MOCell

Example

from jmetal.algorithm.multiobjective.mocell import MOCell
from jmetal.operator.crossover import SBXCrossover
from jmetal.operator.mutation import PolynomialMutation
from jmetal.problem import ZDT4
from jmetal.util.archive import CrowdingDistanceArchive
from jmetal.util.neighborhood import C9
from jmetal.util.termination_criterion import StoppingByEvaluations

problem = ZDT4()

max_evaluations = 25000

algorithm = MOCell(
    problem=problem,
    population_size=100,
    neighborhood=C9(10, 10),
    archive=CrowdingDistanceArchive(100),
    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
    crossover=SBXCrossover(probability=1.0, distribution_index=20),
    termination_criterion=StoppingByEvaluations(max=max_evaluations)
)

algorithm.run()
front = algorithm.get_result()

We can now visualize the Pareto front approximation:

from jmetal.lab.visualization.plotting import Plot

plot_front = Plot(plot_title='Pareto front approximation', axis_labels=['x', 'y'])
plot_front.plot(front, label='MOCell-ZDT4')

API

class jmetal.algorithm.multiobjective.mocell.MOCell(problem: ~jmetal.core.problem.Problem, population_size: int, neighborhood: ~jmetal.util.neighborhood.Neighborhood, archive: ~jmetal.util.archive.BoundedArchive, mutation: ~jmetal.core.operator.Mutation, crossover: ~jmetal.core.operator.Crossover, selection: ~jmetal.core.operator.Selection = <jmetal.operator.selection.BinaryTournamentSelection object>, termination_criterion: ~jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, population_generator: ~jmetal.util.generator.Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: ~jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>, dominance_comparator: ~jmetal.util.comparator.Comparator = <jmetal.util.comparator.DominanceComparator object>)

Bases: GeneticAlgorithm[S, R]

get_name() → str

init_progress() → None

Initialize the algorithm.

replacement(population: List[S], offspring_population: List[S]) → List[List[S]]

Replace least-fit population with new individuals.

reproduction(mating_population: List[S]) → List[S]

Breed new individuals through crossover and mutation operations to give birth to offspring.

result() → R

selection(population: List[S])

Select the best-fit individuals for reproduction (parents).

update_progress() → None

Update the progress after each iteration.