van boeren - 1 month ago 15
Python Question

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

This code suppose to reduce the distance of initial tour: distan(initial_tour) < distan(best) . Can you help me plz? I 've been trying all day now. Do I need to change my swapping method?
Something goes wrong and the simulated annealing does'not work:

``````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))
``````

Your problem is in the first line of your `while` loop, where you write
``````new_solution= current_best
What this does is puts a reference to the `current_best` list into `new_solution`. This means that when you change `new_solution`, you're actually changing `current_best` as well, which was not your intention.
``````new_solution = list(current_best)