Source code for jmetal.algorithm.multiobjective.spea2
from typing import TypeVar, List
from jmetal.algorithm.singleobjective.genetic_algorithm import GeneticAlgorithm
from jmetal.config import store
from jmetal.core.operator import Mutation, Crossover
from jmetal.core.problem import Problem
from jmetal.operator import BinaryTournamentSelection
from jmetal.util.density_estimator import KNearestNeighborDensityEstimator
from jmetal.util.evaluator import Evaluator
from jmetal.util.generator import Generator
from jmetal.util.ranking import StrengthRanking
from jmetal.util.replacement import RankingAndDensityEstimatorReplacement, RemovalPolicyType
from jmetal.util.comparator import Comparator, MultiComparator
from jmetal.util.termination_criterion import TerminationCriterion
S = TypeVar('S')
R = TypeVar('R')
"""
.. module:: SPEA2
:platform: Unix, Windows
:synopsis: SPEA2 implementation. Note that we do not follow the structure of the original SPEA2 code. We consider
SPEA2 as a genetic algorithm with binary tournament selection, with a comparator based on the strength fitness and
the KNN distance, and a sequential replacement strategy based in iteratively (sequentially)
removing the worst solution of the population + offspring population. The worst solutions is selected again
considering the strength fitness and KNN distance. Note that the implementation is exactly the same of NSGA-II,
but using the fast nondominated sorting and the crowding distance density estimator, and the replacement follows a
one-shot scheme (once the solutions are ordered, the best ones are selected without recomputing the ranking and
density estimator).
.. moduleauthor:: Antonio J. Nebro <antonio@lcc.uma.es>
"""
[docs]class SPEA2(GeneticAlgorithm[S, R]):
def __init__(self,
problem: Problem,
population_size: int,
offspring_population_size: int,
mutation: Mutation,
crossover: Crossover,
termination_criterion: TerminationCriterion = store.default_termination_criteria,
population_generator: Generator = store.default_generator,
population_evaluator: Evaluator = store.default_evaluator,
dominance_comparator: Comparator = store.default_comparator):
"""
:param problem: The problem to solve.
:param population_size: Size of the population.
:param mutation: Mutation operator (see :py:mod:`jmetal.operator.mutation`).
:param crossover: Crossover operator (see :py:mod:`jmetal.operator.crossover`).
"""
multi_comparator = MultiComparator([StrengthRanking.get_comparator(),
KNearestNeighborDensityEstimator.get_comparator()])
selection = BinaryTournamentSelection(comparator=multi_comparator)
super(SPEA2, self).__init__(
problem=problem,
population_size=population_size,
offspring_population_size=offspring_population_size,
mutation=mutation,
crossover=crossover,
selection=selection,
termination_criterion=termination_criterion,
population_evaluator=population_evaluator,
population_generator=population_generator
)
self.dominance_comparator = dominance_comparator
[docs] def replacement(self, population: List[S], offspring_population: List[S]) -> List[List[S]]:
""" This method joins the current and offspring populations to produce the population of the next generation
by applying the ranking and crowding distance selection.
:param population: Parent population.
:param offspring_population: Offspring population.
:return: New population after ranking and crowding distance selection is applied.
"""
ranking = StrengthRanking(self.dominance_comparator)
density_estimator = KNearestNeighborDensityEstimator()
r = RankingAndDensityEstimatorReplacement(ranking, density_estimator, RemovalPolicyType.SEQUENTIAL)
solutions = r.replace(population, offspring_population)
return solutions
[docs] def get_result(self) -> R:
return self.solutions
[docs] def get_name(self) -> str:
return 'SPEA2'
