def optimize(x0, syn_func, perturb_type, perturb, n_eval, run_id):
# Optimize using GA
n_best = 30
n_random = 10
n_children = 5
chance_of_mutation = 0.1
population_size = int((n_best + n_random) / 2 * n_children)
population = generate_first_population(x0, population_size, perturb_type,
perturb)
best_inds = []
best_perfs = []
opt_perfs = [0]
i = 0
while 1:
breeders, best_perf, best_individual = select(population, n_best,
n_random, syn_func)
best_inds.append(best_individual)
best_perfs.append(best_perf)
opt_perfs += [np.max(best_perfs)
] * population_size # Best performance so far
print('PARSEC-GA %d-%d: fittest %.2f' % (run_id, i + 1, best_perf))
# No need to create next generation for the last generation
if i < n_eval / population_size - 1:
next_generation = create_children(breeders, n_children)
population = mutate_population(next_generation, chance_of_mutation,
perturb_type, perturb)
i += 1
else:
break
opt_x = best_inds[np.argmax(best_perfs)]
opt_airfoil = synthesize(opt_x, n_points)
print('Optimal CL/CD: {}'.format(opt_perfs[-1]))
return opt_airfoil, opt_perfs
def optimize(bounds, n_eval, run_id):
# Optimize using BO
n_pre_samples = 10
kernel = gp.kernels.Matern()
gp_model = gp.GaussianProcessRegressor(kernel=kernel,
alpha=1e-4,
n_restarts_optimizer=100,
normalize_y=False)
xs = []
perfs = []
opt_perfs = [0]
for i in range(n_eval):
if len(xs) < n_pre_samples:
x = np.random.uniform(bounds[:, 0], bounds[:, 1], size=d)
else:
perfs_normalized = normalize(perfs)
gp_model.fit(np.array(xs), np.array(perfs_normalized))
print('Length scale = {}'.format(gp_model.kernel_.length_scale))
previous_optimum = np.max(perfs_normalized)
# x = sample_next_point(d, neg_expected_improvement, gp_model, previous_optimum, bounds, n_restarts=100)
x = sample_next_point(d,
neg_expected_improvement,
gp_model,
previous_optimum,
bounds,
random_search=100000)
airfoil = synthesize(x, n_points)
perf = evaluate(airfoil)
print('PARSEC-BO %d-%d: CL/CD %.2f' % (run_id, i + 1, perf))
xs.append(x)
perfs.append(perf)
opt_perfs.append(np.max(perfs)) # Best performance so far
opt_x = xs[np.argmax(perfs)]
opt_airfoil = synthesize(opt_x, n_points)
print('Optimal CL/CD: {}'.format(opt_perfs[-1]))
return opt_airfoil, opt_perfs
args = parser.parse_args()
n_runs = args.n_runs
n_eval = args.n_eval
# Airfoil parameters
n_points = 192
# NACA 0012 as the original airfoil
x0 = np.array([
0.0147, 0.2996, -0.06, 0.4406, 7.335, 0.3015, 0.0599, -0.4360, -7.335
]) # NACA 0012
perturb_type = 'relative'
perturb = 0.2
syn_func = lambda x: synthesize(x, n_points)
opt_airfoil_runs = []
opt_perfs_runs = []
time_runs = []
for i in range(n_runs):
start_time = time.time()
opt_airfoil, opt_perfs = optimize(x0, syn_func, perturb_type, perturb,
n_eval, i + 1)
end_time = time.time()
opt_airfoil_runs.append(opt_airfoil)
opt_perfs_runs.append(opt_perfs)
time_runs.append(end_time - start_time)
opt_airfoil_runs = np.array(opt_airfoil_runs)
opt_perfs_runs = np.array(opt_perfs_runs)