def hv_pop():
# ref_point = [2, 2]
# points = [[1, 2], [0.5, 3], [0.1, 3.1]]
udp = pg.dtlz(prob_id=1, dim=5, fdim=3)
pop = pg.population(prob=udp, size=2)
# hv = pg.hypervolume(pop=pop)
udp.plot(pop)
print(pop)
async def evaluate(X, configs={'prob_id': 1, 'D': 30, 'O': 2, 'wall_time': 1}):
assert type(X) == np.ndarray, 'Input X must be a numpy array'
prob_id, D, O, wall_time = itemgetter('prob_id', 'D', 'O',
'wall_time')(configs)
prob = pg.problem(pg.dtlz(prob_id=prob_id, dim=D, fdim=O))
lower_bound, upper_bound = prob.get_bounds()
# denormalize the parameters
X = lower_bound + (upper_bound - lower_bound) * X
Y = []
start_time = time.time()
await asyncio.sleep(wall_time)
for x in X:
y = prob.fitness(x)
Y.append(y)
end_time = time.time()
Y = np.array(Y)
print(f'time cost: {(end_time - start_time) * 1e3:.3f} ms')
return Y
def draw_dtlz(prob_id, dim=5, fdim=3, alph=0.5, individual_size=20):
udp = pg.dtlz(prob_id=prob_id, dim=dim, fdim=fdim)
pop = pg.population(udp, individual_size)
udp.plot(pop) # doctest: +SKIP
pass
def dtlz_p1():
udp = pg.dtlz(prob_id=1, dim=5, fdim=3)
pop = pg.population(udp, 40)
hv = pg.hypervolume(pop)
udp.plot(pop) # doctest: +SKIP
def dtlz_p6():
udp = pg.dtlz(prob_id=6, fdim=3, dim=5)
pop = pg.population(udp, 40)
udp.plot(pop) # doctest: +SKIP
