81 lines
3.4 KiB
Python
81 lines
3.4 KiB
Python
import numpy as np
|
|
|
|
from src.utils import compute_length
|
|
from src.two_opt import swap2opt, gain
|
|
|
|
|
|
def step2dot5opt(solution, matrix_dist, distance):
|
|
seq_length = len(solution) - 2
|
|
tsp_sequence = np.array(solution)
|
|
uncrosses = 0
|
|
for i in range(1, seq_length - 1):
|
|
for j in range(i + 1, seq_length):
|
|
# 2opt swap
|
|
two_opt_tsp_sequence = swap2opt(tsp_sequence, i, j)
|
|
two_opt_len = distance + gain(i, j, tsp_sequence, matrix_dist)
|
|
# node shift 1
|
|
first_shift_tsp_sequence = shift1(tsp_sequence, i, j)
|
|
first_shift_len = distance + shift_gain1(i, j, tsp_sequence, matrix_dist)
|
|
# node shift 2
|
|
second_shift_tsp_sequence = shift2(tsp_sequence, i, j)
|
|
second_shift_len = distance + shift_gain2(i, j, tsp_sequence, matrix_dist)
|
|
|
|
best_len, best_method = min([two_opt_len, first_shift_len, second_shift_len]), np.argmin(
|
|
[two_opt_len, first_shift_len, second_shift_len])
|
|
sequences = [two_opt_tsp_sequence, first_shift_tsp_sequence, second_shift_tsp_sequence]
|
|
if best_len < distance:
|
|
uncrosses += 1
|
|
tsp_sequence = sequences[best_method]
|
|
distance = best_len
|
|
print(i, j, best_len)
|
|
#print(distance, best_method, [twoOpt_len, first_shift_len, second_shift_len])
|
|
return tsp_sequence, distance, uncrosses
|
|
|
|
|
|
def shift1(tsp_sequence, i, j):
|
|
new_tsp_sequence = np.concatenate(
|
|
[tsp_sequence[:i], tsp_sequence[i + 1: j + 1], [tsp_sequence[i]], tsp_sequence[j + 1:]])
|
|
return new_tsp_sequence
|
|
|
|
|
|
def shift_gain1(i, j, tsp_sequence, matrix_dist):
|
|
old_link_len = (matrix_dist[tsp_sequence[i], tsp_sequence[i - 1]] +
|
|
matrix_dist[tsp_sequence[i], tsp_sequence[i + 1]] +
|
|
matrix_dist[tsp_sequence[j], tsp_sequence[j + 1]])
|
|
changed_links_len = (matrix_dist[tsp_sequence[i - 1], tsp_sequence[i + 1]] +
|
|
matrix_dist[tsp_sequence[i], tsp_sequence[j]]
|
|
+ matrix_dist[tsp_sequence[i], tsp_sequence[j + 1]])
|
|
return - old_link_len + changed_links_len
|
|
|
|
|
|
def shift2(tsp_sequence, i, j):
|
|
new_tsp_sequence = np.concatenate(
|
|
[tsp_sequence[:i], [tsp_sequence[j]], tsp_sequence[i: j], tsp_sequence[j + 1:]])
|
|
return new_tsp_sequence
|
|
|
|
|
|
def shift_gain2(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]] + matrix_dist[tsp_sequence[j], tsp_sequence[j + 1]])
|
|
changed_links_len = (
|
|
matrix_dist[tsp_sequence[j], tsp_sequence[i - 1]] + matrix_dist[tsp_sequence[i], tsp_sequence[j]] +
|
|
matrix_dist[tsp_sequence[j - 1], tsp_sequence[j + 1]])
|
|
return - old_link_len + changed_links_len
|
|
|
|
|
|
def loop2dot5opt(solution, instance, max_num_of_changes=2500):
|
|
matrix_dist = instance.dist_matrix
|
|
actual_len = compute_length(solution, matrix_dist)
|
|
new_tsp_sequence = np.copy(np.array(solution))
|
|
uncross = 0
|
|
while uncross < max_num_of_changes:
|
|
new_tsp_sequence, new_len, uncr_ = step2dot5opt(new_tsp_sequence, matrix_dist, actual_len)
|
|
uncross += uncr_
|
|
# print(new_len, uncross)
|
|
if new_len < actual_len:
|
|
actual_len = new_len
|
|
else:
|
|
return new_tsp_sequence.tolist()
|
|
|
|
return new_tsp_sequence.tolist()
|