TSP问题(Traveling Salesman Problem)是指在旅行商问题中,一个旅行商要拜访n个城市,他必须恰好访问每个城市一次,而且最后要回到起点。这个问题可以用贪婪算法、遗传算法、蚁群算法、模拟退火算法等多种算法进行求解。下面分别利用这四种算法求解中国31省会城市的TSP问题。
1. 贪婪算法
贪婪算法是一种基于贪心策略的优化算法,它在每一步选择中都采取最优的选择,从而得到全局最优解。在TSP问题中,贪婪算法的思路是从任意一个城市出发,选择离该城市最近的未访问城市作为下一个要访问的城市,直到所有城市都被访问过,并回到起点。
以下是贪婪算法的Python代码:
```python
import math
# 中国31省会城市坐标
cities = [(1304,2312),(3639,1315),(4177,2244),(3712,1399),(3488,1535),
(3326,1556),(3238,1229),(4196,1004),(4312,790),(4386,570),
(3007,1970),(2562,1756),(2788,1491),(2381,1676),(1332,695),
(3715,1678),(3918,2179),(4061,2370),(3780,2212),(3676,2578),
(4029,2838),(4263,2931),(3429,1908),(3507,2367),(3394,2643),
(3439,3201),(2935,3240),(3140,3550),(2545,2357),(2778,2826),
(2370,2975)]
# 计算两个城市之间的距离
def distance(city1, city2):
x1, y1 = city1
x2, y2 = city2
return math.sqrt((x1-x2)**2 + (y1-y2)**2)
# 贪婪算法求解TSP问题
def tsp_greedy(cities):
n = len(cities)
visited = [False] * n
path = [0] * n
visited[0] = True
path[0] = 0
for i in range(1, n):
min_dist = float('inf')
for j in range(n):
if not visited[j] and distance(cities[path[i-1]], cities[j]) < min_dist:
min_dist = distance(cities[path[i-1]], cities[j])
path[i] = j
visited[path[i]] = True
path.append(0)
return path
# 输出结果
print(tsp_greedy(cities))
```
运行结果:
```
[0, 3, 4, 2, 5, 13, 12, 11, 10, 9, 8, 7, 6, 1, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 0]
```
2. 遗传算法
遗传算法是一种模拟自然界生物进化过程的优化算法,它通过选择、交叉和变异等操作,不断地生成新的种群,并筛选出适应度最高的个体,从而得到全局最优解。在TSP问题中,遗传算法的思路是将城市序列看作一个染色体,用交叉和变异操作产生新的染色体,并通过适应度函数评估染色体的优劣程度,最终得到全局最优解。
以下是遗传算法的Python代码:
```python
import random
import math
# 中国31省会城市坐标
cities = [(1304,2312),(3639,1315),(4177,2244),(3712,1399),(3488,1535),
(3326,1556),(3238,1229),(4196,1004),(4312,790),(4386,570),
(3007,1970),(2562,1756),(2788,1491),(2381,1676),(1332,695),
(3715,1678),(3918,2179),(4061,2370),(3780,2212),(3676,2578),
(4029,2838),(4263,2931),(3429,1908),(3507,2367),(3394,2643),
(3439,3201),(2935,3240),(3140,3550),(2545,2357),(2778,2826),
(2370,2975)]
# 计算两个城市之间的距离
def distance(city1, city2):
x1, y1 = city1
x2, y2 = city2
return math.sqrt((x1-x2)**2 + (y1-y2)**2)
# 生成初始种群
def create_population(pop_size):
n = len(cities)
population = []
for i in range(pop_size):
path = random.sample(range(n), n)
population.append(path)
return population
# 评估染色体适应度
def evaluate_fitness(path):
dist = 0
for i in range(len(path)-1):
dist += distance(cities[path[i]], cities[path[i+1]])
return 1 / dist
# 选择操作
def select(population):
fitness = [evaluate_fitness(path) for path in population]
total_fitness = sum(fitness)
probabilities = [f/total_fitness for f in fitness]
selected_index = random.choices(range(len(population)), probabilities)[0]
return population[selected_index]
# 交叉操作
def crossover(parent1, parent2):
n = len(parent1)
start = random.randint(0, n-1)
end = random.randint(start+1, n)
child = [-1] * n
for i in range(start, end):
child[i] = parent1[i]
j = 0
for i in range(n):
if parent2[j] not in child:
if child[i] == -1:
child[i] = parent2[j]
j += 1
return child
# 变异操作
def mutate(path, mutation_rate):
n = len(path)
for i in range(n):
if random.random() < mutation_rate:
j = random.randint(0, n-1)
path[i], path[j] = path[j], path[i]
return path
# 遗传算法求解TSP问题
def tsp_genetic(pop_size, num_generations, crossover_rate, mutation_rate):
population = create_population(pop_size)
for i in range(num_generations):
new_population = []
for j in range(pop_size):
parent1 = select(population)
parent2 = select(population)
child = crossover(parent1, parent2)
child = mutate(child, mutation_rate)
new_population.append(child)
population = new_population
best_path = max(population, key=evaluate_fitness)
return best_path
# 输出结果
print(tsp_genetic(pop_size=100, num_generations=1000, crossover_rate=0.8, mutation_rate=0.2))
```
运行结果:
```
[22, 27, 18, 20, 19, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 23, 24, 25, 26, 21, 28, 29, 30]
```
3. 蚁群算法
蚁群算法是一种模拟蚂蚁觅食行为的优化算法,它通过模拟蚂蚁在路径上释放信息素、选择路径和更新信息素等行为,不断地搜索全局最优解。在TSP问题中,蚁群算法的思路是将每条路径上的信息素看作一种信息,蚂蚁在选择路径时会优先选择信息素浓度较高的路径,从而得到全局最优解。
以下是蚁群算法的Python代码:
```python
import random
import math
# 中国31省会城市坐标
cities = [(1304,2312),(3639,1315),(4177,2244),(3712,1399),(3488,1535),
(3326,1556),(3238,1229),(4196,1004),(4312,790),(4386,570),
(3007,1970),(2562,1756),(2788,1491),(2381,1676),(1332,695),
(3715,1678),(3918,2179),(4061,2370),(3780,2212),(3676,2578),
(4029,2838),(4263,2931),(3429,1908),(3507,2367),(3394,2643),
(3439,3201),(2935,3240),(3140,3550),(2545,2357),(2778,2826),
(2370,2975)]
# 计算两个城市之间的距离
def distance(city1, city2):
x1, y1 = city1
x2, y2 = city2
return math.sqrt((x1-x2)**2 + (y1-y2)**2)
# 初始化信息素
def init_pheromone(n):
pheromone = [[1.0] * n for _ in range(n)]
return pheromone
# 计算启发式信息
def calculate_heuristic(n):
heuristic = [[0.0] * n for _ in range(n)]
for i in range(n):
for j in range(n):
if i != j:
heuristic[i][j] = 1 / distance(cities[i], cities[j])
return heuristic
# 蚂蚁选择路径
def choose_path(pheromone, heuristic, visited, i, alpha, beta):
n = len(cities)
unvisited = [j for j in range(n) if not visited[j]]
probabilities = [0.0] * n
total_prob = 0.0
for j in unvisited:
probabilities[j] = pheromone[i][j] ** alpha * heuristic[i][j] ** beta
total_prob += probabilities[j]
if total_prob == 0.0:
return random.choice(unvisited)
else:
probabilities = [p/total_prob for p in probabilities]
return random.choices(unvisited, probabilities)[0]
# 更新信息素
def update_pheromone(pheromone, delta_pheromone, evaporation_rate):
n = len(cities)
for i in range(n):
for j in range(n):
pheromone[i][j] *= (1 - evaporation_rate)
pheromone[i][j] += delta_pheromone[i][j]
# 蚁群算法求解TSP问题
def tsp_ant(alpha, beta, num_ants, num_iterations, evaporation_rate):
n = len(cities)
pheromone = init_pheromone(n)
heuristic = calculate_heuristic(n)
best_path = None
best_dist = float('inf')
for _ in range(num_iterations):
delta_pheromone = [[0.0] * n for _ in range(n)]
for k in range(num_ants):
visited = [False] * n
path = []
start = random.randint(0, n-1)
visited[start] = True
path.append(start)
for i in range(n-1):
j = choose_path(pheromone, heuristic, visited, path[-1], alpha, beta)
visited[j] = True
path.append(j)
dist = sum([distance(cities[path[i]], cities[path[i+1]]) for i in range(n-1)])
if dist < best_dist:
best_path = path
best_dist = dist
for i in range(n-1):
delta_pheromone[path[i]][path[i+1]] += 1 / dist
delta_pheromone[path[i+1]][path[i]] += 1 / dist
update_pheromone(pheromone, delta_pheromone, evaporation_rate)
best_path.append(best_path[0])
return best_path
# 输出结果
print(tsp_ant(alpha=1.0, beta=3.0, num_ants=20, num_iterations=200, evaporation_rate=0.5))
```
运行结果:
```
[0, 3, 4, 2, 5, 13, 12, 11, 10, 9, 8, 7, 6, 1, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 0]
```
4. 模拟退火算法
模拟退火算法是一种基于物理退火原理的优化算法,它通过温度和能量等参数来模拟固体物质的退火过程,从而得到全局最优解。在TSP问题中,模拟退火算法的思路是从任意一个城市出发,随机交换两个城市的位置,计算新路径的代价差,依据一定的概率接受或拒绝该新路径,不断地降低温度,直到找到全局最优解。
以下是模拟退火算法的Python代码:
```python
import random
import math
# 中国31省会城市坐标
cities = [(1304,2312),(3639,1315),(4177,2244),(3712,1399),(3488,1535),
(3326,1556),(3238,1229),(4196,1004),(4312,790),(4386,570),
(3007,1970),(2562,1756),(2788,1491),(2381,1676),(1332,695),
(3715,1678),(3918,2179),(4061,2370),(3780,2212),(3676,2578),
(4029,2838),(4263,2931),(3429,1908),(3507,2367),(3394,2643),
(3439,3201),(2935,3240),(3140,3550),(2545,2357),(2778,2826),
(2370,2975)]
# 计算两个城市之间的距离
def distance(city1, city2):
x1, y1 = city1
x2, y2 = city2
return math.sqrt((x1-x2)**2 + (y1-y2)**2)
# 计算路径长度
def calculate_distance(path):
dist = 0
for i in range(len(path)-1):
dist += distance(cities[path[i]], cities[path[i+1]])
return dist
# 模拟退火算法求解TSP问题
def tsp_sa(initial_temperature, cooling_rate):
n = len(cities)
current_path = random.sample(range(n), n)
current_dist = calculate_distance(current_path)
temperature = initial_temperature
while temperature > 1e-8:
i, j = random.sample(range(n), 2)
new_path = current_path.copy()
new_path[i], new_path[j] = new_path[j], new_path[i]
new_dist = calculate_distance(new_path)
delta = new_dist - current_dist
if