import numpy as np
import warnings
from pymoo.algorithms.base.genetic import GeneticAlgorithm
from pymoo.docs import parse_doc_string
from pymoo.operators.crossover.sbx import SBX
from pymoo.operators.mutation.pm import PM
from pymoo.operators.survival.rank_and_crowding import RankAndCrowding
from pymoo.operators.sampling.rnd import FloatRandomSampling
from pymoo.operators.selection.tournament import compare, TournamentSelection
from pymoo.termination.default import DefaultMultiObjectiveTermination
from pymoo.util.display.multi import MultiObjectiveOutput
from pymoo.util.dominator import Dominator
from pymoo.util.misc import has_feasible
# ---------------------------------------------------------------------------------------------------------
# Binary Tournament Selection Function
# ---------------------------------------------------------------------------------------------------------
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, random_state=algorithm.random_state)
# 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, random_state=algorithm.random_state)
return S[:, None].astype(int, copy=False)
# ---------------------------------------------------------------------------------------------------------
# Survival Selection
# ---------------------------------------------------------------------------------------------------------
class RankAndCrowdingSurvival(RankAndCrowding):
def __init__(self, nds=None, crowding_func="cd"):
super().__init__(nds, crowding_func)
# =========================================================================================================
# Implementation
# =========================================================================================================
[docs]
class NSGA2(GeneticAlgorithm):
def __init__(self,
pop_size=100,
sampling=FloatRandomSampling(),
selection=TournamentSelection(func_comp=binary_tournament),
crossover=SBX(eta=15, prob=0.9),
mutation=PM(eta=20),
survival=RankAndCrowding(),
output=MultiObjectiveOutput(),
**kwargs):
super().__init__(
pop_size=pop_size,
sampling=sampling,
selection=selection,
crossover=crossover,
mutation=mutation,
survival=survival,
output=output,
advance_after_initial_infill=True,
**kwargs)
self.termination = DefaultMultiObjectiveTermination()
self.tournament_type = 'comp_by_dom_and_crowding'
def _set_optimum(self, **kwargs):
if not has_feasible(self.pop):
self.opt = self.pop[[np.argmin(self.pop.get("CV"))]]
else:
self.opt = self.pop[self.pop.get("rank") == 0]
parse_doc_string(NSGA2.__init__)
