def _do(self, pop, n_survive, D=None, **kwargs):
# attributes to be set after the survival
F = pop.get("F")
# find or usually update the new ideal point - from feasible solutions
self.ideal_point = np.min(np.vstack((self.ideal_point, F)), axis=0)
self.worst_point = np.max(np.vstack((self.worst_point, F)), axis=0)
# calculate the fronts of the population
fronts, rank = NonDominatedSorting().do(F,
return_rank=True,
n_stop_if_ranked=n_survive)
self.nadir_point = get_nadir_point_from_fronts(F, fronts,
self.ideal_point)
# consider only the population until we come to the splitting front
I = np.concatenate(fronts)
pop, rank, F = pop[I], rank[I], F[I]
# update the front indices for the current population
counter = 0
for i in range(len(fronts)):
for j in range(len(fronts[i])):
fronts[i][j] = counter
counter += 1
last_front = fronts[-1]
# associate individuals to niches
niche_of_individuals, dist_to_niche = associate_to_niches(
F, self.ref_dirs, self.ideal_point, self.nadir_point)
pop.set('rank', rank, 'niche', niche_of_individuals, 'dist_to_niche',
dist_to_niche)
# if we need to select individuals to survive
if len(pop) > n_survive:
# if there is only one front
if len(fronts) == 1:
n_remaining = n_survive
until_last_front = np.array([], dtype=np.int)
niche_count = np.zeros(len(self.ref_dirs), dtype=np.int)
# if some individuals already survived
else:
until_last_front = np.concatenate(fronts[:-1])
niche_count = calc_niche_count(
len(self.ref_dirs), niche_of_individuals[until_last_front])
n_remaining = n_survive - len(until_last_front)
S = niching(F[last_front, :], n_remaining, niche_count,
niche_of_individuals[last_front],
dist_to_niche[last_front])
survivors = np.concatenate(
(until_last_front, last_front[S].tolist()))
pop = pop[survivors]
return pop
def _do(self, problem, pop, n_survive, D=None, **kwargs):
# attributes to be set after the survival
F = pop.get("F")
# find or usually update the new ideal point - from feasible solutions
self.ideal_point = np.min(np.vstack(
(self.ideal_point, F, self.ref_points)),
axis=0)
self.worst_point = np.max(np.vstack(
(self.worst_point, F, self.ref_points)),
axis=0)
# calculate the fronts of the population
fronts, rank = NonDominatedSorting().do(F,
return_rank=True,
n_stop_if_ranked=n_survive)
non_dominated, last_front = fronts[0], fronts[-1]
# find the extreme points for normalization
self.extreme_points = get_extreme_points_c(
np.vstack([F[non_dominated], self.ref_points]),
self.ideal_point,
extreme_points=self.extreme_points)
# find the intercepts for normalization and do backup if gaussian elimination fails
worst_of_population = np.max(F, axis=0)
worst_of_front = np.max(F[non_dominated, :], axis=0)
self.nadir_point = get_nadir_point(self.extreme_points,
self.ideal_point, self.worst_point,
worst_of_population, worst_of_front)
# consider only the population until we come to the splitting front
I = np.concatenate(fronts)
pop, rank, F = pop[I], rank[I], F[I]
# update the front indices for the current population
counter = 0
for i in range(len(fronts)):
for j in range(len(fronts[i])):
fronts[i][j] = counter
counter += 1
last_front = fronts[-1]
unit_ref_points = (self.ref_points - self.ideal_point) / (
self.nadir_point - self.ideal_point)
ref_dirs = get_ref_dirs_from_points(unit_ref_points,
self.aspiration_ref_dirs,
mu=self.mu)
self.ref_dirs = denormalize(ref_dirs, self.ideal_point,
self.nadir_point)
# associate individuals to niches
niche_of_individuals, dist_to_niche, dist_matrix = associate_to_niches(
F, ref_dirs, self.ideal_point, self.nadir_point)
pop.set('rank', rank, 'niche', niche_of_individuals, 'dist_to_niche',
dist_to_niche)
# set the optimum, first front and closest to all reference directions
closest = np.unique(
dist_matrix[:, np.unique(niche_of_individuals)].argmin(axis=0))
self.opt = pop[intersect(fronts[0], closest)]
# if we need to select individuals to survive
if len(pop) > n_survive:
# if there is only one front
if len(fronts) == 1:
n_remaining = n_survive
until_last_front = np.array([], dtype=int)
niche_count = np.zeros(len(ref_dirs), dtype=int)
# if some individuals already survived
else:
until_last_front = np.concatenate(fronts[:-1])
niche_count = calc_niche_count(
len(ref_dirs), niche_of_individuals[until_last_front])
n_remaining = n_survive - len(until_last_front)
S = niching(pop[last_front], n_remaining, niche_count,
niche_of_individuals[last_front],
dist_to_niche[last_front])
survivors = np.concatenate(
(until_last_front, last_front[S].tolist()))
pop = pop[survivors]
return pop
def _do(self, pop, n_survive, D=None, **kwargs):
# attributes to be set after the survival
X, F = pop.get("X", "F")
# find or usually update the new ideal point - from feasible solutions
self.ideal_point = np.min(np.vstack((self.ideal_point, F)), axis=0)
self.worst_point = np.max(np.vstack((self.worst_point, F)), axis=0)
# calculate the fronts of the population
fronts, rank = NonDominatedSorting().do(F,
return_rank=True,
n_stop_if_ranked=n_survive)
non_dominated, last_front = fronts[0], fronts[-1]
# find the extreme points for normalization
self.extreme_points = get_extreme_points_c(
F[non_dominated, :],
self.ideal_point,
extreme_points=self.extreme_points)
# find the intercepts for normalization and do backup if gaussian elimination fails
worst_of_population = np.max(F, axis=0)
worst_of_front = np.max(F[non_dominated, :], axis=0)
self.nadir_point = get_nadir_point(self.extreme_points,
self.ideal_point, self.worst_point,
worst_of_population, worst_of_front)
# consider only the population until we come to the splitting front
I = np.concatenate(fronts)
pop, rank, X, F = pop[I], rank[I], X[I], F[I]
# update the front indices for the current population
counter = 0
for i in range(len(fronts)):
for j in range(len(fronts[i])):
fronts[i][j] = counter
counter += 1
last_front = fronts[-1]
# associate individuals to niches
niche_of_individuals, dist_to_niche = associate_to_niches(
F, self.ref_dirs, self.ideal_point, self.nadir_point)
kktpm, _ = KKTPM(var_bounds_as_constraints=False).calc(X, problem)
kktpm = kktpm[:, 0]
pop.set('rank', rank, 'niche', niche_of_individuals, 'dist_to_niche',
dist_to_niche, 'kktpm', kktpm)
# if we need to select individuals to survive
if len(pop) > n_survive:
# if there is only one front
if len(fronts) == 1:
n_remaining = n_survive
until_last_front = np.array([], dtype=np.int)
niche_count = np.zeros(len(self.ref_dirs), dtype=np.int)
# if some individuals already survived
else:
until_last_front = np.concatenate(fronts[:-1])
niche_count = calc_niche_count(
len(self.ref_dirs), niche_of_individuals[until_last_front])
n_remaining = n_survive - len(until_last_front)
S = niching(F[last_front, :], n_remaining, niche_count,
niche_of_individuals[last_front], kktpm[last_front])
survivors = np.concatenate(
(until_last_front, last_front[S].tolist()))
pop = pop[survivors]
return pop
