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
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)
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, 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
# normalize the whole population by the estimations made
N = normalize(F, self.ideal_point, self.nadir_point)
# the final indices of surviving individuals
survivors = []
for k, front in enumerate(fronts):
# calculate the crowding distance of the front
crowding_of_front = calc_asf_crowding_distance(N[front, :])
# save rank and crowding in the individual class
for j, i in enumerate(front):
pop[i].set("rank", k)
pop[i].set("crowding", crowding_of_front[j])
# current front sorted by crowding distance if splitting
if len(survivors) + len(front) > n_survive:
I = randomized_argsort(crowding_of_front,
order='descending',
method='numpy')
I = I[:(n_survive - len(survivors))]
# otherwise take the whole front unsorted
else:
I = np.arange(len(front))
# extend the survivors by all or selected individuals
survivors.extend(front[I])
return pop[survivors]
def _do(self, pop, n_survive, algorithm=None, **kwargs):
if True:
if self.archive is None:
self.archive = pop
else:
self.archive = self.archive.merge(pop)
# get the function values of the current archive for operations
F = self.archive.get("F")
# filter out all the duplicate solutions
I = np.logical_not(default_is_duplicate(F))
self.archive, F = self.archive[I], F[I]
# get only the non-dominated solutions
I = NonDominatedSorting().do(F, only_non_dominated_front=True)
self.archive, F = self.archive[I], F[I]
if len(self.archive) > self.n_archive:
cd = calc_crowding_distance(F)
self.archive = self.archive[np.argsort(cd)[::-1]
[:self.n_archive]]
# attributes to be set after the survival
pop.merge(self.archive)
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(epsilon=1e-10).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 = non_dominated[get_extreme_points(
F[non_dominated, :], self.ideal_point)]
# 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(F[self.extreme_points],
self.ideal_point, self.worst_point,
worst_of_population, worst_of_front)
# associate individuals to niches
pop.set('rank', rank)
# if we need to select individuals to survive
if len(pop) > n_survive:
for i in range(len(fronts)):
fronts[i] = np.array(
[j for j in fronts[i] if j not in self.extreme_points])
survivors = np.unique(self.extreme_points).tolist()
for k, front in enumerate(fronts):
# current front sorted by crowding distance if splitting
if len(survivors) + len(front) > n_survive:
I = selection(survivors, list(front), F,
(n_survive - len(survivors)))
survivors.extend(I)
# otherwise take the whole front unsorted
else:
# extend the survivors by all or selected individuals
survivors.extend(front)
pop = pop[survivors]
return pop