ACOr.set_cost(
optimization_utilities.define_cost(
L, H, Q, Fs, "./signals_and_systems/infinite_order_train.csv"))
ACOr.set_parameters(m, k, q, xi, function_evals)
ACOr.define_variables(initial_ranges, is_bounded)
best_solution = ACOr.optimize()
print(best_solution)
# SA
# Total # of function evaluations: global_iter * local_iter + 1
print("SA")
SA_local_iterations = 50
T0 = 100.0
decay_constant = 0.99
step_size = 1e-2
SA = simulated_annealing.SA()
SA.set_verbosity(False)
SA.set_cost(
optimization_utilities.define_cost(
L, H, Q, Fs, "./signals_and_systems/infinite_order_train.csv"))
SA.set_parameters(T0, decay_constant, step_size, SA_local_iterations,
function_evals)
SA.define_variables(initial_ranges, is_bounded)
best_solution = SA.optimize()
print(best_solution)
# PSO
# Total # of function evaluations: population_size * num_iterations
print("PSO")
swarm_size = 20
accel_p = 2
MAX_TEMP = 10000
NUMBER_OF_DISTRICTS = 16
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
data = None
if rank == 0:
data = [None for i in range(size)]
OUTPUT_FILE_24_24 = (
"./experiments_output/region_24_24/output_{0}_sa_{1}_run_{2}_rank{3}.csv".
format(REGION_ID, SA_VERSION, TEST_ID_NUMBER, rank))
sa = simulated_annealing.SA(REGION_DATA_FILE_PATH_24_24, OUTPUT_FILE_24_24,
MAX_ITERATIONS, MAX_TEMP, NUMBER_OF_DISTRICTS)
best_state, best_energy = sa.run_instance(rank)
data = comm.gather((rank, best_energy), root=0)
if rank == 0:
end = time.time()
data.sort(key=lambda x: x[1], reverse=False)
print(data)
running_time = end - start
print("TEST_ID_NUMBER {0}; REGION_ID {1}".format(TEST_ID_NUMBER,
REGION_ID))
print(
"start time {0} seconds; end time {1} seconds; running time {2} seconds"
.format(start, end, running_time))
# Parameters used for BAACOr
k = 50
m = 10
q_min = 1e-2; q_max = 1.0
xi_min = 0.1; xi_max = 0.93
# Configure
metaheuristic = ant_colony_for_continuous_domains.BAACOr()
metaheuristic.set_verbosity(False)
metaheuristic.set_parameters(m, k, q_min, q_max, xi_min, xi_max, 'exp', 'sig', function_evals)
elif metaheuristic_name == 'sa':
print('SA')
# Parameters to be used for SA
initial_temperature = 10.0; cooling_constant = 0.99; step_size = 1e-2;
local_iterations = 100
metaheuristic = simulated_annealing.SA()
metaheuristic.set_parameters(initial_temperature, cooling_constant, step_size, local_iterations, function_evals)
elif metaheuristic_name == 'acfsa':
print('ACFSA')
# Parameters to be used for ACFSA
local_iterations = 100
initial_temperature = 10
cooling_constant = 0.99
# Configure
metaheuristic = simulated_annealing.ACFSA()
metaheuristic.set_verbosity(False)
metaheuristic.set_parameters(initial_temperature, cooling_constant, local_iterations, function_evals)
elif metaheuristic_name == 'pso':
