IBEA

IBEA (Indicator-Based Evolutionary Algorithm) uses a binary quality indicator (additive epsilon, by default) to guide environmental selection, instead of Pareto dominance and a density estimator.

from jmetal.algorithm.multiobjective.ibea import IBEA
from jmetal.operator.crossover import SBXCrossover
from jmetal.operator.mutation import PolynomialMutation
from jmetal.problem.multiobjective.dtlz import DTLZ1
from jmetal.util.plotting import save_plt_to_file
from jmetal.util.solution import (
    print_function_values_to_file,
    print_variables_to_file,
    read_solutions,
)
from jmetal.util.termination_criterion import StoppingByEvaluations

if __name__ == "__main__":
    problem = DTLZ1()
    problem.reference_front = read_solutions(filename="resources/reference_fronts/DTLZ1.3D.pf")

    algorithm = IBEA(
        problem=problem,
        kappa=1.0,
        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=50000),
    )

    algorithm.run()
    front = algorithm.result()

    # Save results to file
    print_function_values_to_file(front, "FUN." + algorithm.label)
    print_variables_to_file(front, "VAR." + algorithm.label)

    # Save a PNG visualization of the front (and optional HTML if Plotly available)
    try:
        png = save_plt_to_file(front, "FUN." + algorithm.label, out_dir=".", html_plotly=True)
        print(f"Saved front plot to: {png}")
    except Exception as e:
        print(f"Warning: could not generate front plot: {e}")

    print(f"Algorithm: {algorithm.get_name()}")
    print(f"Problem: {problem.name()}")
    print(f"Computing time: {algorithm.total_computing_time}")