题。在Python中，您可以使用遗传算法库进行路径规划的实现。以下是一个简单的示例：

import random

# 定义路径规划问题
# 假设我们有一个包含城市坐标的列表，需要找到最短路径经过所有城市并回到起点

city_list = [(2, 4), (5, 7), (8, 3), (1, 6), (9, 2)]

# 定义遗传算法参数
population_size = 100  # 种群大小
elite_size = 20  # 精英个体数量
mutation_rate = 0.01  # 变异率
generations = 100  # 迭代次数

# 创建一个路径类表示个体
class Path:
    def __init__(self, path):
        self.path = path
        self.distance = 0

    def calculate_distance(self):
        total_distance = 0
        for i in range(len(self.path) - 1):
            city1 = self.path[i]
            city2 = self.path[i + 1]
            total_distance += ((city2[0] - city1[0]) ** 2 + (city2[1] - city1[1]) ** 2) ** 0.5
        self.distance = total_distance

# 创建初始种群
def create_population():
    population = []
    for _ in range(population_size):
        path = random.sample(city_list, len(city_list))
        population.append(Path(path))
    return population

# 计算每个个体的适应度（路径长度）
def calculate_fitness(population):
    for path in population:
        path.calculate_distance()

# 选择精英个体
def select_elite(population):
    sorted_population = sorted(population, key=lambda x: x.distance)
    return sorted_population[:elite_size]

# 交叉操作
def crossover(parent1, parent2):
    child = []
    gene_a = int(random.random() * len(parent1.path))
    gene_b = int(random.random() * len(parent1.path))
    start_gene = min(gene_a, gene_b)
    end_gene = max(gene_a, gene_b)

    for i in range(start_gene, end_gene):
        child.append(parent1.path[i])

    for city in parent2.path:
        if city not in child:
            child.append(city)

    return Path(child)

# 变异操作
def mutate(path):
    for _ in range(len(path.path)):
        if random.random() < mutation_rate:
            index1 = int(random.random() * len(path.path))
            index2 = int(random.random() * len(path.path))
            path.path[index1], path.path[index2] = path.path[index2], path.path[index1]

# 迭代进化
def evolve(population):
    elite = select_elite(population)
    new_population = elite

    while len(new_population) < population_size:
        parent1 = random.choice(elite)
        parent2 = random.choice(elite)
        child = crossover(parent1, parent2)
        mutate(child)
        new_population.append(child)

    return new_population

# 主函数
def main():
    population = create_population()

    for _ in range(generations):
        calculate_fitness(population)
        population = evolve(population)

    best_path = min(population, key=lambda x: x.distance)
    print("最短路径：", best_path.path)
    print("最短路径长度：", best_path.distance)

if __name__ == '__main__':
    main()

上述代码演示了如何使用遗传算法解决路径规划问题。您可以根据实际需求进行扩展和修改。希望对您有所帮助！