Source code for jmetal.algorithm.multiobjective.mocell
import copy
from functools import cmp_to_key
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, Selection
from jmetal.core.problem import Problem
from jmetal.operator import BinaryTournamentSelection
from jmetal.util.archive import BoundedArchive
from jmetal.util.density_estimator import CrowdingDistance, DensityEstimator
from jmetal.util.evaluator import Evaluator
from jmetal.util.generator import Generator
from jmetal.util.neighborhood import Neighborhood
from jmetal.util.ranking import FastNonDominatedRanking, Ranking
from jmetal.util.comparator import Comparator, MultiComparator
from jmetal.util.termination_criterion import TerminationCriterion
S = TypeVar('S')
R = TypeVar('R')
"""
.. module:: MOCell
:platform: Unix, Windows
:synopsis: MOCell (Multi-Objective Cellular evolutionary algorithm) implementation
.. moduleauthor:: Antonio J. Nebro <antonio@lcc.uma.es>
"""
[docs]class MOCell(GeneticAlgorithm[S, R]):
def __init__(self,
problem: Problem,
population_size: int,
neighborhood: Neighborhood,
archive: BoundedArchive,
mutation: Mutation,
crossover: Crossover,
selection: Selection = BinaryTournamentSelection(
MultiComparator([FastNonDominatedRanking.get_comparator(),
CrowdingDistance.get_comparator()])),
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):
"""
MOCEll implementation as described in:
: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`).
:param selection: Selection operator (see :py:mod:`jmetal.operator.selection`).
"""
super(MOCell, self).__init__(
problem=problem,
population_size=population_size,
offspring_population_size=1,
mutation=mutation,
crossover=crossover,
selection=selection,
termination_criterion=termination_criterion,
population_evaluator=population_evaluator,
population_generator=population_generator
)
self.dominance_comparator = dominance_comparator
self.neighborhood = neighborhood
self.archive = archive
self.current_individual = 0
self.current_neighbors = []
self.comparator = MultiComparator([FastNonDominatedRanking.get_comparator(),
CrowdingDistance.get_comparator()])
[docs] def init_progress(self) -> None:
super().init_progress()
for solution in self.solutions:
self.archive.add(copy.copy(solution))
[docs] def update_progress(self) -> None:
super().update_progress()
self.current_individual = (self.current_individual + 1) % self.population_size
[docs] def selection(self, population: List[S]):
parents = []
self.current_neighbors = self.neighborhood.get_neighbors(self.current_individual, population)
self.current_neighbors.append(self.solutions[self.current_individual])
parents.append(self.selection_operator.execute(self.current_neighbors))
if len(self.archive.solution_list) > 0:
parents.append(self.selection_operator.execute(self.archive.solution_list))
else:
parents.append(self.selection_operator.execute(self.current_neighbors))
return parents
[docs] def reproduction(self, mating_population: List[S]) -> List[S]:
number_of_parents_to_combine = self.crossover_operator.get_number_of_parents()
if len(mating_population) % number_of_parents_to_combine != 0:
raise Exception('Wrong number of parents')
offspring_population = self.crossover_operator.execute(mating_population)
self.mutation_operator.execute(offspring_population[0])
return [offspring_population[0]]
[docs] def replacement(self, population: List[S], offspring_population: List[S]) -> List[List[S]]:
result = self.dominance_comparator.compare(population[self.current_individual], offspring_population[0])
if result == 1: # the offspring individual dominates the current one
population[self.current_individual] = offspring_population[0]
self.archive.add(offspring_population[0])
elif result == 0: # the offspring and current individuals are non-dominated
new_individual = offspring_population[0]
self.current_neighbors.append(new_individual)
ranking: Ranking = FastNonDominatedRanking()
ranking.compute_ranking(self.current_neighbors)
density_estimator: DensityEstimator = CrowdingDistance()
for i in range(ranking.get_number_of_subfronts()):
density_estimator.compute_density_estimator(ranking.get_subfront(i))
self.current_neighbors.sort(key=cmp_to_key(self.comparator.compare))
worst_solution = self.current_neighbors[-1]
self.archive.add(new_individual)
if worst_solution != new_individual:
population[self.current_individual] = new_individual
return population
[docs] def get_result(self) -> R:
return self.archive.solution_list
[docs] def get_name(self) -> str:
return 'MOCell'