[1]:
from jmetal.algorithm.multiobjective.moead import MOEAD
from jmetal.operator import PolynomialMutation, 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:
[3]:
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')
jmetal.algorithm.multiobjective.moead.
MOEAD
(problem: jmetal.core.problem.Problem, population_size: int, mutation: jmetal.core.operator.Mutation, crossover: jmetal.operator.crossover.DifferentialEvolutionCrossover, aggregative_function: jmetal.util.aggregative_function.AggregativeFunction, 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: Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>)[source]¶Bases: jmetal.algorithm.singleobjective.genetic_algorithm.GeneticAlgorithm
replacement
(population: List[S], offspring_population: List[S]) → List[S][source]¶Replace least-fit population with new individuals.