49 lines
1.7 KiB
Python
49 lines
1.7 KiB
Python
import numpy as np
|
|
|
|
from src.utils import compute_length
|
|
|
|
def step2opt(solution, matrix_dist, distance):
|
|
seq_length = len(solution) - 1
|
|
tsp_sequence = np.array(solution)
|
|
uncrosses = 0
|
|
for i in range(1, seq_length - 1):
|
|
for j in range(i + 1, seq_length):
|
|
new_tsp_sequence = swap2opt(tsp_sequence, i, j)
|
|
new_distance = distance + gain(i, j, tsp_sequence, matrix_dist)
|
|
if new_distance < distance:
|
|
uncrosses += 1
|
|
tsp_sequence = np.copy(new_tsp_sequence)
|
|
distance = new_distance
|
|
return tsp_sequence, distance, uncrosses
|
|
|
|
|
|
def swap2opt(tsp_sequence, i, j):
|
|
new_tsp_sequence = np.copy(tsp_sequence)
|
|
new_tsp_sequence[i:j + 1] = np.flip(tsp_sequence[i:j + 1], axis=0) # flip or swap ?
|
|
return new_tsp_sequence
|
|
|
|
|
|
def gain(i, j, tsp_sequence, matrix_dist):
|
|
old_link_len = (matrix_dist[tsp_sequence[i], tsp_sequence[i - 1]] + matrix_dist[
|
|
tsp_sequence[j], tsp_sequence[j + 1]])x
|
|
changed_links_len = (matrix_dist[tsp_sequence[j], tsp_sequence[i - 1]] + matrix_dist[
|
|
tsp_sequence[i], tsp_sequence[j + 1]])
|
|
return - old_link_len + changed_links_len
|
|
|
|
|
|
def loop2opt(solution, instance, max_num_of_uncrosses=10000):
|
|
matrix_dist = instance.dist_matrix
|
|
new_len = compute_length(solution, matrix_dist)
|
|
new_tsp_sequence = np.copy(np.array(solution))
|
|
uncross = 0
|
|
while uncross < max_num_of_uncrosses:
|
|
new_tsp_sequence, new_reward, uncr_ = step2opt(new_tsp_sequence, matrix_dist, new_len)
|
|
uncross += uncr_
|
|
if new_reward < new_len:
|
|
new_len = new_reward
|
|
else:
|
|
return new_tsp_sequence.tolist()
|
|
|
|
# return new_tsp_sequence.tolist(), new_len, uncross
|
|
return new_tsp_sequence.tolist()
|