def main(args):
# Parameters
initial_seed = 123456 # Used to generate the set of seeds for repetitions
n_repetitions = 30
n_iterations = args.iterations
initial_pheromone = 0.5
t_min = 0.001 # Min pheromone level
t_max = 0.999 # Max pheromone level
rho = args.rho # Pheromone decay rate
alpha = args.alpha
beta = args.beta
# Initializations
random_seeds = utils.generate_seeds(initial_seed, n_repetitions)
n, p, nodes = utils.read_data(args.dataset)
world = World(n, p, nodes)
n_ants = (n - p) if args.ants is None else args.ants
colony = Colony(n_ants)
ni = aco.information_heuristic(world) # Information Heuristic
dataset_name = args.dataset.split('/')[-1].split('.')[0]
output = np.zeros((n_repetitions, n_iterations, 3))
output_dir = "../results/{}it{}rho{}alpha{}beta{}ants{}/".format(
dataset_name, n_iterations, rho, alpha, beta, n_ants)
# Main loop
for repetition in range(n_repetitions):
np.random.seed(random_seeds[repetition])
# Reset things for new repetition
g_best = Solution(distance=np.inf)
world.reset_pheromones(initial_pheromone)
print("Repetition {}\n".format(repetition))
for iteration in tqdm(range(n_iterations)):
for ant in colony.ants:
ant.build_solution(world, ni, alpha, beta)
l_best, l_worst = aco.evaluate_solutions(world, colony)
world.update_pheromones(rho, g_best, l_best, l_worst)
# Check algorithm stagnation
if aco.is_stagnated(world, t_min, t_max):
world.reset_pheromones(initial_pheromone)
# Update global solution
if l_best.distance < g_best.distance:
g_best = l_best
# Reset for next iteration
colony.reset_solutions()
# Store output data
output[repetition][iteration][0] = g_best.distance
output[repetition][iteration][1] = l_best.distance
output[repetition][iteration][2] = l_worst.distance
print("\nBest solution\n"
"-------------\n"
"Distance: {}\n"
"Medians: {}\n".format(g_best.distance, g_best.medians))
utils.write_data(output_dir, output)
