from jmetal.algorithm.multiobjective.nsgaiii import NSGAIII, UniformReferenceDirectionFactory
from jmetal.lab.visualization import Plot
from jmetal.operator import SBXCrossover, PolynomialMutation
from jmetal.problem import DTLZ1
from jmetal.util.observer import ProgressBarObserver
from jmetal.util.solutions import read_solutions
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == '__main__':
problem = DTLZ1()
problem.reference_front = read_solutions(
filename='esources/reference_front/DTLZ1.3D.pf')
max_evaluations = 25000
algorithm = NSGAIII(
problem=problem,
population_size=92,
reference_directions=UniformReferenceDirectionFactory(3, n_points=91),
mutation=PolynomialMutation(probability=1.0 /
problem.number_of_variables,
distribution_index=20),
crossover=SBXCrossover(probability=1.0, distribution_index=30),
termination_criterion=StoppingByEvaluations(max=max_evaluations))
algorithm.observable.register(observer=ProgressBarObserver(
max=max_evaluations))
algorithm.run()
front = algorithm.get_result()
from jmetal.algorithm.multiobjective.smpso import SMPSO
from jmetal.lab.visualization import InteractivePlot, Plot
from jmetal.operator import PolynomialMutation
from jmetal.problem import DTLZ1
from jmetal.util.archive import CrowdingDistanceArchive
from jmetal.util.observer import ProgressBarObserver
from jmetal.util.solutions import print_function_values_to_file, print_variables_to_file
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == '__main__':
problem = DTLZ1(number_of_objectives=5)
max_evaluations = 25000
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=max_evaluations)
)
algorithm.observable.register(observer=ProgressBarObserver(max=max_evaluations))
algorithm.run()
front = algorithm.get_result()
label = algorithm.get_name() + "." + problem.get_name()
# Plot front
plot_front = Plot(plot_title='Pareto front approximation', reference_front=problem.reference_front,
axis_labels=problem.obj_labels)
from jmetal.algorithm import NSGAII
from jmetal.problem import DTLZ1
from jmetal.operator import SBX, Polynomial, BinaryTournamentSelection
from jmetal.component import ProgressBarObserver, VisualizerObserver, RankingAndCrowdingDistanceComparator
from jmetal.util import FrontPlot, SolutionList
if __name__ == '__main__':
problem = DTLZ1(rf_path='../../resources/reference_front/DTLZ1.pf')
algorithm = NSGAII(
problem=problem,
population_size=100,
max_evaluations=50000,
mutation=Polynomial(probability=1.0 / problem.number_of_variables, distribution_index=20),
crossover=SBX(probability=1.0, distribution_index=20),
selection=BinaryTournamentSelection(comparator=RankingAndCrowdingDistanceComparator())
)
progress_bar = ProgressBarObserver(step=100, maximum=50000)
visualizer = VisualizerObserver()
algorithm.observable.register(observer=progress_bar)
algorithm.observable.register(observer=visualizer)
algorithm.run()
front = algorithm.get_result()
# Plot frontier to file
pareto_front = FrontPlot(plot_title='NSGAII-DTLZ1', axis_labels=problem.obj_labels)
pareto_front.plot(front, reference_front=problem.reference_front)
pareto_front.to_html(filename='NSGAII-DTLZ1')