IBEA

Example

from jmetal.algorithm.multiobjective.ibea import IBEA
from jmetal.operator.crossover import SBXCrossover
from jmetal.operator.mutation import PolynomialMutation
from jmetal.problem import ZDT1
from jmetal.util.termination_criterion import StoppingByEvaluations

problem = ZDT1()

max_evaluations = 25000

algorithm = IBEA(
    problem=problem,
    kappa=1.,
    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(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='IBEA-ZDT1')

API

class jmetal.algorithm.multiobjective.ibea.IBEA(problem: ~jmetal.core.problem.Problem, population_size: int, offspring_population_size: int, mutation: ~jmetal.core.operator.Mutation, crossover: ~jmetal.core.operator.Crossover, kappa: float, 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>)

Bases: GeneticAlgorithm[S, R]

compute_fitness_values(population: List[S], kappa: float) → List[S]

create_initial_solutions() → List[S]

Creates the initial list of solutions of a metaheuristic.

get_name() → str

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

Replace least-fit population with new individuals.

result() → R