MOEA/D

Example

from jmetal.algorithm.multiobjective.moead import MOEAD
from jmetal.operator.mutation import PolynomialMutation
from jmetal.operator.crossover import DifferentialEvolutionCrossover
from jmetal.problem import LZ09_F2
from jmetal.util.aggregative_function import Tschebycheff
from jmetal.util.termination_criterion import StoppingByEvaluations

problem = LZ09_F2()

max_evaluations = 150000

algorithm = MOEAD(
    problem=problem,
    population_size=300,
    crossover=DifferentialEvolutionCrossover(CR=1.0, F=0.5, K=0.5),
    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
    aggregative_function=Tschebycheff(dimension=problem.number_of_objectives),
    neighbor_size=20,
    neighbourhood_selection_probability=0.9,
    max_number_of_replaced_solutions=2,
    weight_files_path='resources/MOEAD_weights',
    termination_criterion=StoppingByEvaluations(max=max_evaluations)
)

algorithm.run()
solutions = algorithm.get_result()

We can now visualize the Pareto front approximation:

from jmetal.lab.visualization.plotting import Plot
from jmetal.util.solution import get_non_dominated_solutions

front = get_non_dominated_solutions(solutions)

plot_front = Plot(plot_title='Pareto front approximation', axis_labels=['x', 'y'])
plot_front.plot(front, label='MOEAD-LZ09_F2')

API

class jmetal.algorithm.multiobjective.moead.MOEAD(problem: ~jmetal.core.problem.Problem, population_size: int, mutation: ~jmetal.core.operator.Mutation, crossover: ~jmetal.operator.crossover.DifferentialEvolutionCrossover, aggregation_function: ~jmetal.util.aggregation_function.AggregationFunction, neighbourhood_selection_probability: float, max_number_of_replaced_solutions: int, neighbor_size: int, weight_files_path: str, termination_criterion: ~jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, population_generator: ~typing.Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: ~jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>)

Bases: GeneticAlgorithm

choose_neighbor_type()

generate_permutation_of_neighbors(subproblem_id)

get_name()

init_progress() → None

Initialize the algorithm.

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

selection(population: List[S])

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

update_current_subproblem_neighborhood(new_solution, population)