HYPE

Example

[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')
../../../../_images/api_algorithm_multiobjective_eas_hype_5_0.png

API

class 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

evaluate(population: List[S])[source]

Evaluates a solution list.

get_name() → str[source]
get_result() → R[source]
replacement(population: List[S], offspring_population: List[S]) → List[List[S]][source]

Replace least-fit population with new individuals.