def msls(points, dist_matrix):
result_dict = {}
for i in range(100):
groups = random_heuristic(points, dist_matrix)
groups = local_search_steep(groups, dist_matrix)
result_dict[sum_all_groups_fully_connected(groups,
dist_matrix)] = groups
return result_dict[min(result_dict)]
def ils_big(max_time, points, dist_matrix):
timepoint = time.time()
groups = random_heuristic(points, dist_matrix)
previous_groups = local_search_steep(groups, dist_matrix)
destroy_number = random.randint(int(len(points) * 0.1),
int(len(points) * 0.3))
while get_time(timepoint) < max_time:
new_groups, deleted = destroy(copy.deepcopy(previous_groups),
destroy_number)
new_groups = repair(new_groups, deleted, dist_matrix)
new_groups = local_search_steep(new_groups, dist_matrix)
if (sum_all_groups_fully_connected(new_groups, dist_matrix) <
sum_all_groups_fully_connected(previous_groups, dist_matrix)):
previous_groups = new_groups
return previous_groups
def ils_small(max_time, points, dist_matrix):
timepoint = time.time()
groups = random_heuristic(points, dist_matrix)
previous_groups = local_search_steep(groups, dist_matrix)
swap_number = random.randint(2, 10)
while get_time(timepoint) < max_time:
# print(get_time(timepoint))
new_groups = swap_points(swap_number, copy.deepcopy(previous_groups))
new_groups = local_search_steep(new_groups, dist_matrix)
if (sum_all_groups_fully_connected(new_groups, dist_matrix) <
sum_all_groups_fully_connected(previous_groups, dist_matrix)):
if swap_number > 1:
swap_number -= 1
previous_groups = new_groups
else:
if swap_number < int(len(points) * 0.1):
swap_number += 1
# print(swap_number)
return previous_groups
def get_n_msls_solutions(points, dist_matrix, n):
results = []
for i in range(n):
if i % 20 == 0:
print(i)
groups = random_heuristic(points, dist_matrix)
groups = local_search_steep(groups, dist_matrix)
results.append({
'groups':
groups,
'result':
sum_all_groups_fully_connected(groups, dist_matrix),
'best_sim':
0,
'ave_sim':
0
})
return results
def get_solution(points, dist_matrix):
groups = random_heuristic(points, dist_matrix)
solution = local_search_steep(groups, dist_matrix)
# print(1)
return solution