Question

此代码假设减少初始巡视的距离：distan（initial_tour）＆lt; distan（最好的）。你能帮帮我吗？我现在一整天都在努力。 我是否需要更改交换方法？ 出了点问题，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

您的问题出现在while循环的第一行，您可以在其中编写

new_solution= current_best

这样做会将current_best列表引用到new_solution。这意味着当您更改new_solution时，您实际上也在改变current_best，这不是您的意图。

问题可以通过将有问题的行替换为将列表复制到新列表中来解决，如下所示：

new_solution = list(current_best)

我为TSP编写了这个模拟退火代码，我一整天都在尝试调试它，但出了点问题

1 个答案: