"""GDE3 multi-objective differential evolution algorithm and its crowding-metric variants."""
from typing import Optional, Tuple, Union
from pymoo.algorithms.moo.nsde import NSDE
from pymoo.core.population import Population
from pymoo.operators.survival.rank_and_crowding import RankAndCrowding
from pymoo.util.dominator import get_relation
# =========================================================================================================
# GDE3
# =========================================================================================================
[docs]
class GDE3(NSDE):
def __init__(
self,
pop_size: int = 100,
variant: str = "DE/rand/1/bin",
CR: float = 0.5,
F: Optional[Union[float, Tuple[float, float]]] = None,
gamma: float = 1e-4,
**kwargs,
):
"""GDE3 (Generalized Differential Evolution 3) extends DE to multi-objective problems.
Each trial vector competes one-to-one with its parent: if the trial dominates, it
replaces the parent; if the parent dominates, it is kept; otherwise both enter a
combined pool that is trimmed to pop_size via the survival operator.
Derived classes GDE3MNN, GDE32NN, GDE3PCD use alternative crowding metrics.
Reference: Kukkonen & Lampinen (2005). GDE3: The third evolution step of generalized
differential evolution. IEEE CEC 2005.
Args:
pop_size: Population size. Defaults to 100.
variant: DE strategy: "DE/selection/n/crossover". Defaults to 'DE/rand/1/bin'.
CR: Crossover rate in [0, 1]. Defaults to 0.5.
F: Scale factor(s). Defaults to randomized in (0, 1).
gamma: Jitter deviation. Defaults to 1e-4.
de_repair: Donor vector repair. Defaults to 'bounce-back'.
survival: Survival operator applied when pool > pop_size. Defaults to RankAndCrowding().
**kwargs: Additional keyword arguments forwarded to the NSDE base class.
"""
super().__init__(
pop_size=pop_size, variant=variant, CR=CR, F=F, gamma=gamma, **kwargs
)
def _advance(self, infills=None, **kwargs):
assert infills is not None, (
"This algorithm uses the AskAndTell interface; 'infills' must be provided."
)
survivors = []
for k in range(len(self.pop)):
off, parent = infills[k], self.pop[k]
rel = get_relation(parent, off)
if rel == 0:
survivors.extend([parent, off])
elif rel == -1:
survivors.append(off)
else:
survivors.append(parent)
survivors = Population.create(*survivors)
self.pop = self.survival.do(
self.problem,
survivors,
n_survive=self.n_offsprings,
random_state=self.random_state,
)
class GDE3MNN(GDE3):
"""GDE3 with MNN crowding metric — recommended for many-objective problems."""
def __init__(
self,
pop_size=100,
variant="DE/rand/1/bin",
CR=0.5,
F=None,
gamma=1e-4,
**kwargs,
):
super().__init__(
pop_size,
variant,
CR,
F,
gamma,
survival=RankAndCrowding(crowding_func="mnn"),
**kwargs,
)
class GDE32NN(GDE3):
"""GDE3 with 2NN crowding metric — recommended for many-objective problems."""
def __init__(
self,
pop_size=100,
variant="DE/rand/1/bin",
CR=0.5,
F=None,
gamma=1e-4,
**kwargs,
):
super().__init__(
pop_size,
variant,
CR,
F,
gamma,
survival=RankAndCrowding(crowding_func="2nn"),
**kwargs,
)
class GDE3PCD(GDE3):
"""GDE3 with PCD crowding metric — recommended for bi-objective problems."""
def __init__(
self,
pop_size=100,
variant="DE/rand/1/bin",
CR=0.5,
F=None,
gamma=1e-4,
**kwargs,
):
super().__init__(
pop_size,
variant,
CR,
F,
gamma,
survival=RankAndCrowding(crowding_func="pcd"),
**kwargs,
)
