def main():
filename, algorithm, cut_off_sec, random_seed = format_check(
) # check input format
city, dim, edge_weight_type, coord = parse_input(
filename) # parse input information
adj_mat = adjacency_mat(dim, edge_weight_type, coord) # input matrix
write_adj_mat_file(adj_mat, city, dim) # save input matrix as csv
if random_seed == None:
random_seed = 0
else:
random_seed = int(random_seed)
if algorithm == 'Approx':
output = Output(filename, algorithm, cut_off_sec,
algorithm) # init output object
start_MST = time.time()
c, tour = compute(dim, adj_mat, cut_off_sec=cut_off_sec)
total_time = time.time() - start_MST
output.solution([c] + tour) # generate solution file
output.sol_trace([('%.4f' % total_time, c)
]) # generate solution trace file
elif algorithm == 'BnB':
output = Output(filename, algorithm, cut_off_sec,
algorithm) # init output object
bnb = BranchNBound(
adj_mat, dim,
cut_off_sec) # param: dist_matrix, num_city, time_limit
path, cost, trace_list = bnb.run_branch_and_bound()
output.solution([cost] + path) # generate solution file
output.sol_trace(trace_list) # generate solution trace file
elif algorithm == 'LS1': # Iterated LocalSearch
output = Output(filename, algorithm, cut_off_sec, algorithm,
random_seed) # init output object
ils = ILS(adj_mat, dim, cut_off_sec, random_seed
) # param: dist_matrix, num_city, time_limit, random_seed
path, cost, trace_list = ils.iterated_local_search()
output.solution([cost] + path) # generate solution file
output.sol_trace(trace_list) # generate solution trace file
elif algorithm == 'LS2': # Simulated Annealing
output = Output(filename, algorithm, cut_off_sec, algorithm,
random_seed) # init output object
sa = SA(adj_mat, dim, 1e30, 1, 0.999, random_seed, cut_off_sec)
path, cost, trace_list = sa.run_simulated_annealing()
output.solution([cost] + path) # generate solution file
output.sol_trace(trace_list) # generate solution trace file
def test_io():
filename = "DATA/Cincinnati.tsp"
city, dim, edge_weight_type, coord = parse_input(filename)
adj_mat = adjacency_mat(dim, edge_weight_type, coord)
algorithm = "TwoOpt"
cut_off_sec = 10
output = Output(filename, algorithm, cut_off_sec)
to = TwoOpt(adj_mat, dim, cut_off_sec)
path, cost, quality = to.two_opt()
output.solution([cost] + path)
output.sol_trace([(quality, cost)])
def test_ils():
# filename = "DATA/Cincinnati.tsp"
filename = "DATA/UKansasState.tsp"
city, dim, edge_weight_type, coord = parse_input(filename)
adj_mat = adjacency_mat(dim, edge_weight_type, coord)
algorithm = "IteratedLocalSearch"
cut_off_sec = 600
output = Output(filename, algorithm, cut_off_sec)
ils = IteratedLocalSearch(adj_mat, dim, cut_off_sec, 20)
path, cost, quality, _ = ils.iterated_local_search()
output.solution([cost] + path)
output.sol_trace([(quality, cost)])
def run_all_data():
dir = "DATA/"
for f in os.listdir(dir):
if f[-3:] == "tsp":
print dir + f
filename = dir + f
city, dim, edge_weight_type, coord = parse_input(filename)
adj_mat = adjacency_mat(dim, edge_weight_type, coord)
algorithm = "IteratedLocalSearch"
cut_off_sec = 600
random_seed = 0
output = Output(filename, algorithm, cut_off_sec)
ils = IteratedLocalSearch(adj_mat, dim, cut_off_sec, random_seed)
path, cost, trace_list = ils.iterated_local_search()
print path, cost, trace_list
output.solution([cost] + path)
output.sol_trace(trace_list)
# print(cost4)
# cost_matrix = np.array([[0, 10, 15, 20], [10, 0, 35, 25], [15, 35, 0, 30], [20, 25, 30, 0]])
# bnb5 = BranchNBound(cost_matrix, 4, 5)
# path5, cost5, quality5 = bnb5.run_branch_and_bound()
# print_path(path5)
# print(cost5)
# file_name = "../DATA/Cincinnati.tsp"
# city, dim, edge_weight_type, coord = parse_input(file_name)
# adj_mat = adjacency_mat(dim, edge_weight_type, coord)
# bnb_Atlanta = BranchNBound(adj_mat, dim, 600)
# path_atl, cost_atl, quality_atl = bnb_Atlanta.run_branch_and_bound()
filename, algorithm, cut_off_sec, random_seed = format_check()
city, dim, edge_weight_type, coord = parse_input(filename)
adj_mat = adjacency_mat(dim, edge_weight_type, coord)
output = Output(filename, algorithm, cut_off_sec)
bnb = BranchNBound(adj_mat, dim, cut_off_sec)
path, cost, quality = bnb.run_branch_and_bound()
# print_path(path)
# print('The cost is ', cost)
# print('The quality is ', quality)
output.solution([cost] + path)
output.sol_trace([(quality, cost)])