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new_mp_utils.py
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new_mp_utils.py
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import numpy as np
import random
from scipy.spatial import distance_matrix
def create_tour(N):
"""
Create an initial tour for the TSP
:param int tour_length: Tour length
:param bool rand: Generate random tour
:return: list with a TSP tour
"""
tour = random.sample(range(N), N)
return list(tour)
def calculate_distances(positions):
"""
Calculate a all distances between poistions
:param np.array positions: Positions of (tour_len, 2) points
:return: list with all distances
"""
# def length(x, y):
# return np.linalg.norm(np.asarray(x) - np.asarray(y))
# distances = [[length(x, y) for y in positions] for x in positions]
distances = distance_matrix(positions, positions)
return distances
def route_distance(tour, distances):
"""
Calculate a tour distance (including 0)
:param list tour: TSP tour
:param list : list with all distances
:return dist: Distance of a tour
"""
dist = 0
prev = tour[-1]
for node in tour:
dist += distances[int(prev)][int(node)]
prev = node
return dist
def swap_2opt(tour, i, k):
"""
Swaps two edges by reversing a section of nodes
:param list tour: TSP tour
:param int i: First index for the swap
:param int j: Second index for the swap
"""
# assert tour[0] == 0 and tour[-1] != 0
if k <= i:
i_a = i
i = k
k = i_a
assert i >= 0 and i < (len(tour) - 1)
assert k >= i and k < len(tour)
new_tour = tour[0:i]
new_tour = np.append(new_tour, np.flip(tour[i:k + 1], axis=0))
new_tour = np.append(new_tour, tour[k+1:])
# assert len(new_tour) == len(tour)
new_tour = [int(i) for i in new_tour]
return list(new_tour)
def heuristic_2opt_fi(positions):
"""
Improves an existing route using 2-opt until no improvement is found
:param list tour: TSP tour
:param list distances: distances between points (i, j)
:param bool return_indices: return list of indices otherwise return nodes
:param bool return_first: return just the first 2opt move
:param bool return_first: return just the first 2opt move
"""
improvement = True
tour = [x for x in range(len(positions))]
best_tour = tour
distances = calculate_distances(positions)
distances = np.rint(distances*10000)
distances = distances.astype(int)
best_distance = route_distance(tour, distances)
# tours: list with tours
tours = []
# swap_indices: list with indices to swap
swap_indices = []
# print("initial distance", best_distance)
while improvement:
improvement = False
for i in range(0, len(best_tour) - 1):
for k in range(i+1, len(best_tour)):
new_tour = swap_2opt(best_tour, i, k)
new_distance = route_distance(new_tour, distances)
if new_distance < best_distance:
swap_indices.append([i, k])
tours.append(best_tour)
best_distance = new_distance
best_tour = new_tour
improvement = True
break
if improvement:
break
assert len(best_tour) == len(tour)
swap_indices = np.array(swap_indices)
best_tour = np.array(best_tour)
tours = np.array(tours)
return best_distance/10000
def heuristic_2opt_bi(positions):
"""
Improves an existing route using 2-opt until no improvement is found
:param list tour: TSP tour
:param list distances: distances between points (i, j)
:param bool return_indices: return list of indices otherwise return nodes
:param bool return_first: return just the first 2opt move
:param bool return_first: return just the first 2opt move
"""
improvement = True
tour = [x for x in range(len(positions))]
best_tour = tour
distances = calculate_distances(positions)
distances = np.rint(distances*10000)
distances = distances.astype(int)
best_distance = route_distance(tour, distances)
# tours: list with tours
tours = []
# swap_indices: list with indices to swap
swap_indices = []
# print("initial distance", best_distance)
while improvement:
improvement = False
for i in range(0, len(best_tour) - 1):
for k in range(i+1, len(best_tour)):
new_tour = swap_2opt(tour, i, k)
new_distance = route_distance(new_tour, distances)
# print("i,j", i,k)
if new_distance < best_distance:
swap_indices.append([i, k])
tours.append(best_tour)
best_distance = new_distance
best_tour = new_tour
improvement = True
tour = best_tour
assert len(best_tour) == len(tour)
swap_indices = np.array(swap_indices)
best_tour = np.array(best_tour)
tours = np.array(tours)
# return best_tour, best_distance/10000
return best_distance/10000
def heuristic_2opt_fi_restart(positions, steps):
"""
Improves an existing route using 2-opt until no improvement is found
:param list tour: TSP tour
:param list distances: distances between points (i, j)
:param bool return_indices: return list of indices otherwise return nodes
:param bool return_first: return just the first 2opt move
:param bool return_first: return just the first 2opt move
"""
improvement = True
tour = [x for x in range(len(positions))]
best_tour = tour
distances = calculate_distances(positions)
distances = np.rint(distances*10000)
distances = distances.astype(int)
best_distance = route_distance(tour, distances)
restart_distance = best_distance
# print("initial distance", best_distance)
for n in range(steps):
improvement = False
for i in range(0, len(best_tour) - 1):
for k in range(i+1, len(best_tour)):
new_tour = swap_2opt(tour, i, k)
new_distance = route_distance(new_tour, distances)
if new_distance < best_distance:
best_distance = new_distance
best_tour = new_tour
improvement = True
tour = new_tour
break
if improvement:
break
if improvement is False:
if best_distance < restart_distance:
restart_distance = best_distance
restart_tour = best_tour
tour = create_tour(len(tour))
best_distance = 1e10
if n == steps-1:
if best_distance < restart_distance:
restart_distance = best_distance
restart_tour = best_tour
assert len(best_tour) == len(tour)
return restart_distance/10000
# return_dict[procnum] = restart_tour, restart_distance/10000
def heuristic_2opt_bi_restart(positions, steps):
"""
Improves an existing route using 2-opt until no improvement is found
:param list tour: TSP tour
:param list distances: distances between points (i, j)
:param bool return_indices: return list of indices otherwise return nodes
:param bool return_first: return just the first 2opt move
:param bool return_first: return just the first 2opt move
"""
improvement = True
tour = [x for x in range(len(positions))]
best_tour = tour
distances = calculate_distances(positions)
distances = np.rint(distances*10000)
distances = distances.astype(int)
best_distance = route_distance(tour, distances)
restart_distance = best_distance
# tours: list with tours
tours = []
# swap_indices: list with indices to swap
swap_indices = []
# print("initial distance", best_distance)
for n in range(steps):
improvement = False
for i in range(0, len(best_tour) - 1):
for k in range(i+1, len(best_tour)):
new_tour = swap_2opt(tour, i, k)
new_distance = route_distance(new_tour, distances)
if new_distance < best_distance:
swap_indices.append([i, k])
tours.append(best_tour)
best_distance = new_distance
best_tour = new_tour
improvement = True
tour = best_tour
if improvement is False:
if best_distance < restart_distance:
restart_distance = best_distance
restart_tour = best_tour
tour = create_tour(len(tour))
best_distance = 1e10
if n == steps-1:
if best_distance < restart_distance:
restart_distance = best_distance
restart_tour = best_tour
assert len(best_tour) == len(tour)
swap_indices = np.array(swap_indices)
best_tour = np.array(best_tour)
tours = np.array(tours)
return restart_distance/10000
# return_dict[procnum] = restart_tour, restart_distance/10000