while improved:
improved = False
for si in range(t.tour.n):
for sj in range(si + 2, t.tour.n):
t.tour.insert_new_node(t.tour.midpoint(si, sj))
t.optimize()
t.tour.remove_last_xy()
new_length = t.tour.tour_length()
if new_length < best_length:
t.optimize()
best_length = t.tour.tour_length()
best = t.tour.node_ids[:]
print("best length: " + str(best_length))
improved = True
else:
t.tour.reset(best)
if __name__ == "__main__":
xy = reader.read_xy("input/berlin52.tsp")
t = TwoOpt(xy)
t.optimize()
t.tour.plot()
inserter(t)
t.tour.validate()
print("final length: " + str(t.tour.tour_length()))
t.tour.plot()
t.tour.show()
def perturbed_hill_climb_naive(xy, tour):
tries = 0
success = 0
best_length = tour_util.length(xy, tour)
while True:
new_tour, naive_new_length = two_opt.optimize(xy, tour_util.double_bridge(tour)) # double bridge
#test_tour = tour[:]
#random.shuffle(test_tour)
#new_tour, naive_new_length = two_opt.optimize(xy, test_tour) # random restart
naive_gain = best_length - naive_new_length
if naive_gain > 0:
tour = new_tour
best_length = naive_new_length
success += 1
tries += 1
current_length = basic.tour_length(xy, tour)
assert(best_length == current_length)
if current_length <= TARGET_LENGTH:
break
print('current best: {} (iteration {}), improvement rate: {}'.format(best_length, tries, success / tries))
if __name__ == "__main__":
print('stopping at target length {}'.format(TARGET_LENGTH))
problem_name = 'xqf131'
xy = reader.read_xy("problems/{}.tsp".format(problem_name))
tour = tour_util.default(xy)
tour, improvement = two_opt.optimize(xy, tour)
perturbed_hill_climb(xy, tour)
#!/usr/bin/env python3
import reader
from two_opt import TwoOpt
xy = reader.read_xy("input/berlin52.tsp")
xy = reader.read_xy("input/xqf131.tsp")
t = TwoOpt(xy)
t.optimize()
print("local optimum: " + str(t.tour.tour_length()))
best_length = t.tour.tour_length()
best = t.tour.node_ids[:]
for i in range(50):
print(i)
t.tour.randomize()
t.optimize()
new_length = t.tour.tour_length()
if new_length < best_length:
best = t.tour.node_ids[:]
best_length = new_length
print("best length: " + str(best_length))
else:
t.tour.reset(best)
print("trial tour length: " + str(trial_length))
old_edges, new_edges = tour_diff(best_order, trial_order)
basic.write_edges(old_edges, "output/old_edges_test.txt")
basic.write_edges(new_edges, "output/new_edges_test.txt")
best_order = merge(xy, best_order, trial_order)
new_length = basic.tour_length(xy, best_order)
print("new length: " + str(new_length))
assert (trial_length >= new_length)
assert (len(best_order) == original_length)
return best_order
if __name__ == "__main__":
xy = reader.read_xy("../data/xqf131.tsp")
order = [i for i in range(len(xy))]
random.shuffle(order)
order = opt2(order)
print("initial tour length: " + str(basic.tour_length(xy, order)))
for i in range(1000):
basic.write_edges_from_order(order, "output/order_test.txt")
print("merge iteration: " + str(i))
premerge_length = basic.tour_length(xy, order)
#order = merge_nn(xy, order, i)
order = merge_double_bridge(xy, order, i)
postmerge_length = basic.tour_length(xy, order)
if postmerge_length != premerge_length:
order = opt2(order)
postmerge_climb_length = basic.tour_length(xy, order)
edges2 = set(edges2)
same = []
for edge in edges1:
if edge in edges2:
same.append(edge)
return same
if __name__ == '__main__':
problem_name = 'xqf131'
lengths = [589, 592]
lengths = [589, 596]
edges1 = read_tour('solutions/{}_{}.tour'.format(problem_name, lengths[0]))
edges2 = read_tour('solutions/{}_{}.tour'.format(problem_name, lengths[1]))
same = common(edges1, edges2)
xy = reader.read_xy('problems/{}.tsp'.format(problem_name))
diff1, diff2 = difference(edges1, edges2)
for edge in diff1:
a = xy[edge[0]]
b = xy[edge[1]]
plt.plot([a[0], b[0]], [a[1], b[1]], 'x-b')
for edge in diff2:
a = xy[edge[0]]
b = xy[edge[1]]
plt.plot([a[0], b[0]], [a[1], b[1]], 'x-r')
for edge in same:
a = xy[edge[0]]
b = xy[edge[1]]
plt.plot([a[0], b[0]], [a[1], b[1]], 'k:')
plt.show()