from jmetal.algorithm.multiobjective.nsgaii import NSGAII
from jmetal.operator import PolynomialMutation, SBXCrossover
from jmetal.problem import DTLZ2
from jmetal.util.comparator import DominanceComparator
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 = DTLZ2()
problem.reference_front = read_solutions(filename="resources/reference_front/DTLZ2.3D.pf")
max_evaluations = 25000
algorithm = NSGAII(
problem=problem,
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=max_evaluations),
dominance_comparator=DominanceComparator(),
)
algorithm.run()
front = algorithm.get_result()
# Save results to file
print_function_values_to_file(front, "FUN." + algorithm.label)
from jmetal.algorithm.multiobjective.smpso import SMPSO
from jmetal.operator import PolynomialMutation
from jmetal.problem import DTLZ2
from jmetal.util.archive import CrowdingDistanceArchive
from jmetal.util.observer import ProgressBarObserver
from jmetal.util.termination_criterion import StoppingByEvaluations
from jmetal.util.visualization.chord_plot import chord_diagram
if __name__ == '__main__':
problem = DTLZ2(number_of_objectives=5)
algorithm = SMPSO(problem=problem,
swarm_size=100,
mutation=PolynomialMutation(probability=1.0 /
problem.number_of_variables,
distribution_index=20),
leaders=CrowdingDistanceArchive(100),
termination_criterion=StoppingByEvaluations(max=25000))
algorithm.observable.register(observer=ProgressBarObserver(max=25000))
algorithm.run()
front = algorithm.get_result()
# Chord interactive plot
chord_diagram(front, nbins='auto')
print(
'Hover mouse over the white patches to depict samples as chords among objectives'
)