qap = Reader(path) # construct the problem
Util.get_configuration() # get the best configuration of the ant system
runs = [sys.maxsize for i in range(Params.singleton.MAX_RUN)]
for t in range(Params.singleton.MAX_TRY):
colony = Ants(qap.matrix_A, qap.matrix_B, Params.singleton.nb_ants,
qap.size)
colony.init_pheromone(1 / (Params.singleton.RHO * qap.size))
run = 0
start = time.time()
while not terminal_condition(run, start):
construct_solutions(colony) # build the solution for each ant
if Params.singleton.lcs_flag:
colony.local_search() # apply the local search
update_pheromone(
colony) # update the pheromone depending on the ant system
save_run(run, colony) # Save the current run
exit_run(run, colony) # Print the information about the run
run += 1
save_best_run() # Save the the run that have the best results
exit_trial(t) # Print the statistic about the trial
runs = [sys.maxsize for i in range(Params.singleton.MAX_RUN)]
get_best_solution_on_instance() # Retrieve the best try of the instance
tries.clear()
