Source code for jmetal.algorithm.multiobjective.spea2

from typing import List, TypeVar

from jmetal.algorithm.singleobjective.genetic_algorithm import GeneticAlgorithm
from jmetal.config import store
from jmetal.core.operator import Crossover, Mutation
from jmetal.core.problem import Problem
from jmetal.operator.selection import BinaryTournamentSelection
from jmetal.util.comparator import Comparator, MultiComparator
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.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 result(self) -> R: return self.solutions
[docs] def get_name(self) -> str: return "SPEA2"