def update_PCpop(pc_pop, off):
pc_objs = pc_pop.get("F")
off_objs = off.get("F")
n = pc_objs.shape[0]
del_ind = []
for i in range(n):
flag = Dominator.get_relation(off_objs[0, :], pc_objs[i, :])
if flag == 1:
# off dominates pc_pop[i]
del_ind.append(i)
break
elif flag == -1:
# pc_pop[i] dominates off
return pc_pop
else:
# flag == 0
# off and pc_pop[i] are nondominated
break
if len(del_ind) > 0:
pc_index = np.arange(n)
# Delete element at index positions given by the list 'del_ind'
pc_index = np.delete(pc_index, del_ind)
pc_pop = pc_pop[pc_index.tolist()]
pc_pop = Population.merge(pc_pop, off)
return pc_pop
def sequential_search(F, i, fronts) -> int:
"""
Find the front rank for the i-th individual through sequential search
Parameters
----------
F: np.ndarray
the objective values
i: int
the index of the individual
fronts: list
individuals in each front
"""
num_found_fronts = len(fronts)
k = 0 # the front now checked
current = F[i]
while True:
if num_found_fronts == 0:
return 0
# solutions in the k-th front, examine in reverse order
fk_indices = fronts[k]
solutions = F[fk_indices[::-1]]
non_dominated = True
for f in solutions:
relation = Dominator.get_relation(current, f)
if relation == -1:
non_dominated = False
break
if non_dominated:
return k
else:
k += 1
if k >= num_found_fronts:
# move the individual to a new front
return num_found_fronts
def binary_tournament(pop, P, algorithm):
if P.shape[1] != 2:
raise ValueError("Only implemented for binary tournament!")
tournament_type = algorithm.tournament_type
S = np.full(P.shape[0], np.nan)
for i in range(P.shape[0]):
a, b = P[i, 0], P[i, 1]
if tournament_type == 'comp_by_dom_and_crowding':
rel = Dominator.get_relation(pop[a].F, pop[b].F)
if rel == 1:
S[i] = a
elif rel == -1:
S[i] = b
elif tournament_type == 'comp_by_rank_and_crowding':
S[i] = compare(a,
pop[a].rank,
b,
pop[b].rank,
method='smaller_is_better')
else:
raise Exception("Unknown tournament type.")
if np.isnan(S[i]):
S[i] = compare(a,
pop[a].get("crowding"),
b,
pop[b].get("crowding"),
method='larger_is_better',
return_random_if_equal=True)
return S[:, None].astype(np.int)
def comp_by_dom_and_crowding(pop, P, crowding, **kwargs):
if P.shape[1] != 2:
raise ValueError("Only implemented for binary tournament!")
S = np.zeros((P.shape[0], 1), dtype=np.int)
for i, p in enumerate(P):
rel = Dominator.get_relation(pop.F[P[i, 0], :], pop.F[P[i, 1], :])
# first by domination
if rel == 1:
S[i, 0] = P[i, 0]
elif rel == -1:
S[i, 0] = P[i, 1]
# then by crowding
else:
if crowding[P[i, 0]] > crowding[P[i, 1]]:
S[i, 0] = P[i, 0]
elif crowding[P[i, 1]] > crowding[P[i, 0]]:
S[i, 0] = P[i, 1]
else:
S[i, 0] = P[i, random.randint(0, 2)]
return S
def binary_tournament(pop, P, algorithm, **kwargs):
n_tournaments, n_parents = P.shape
if n_parents != 2:
raise ValueError("Only implemented for binary tournament!")
tournament_type = algorithm.tournament_type
S = np.full(n_tournaments, np.nan)
for i in range(n_tournaments):
a, b = P[i, 0], P[i, 1]
a_cv, a_f, b_cv, b_f, = pop[a].CV[0], pop[a].F, pop[b].CV[0], pop[b].F
rank_a, cd_a = pop[a].get("rank", "crowding")
rank_b, cd_b = pop[b].get("rank", "crowding")
# if at least one solution is infeasible
if a_cv > 0.0 or b_cv > 0.0:
S[i] = compare(a,
a_cv,
b,
b_cv,
method='smaller_is_better',
return_random_if_equal=True)
# both solutions are feasible
else:
if tournament_type == 'comp_by_dom_and_crowding':
rel = Dominator.get_relation(a_f, b_f)
if rel == 1:
S[i] = a
elif rel == -1:
S[i] = b
elif tournament_type == 'comp_by_rank_and_crowding':
S[i] = compare(a,
rank_a,
b,
rank_b,
method='smaller_is_better')
else:
raise Exception("Unknown tournament type.")
# if rank or domination relation didn't make a decision compare by crowding
if np.isnan(S[i]):
S[i] = compare(a,
cd_a,
b,
cd_b,
method='larger_is_better',
return_random_if_equal=True)
return S[:, None].astype(int, copy=False)
def binary_tournament(pop, P, algorithm, **kwargs):
if P.shape[1] != 2:
raise ValueError("Only implemented for binary tournament!")
tournament_type = algorithm.tournament_type
S = np.full(P.shape[0], np.nan)
for i in range(P.shape[0]):
a, b = P[i, 0], P[i, 1]
# if at least one solution is infeasible
if pop[a].CV > 0.0 or pop[b].CV > 0.0:
S[i] = compare(
a,
pop[a].CV,
b,
pop[b].CV,
method="smaller_is_better",
return_random_if_equal=True,
)
# both solutions are feasible
else:
if tournament_type == "comp_by_dom_and_crowding":
rel = Dominator.get_relation(pop[a].F, pop[b].F)
if rel == 1:
S[i] = a
elif rel == -1:
S[i] = b
elif tournament_type == "comp_by_rank_and_crowding":
S[i] = compare(
a, pop[a].rank, b, pop[b].rank, method="smaller_is_better"
)
else:
raise Exception("Unknown tournament type.")
# if rank or domination relation didn't make a decision compare by crowding
if np.isnan(S[i]):
S[i] = compare(
a,
pop[a].get("crowding"),
b,
pop[b].get("crowding"),
method="larger_is_better",
return_random_if_equal=True,
)
return S[:, None].astype(np.int)
def binary_search(F, i, fronts):
"""
Find the front rank for the i-th individual through binary search.
Parameters
----------
F: np.ndarray
the objective values
i: int
the index of the individual
fronts: list
individuals in each front
"""
num_found_fronts = len(fronts)
if num_found_fronts == 0:
return 0
k_min = 0 # the lower bound for checking
k_max = num_found_fronts # the upper bound for checking
k = floor((k_max + k_min) / 2 + 0.5) # the front now checked
current = F[i]
while True:
# solutions in the k-th front, examine in reverse order
fk_indices = fronts[k - 1]
solutions = F[fk_indices[::-1]]
non_dominated = True
for f in solutions:
relation = Dominator.get_relation(current, f)
if relation == -1:
non_dominated = False
break
# binary search
if non_dominated:
if k == k_min + 1:
return k - 1
else:
k_max = k
k = floor((k_max + k_min) / 2 + 0.5)
else:
k_min = k
if k_max == k_min + 1 and k_max < num_found_fronts:
return k_max - 1
elif k_min == num_found_fronts:
return num_found_fronts
else:
k = floor((k_max + k_min) / 2 + 0.5)
def comp_by_dom_and_crowding(pop, indices, data):
if len(indices) != 2:
raise ValueError("Only implemented for binary tournament!")
first = indices[0]
second = indices[1]
rel = Dominator.get_relation(pop.F[first, :], pop.F[second, :])
if rel == 1:
return first
elif rel == -1:
return second
else:
if data.crowding[first] > data.crowding[second]:
return first
elif data.crowding[second] > data.crowding[first]:
return second
else:
return indices[random.randint(0, 2)]
def comp_by_cv_dom_then_random(pop, P, **kwargs):
S = np.full(P.shape[0], np.nan)
for i in range(P.shape[0]):
a, b = P[i, 0], P[i, 1]
if pop[a].CV <= 0.0 and pop[b].CV <= 0.0:
rel = Dominator.get_relation(pop[a].F, pop[b].F)
if rel == 1:
S[i] = a
elif rel == -1:
S[i] = b
else:
S[i] = np.random.choice([a, b])
elif pop[a].CV <= 0.0:
S[i] = a
elif pop[b].CV <= 0.0:
S[i] = b
else:
S[i] = np.random.choice([a, b])
return S[:, None].astype(np.int)