我正在努力实现基于模拟退火的程序来解决旅行商问题。我得到的所有解决方案都不令人满意，我也不知道如何改善实施。显然，我不是在关注基准，而只是在寻找视觉上可接受的最短路径。如果有人能启发我，我将不胜感激。
# weight function, simple euclidean norm
def road(X,Y):
sum = 0
size = len(X) -1
for i in range(0,size):
sum +=math.sqrt((X[i]-X[i+1])**2 + (Y[i]-Y[i+1])**2)
return sum
def array_swap(X,Y,index_1,index_2):
X[index_1],X[index_2] = X[index_2],X[index_1]
Y[index_1],Y[index_2] = Y[index_2],Y[index_1]
def arbitrarty_swap(X,Y):
ran = len(X)-1
pick_1 = random.randint(0,ran)
pick_2 = random.randint(0,ran)
X[pick_1],X[pick_2] = X[pick_2],X[pick_1]
Y[pick_1],Y[pick_2] = Y[pick_2],Y[pick_1]
return pick_1, pick_2
N = 40
X = np.random.rand(N) * 100
Y = np.random.rand(N) * 100
plt.plot(X, Y, '-o')
plt.show()
best = road(X,Y)
X1 = X.copy()
Y1 = Y.copy() …