[1]:
from jmetal.algorithm.multiobjective.hype import HYPE
from jmetal.core.solution import FloatSolution
from jmetal.operator import SBXCrossover, PolynomialMutation
from jmetal.problem import ZDT1
from jmetal.util.termination_criterion import StoppingByEvaluations
problem = ZDT1()
reference_point = FloatSolution([0], [1], problem.number_of_objectives, )
reference_point.objectives = [1., 1.] # Mandatory for HYPE
algorithm = HYPE(
problem=problem,
reference_point=reference_point,
population_size=100,
offspring_population_size=100,
mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
crossover=SBXCrossover(probability=1.0, distribution_index=20),
termination_criterion=StoppingByEvaluations(2500)
)
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='HYPE-ZDT1')
jmetal.algorithm.multiobjective.hype.
HYPE
(problem: jmetal.core.problem.Problem, reference_point: jmetal.core.solution.Solution, population_size: int, offspring_population_size: int, mutation: jmetal.core.operator.Mutation, crossover: jmetal.core.operator.Crossover, 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>)[source]¶Bases: jmetal.algorithm.singleobjective.genetic_algorithm.GeneticAlgorithm