我为 TSP 编写了这段模拟退火代码，我一整天都在尝试调试它，但出了点问题

Question

此代码旨在减少初始游览的距离： distan(initial_tour) < distan(best) 。你能帮我吗？我一整天都在努力。 我需要改变交换方式吗？ 出问题了，模拟退火不起作用：

def prob(currentDistance,neighbourDistance,temp):

    if neighbourDistance < currentDistance:
       return 1.0

    else:
       return math.exp( (currentDistance - neighbourDistance) / temp)


def distan(solution):

    #gives the distance of solution


    listax, listay = [], []
    for i in range(len(solution)):

       listax.append(solution[i].x)
       listay.append(solution[i].y)

    dists = np.linalg.norm(np.vstack([np.diff(np.array(listax)), np.diff(np.array(listay))]), axis=0)
    cumsum_dist = np.cumsum(dists)

    return cumsum_dist[-1]


#simulated annealing

temp = 1000000

#creating initial tour

shuffle(greedys)

initial_tour=greedys


print (distan(initial_tour))

current_best = initial_tour

best = current_best

while(temp >1 ):

    #create new neighbour tour 

    new_solution= current_best 

    #Get a random positions in the neighbour tour

    tourPos1=random.randrange(0, len(dfar))
    tourPos2=random.randrange(0, len(dfar))

    tourCity1=new_solution[tourPos1]
    tourCity2=new_solution[tourPos2]

    #swapping
    new_solution[tourPos1]=tourCity2
    new_solution[tourPos2]=tourCity1

    #get distance of both current_best and its neighbour 

    currentDistance = distan(current_best)

    neighbourDistance = distan(new_solution)


    # decide if we should accept the neighbour
    # random.random() returns a number in [0,1)

    if prob(currentDistance,neighbourDistance,temp) > random.random():

        current_best = new_solution 

    # keep track of the best solution found  

    if distan(current_best) <  distan(best):

        best = current_best

    #Cool system

    temp = temp*0.99995


print(distan(best))

Answer 1

你的问题出在你的 while 循环的第一行，你写

new_solution= current_best

它所做的是将对 current_best 列表的引用放入 new_solution。这意味着当您更改 new_solution 时，您实际上也在更改 current_best，这不是您的本意。

可以通过将有问题的行替换为将列表复制到新列表中的行来解决问题，如下所示：

new_solution = list(current_best)

我为 TSP 编写了这段模拟退火代码，我一整天都在尝试调试它，但出了点问题

I write this code of Simulated Annealing for TSP and I have been trying all day to debug it but something goes wrong

python

traveling-salesman

simulated-annealing