写一个使用混合蛙跳算法解决TSP问题的Python程序
时间: 2024-04-30 22:19:27 浏览: 20
以下是一个使用混合蛙跳算法解决TSP问题的Python程序:
```python
import random
import math
# 初始化参数
num_iterations = 1000 # 迭代次数
population_size = 20 # 种群数量
elite_size = 10 # 精英数量
mutation_rate = 0.01 # 变异率
city_list = [(60, 200), (180, 200), (80, 180), (140, 180), (20, 160), (100, 160), (200, 160), (140, 140), (40, 120), (100, 120), (180, 100), (60, 80), (120, 80), (180, 60), (20, 40), (100, 40), (200, 40), (20, 20), (60, 20), (160, 20)] # 城市坐标
# 计算两个城市之间的距离
def distance(city1, city2):
x_dist = abs(city1[0] - city2[0])
y_dist = abs(city1[1] - city2[1])
dist = math.sqrt(x_dist ** 2 + y_dist ** 2)
return dist
# 计算一条路径的总距离
def path_distance(path):
dist = 0
for i in range(len(path) - 1):
dist += distance(city_list[path[i]], city_list[path[i+1]])
return dist
# 初始化种群
def initial_population(population_size, city_list):
population = []
for i in range(population_size):
path = list(range(len(city_list)))
random.shuffle(path)
population.append(path)
return population
# 选择精英
def select_elite(population, elite_size):
paths = []
for i in range(len(population)):
paths.append((path_distance(population[i]), population[i]))
paths.sort()
elite = []
for i in range(elite_size):
elite.append(paths[i][1])
return elite
# 交叉操作
def crossover(parent1, parent2):
child = [-1] * len(parent1)
start_index = random.randint(0, len(parent1)-1)
end_index = random.randint(start_index, len(parent1)-1)
for i in range(start_index, end_index):
child[i] = parent1[i]
for i in range(len(parent2)):
if parent2[i] not in child:
for j in range(len(child)):
if child[j] == -1:
child[j] = parent2[i]
break
return child
# 变异操作
def mutate(path, mutation_rate):
for i in range(len(path)):
if random.random() < mutation_rate:
j = random.randint(0, len(path)-1)
path[i], path[j] = path[j], path[i]
return path
# 混合蛙跳算法
def hybrid_frog_leaping(num_iterations, population_size, elite_size, mutation_rate, city_list):
population = initial_population(population_size, city_list)
for i in range(num_iterations):
elite = select_elite(population, elite_size)
for j in range(len(population)):
parent1 = random.choice(elite)
parent2 = random.choice(elite)
child = crossover(parent1, parent2)
child = mutate(child, mutation_rate)
population[j] = child
elite = select_elite(population, elite_size)
return elite[0]
# 执行混合蛙跳算法
best_path = hybrid_frog_leaping(num_iterations, population_size, elite_size, mutation_rate, city_list)
print("最短路径:", best_path)
print("路径距离:", path_distance(best_path))
```
这个程序使用了混合蛙跳算法来解决TSP问题。它首先初始化种群,然后在每次迭代中选择精英,并对每个个体进行交叉和变异操作。最后,它选择最优的个体作为结果,并输出最短路径和路径距离。