algorithm = NSGAIII(
problem=problem,
mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
crossover=SBXCrossover(probability=1.0, distribution_index=20),
selection=BinaryTournamentSelection(comparator=RankingAndCrowdingDistanceComparator()),
termination_criterion=StoppingByEvaluations(max=max_evaluations)
)
progress_bar = ProgressBarObserver(max=max_evaluations)
algorithm.observable.register(observer=progress_bar)
algorithm.observable.register(observer=VisualizerObserver(reference_front=problem.reference_front, display_frequency=1000))
algorithm.run()
front = algorithm.get_result()
label = algorithm.get_name() + "." + problem.get_name()
algorithm_name = label
# Plot front
plot_front = Plot(plot_title='Pareto front approximation', axis_labels=problem.obj_labels)
plot_front.plot(front, label=label, filename=algorithm_name)
# Plot interactive front
plot_front = InteractivePlot(plot_title='Pareto front approximation', axis_labels=problem.obj_labels)
plot_front.plot(front, label=label, filename=algorithm_name)
# Save results to file
print_function_values_to_file(front, 'FUN.' + label)
print_variables_to_file(front, 'VAR.'+ label)
print('Algorithm (continuous problem): ' + algorithm.get_name())
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()
# Plot front
plot_front = Plot(plot_title='Pareto front approximation',
axis_labels=problem.obj_labels)
plot_front.plot(front, label=algorithm.label)
print('Algorithm (continuous problem): ' + algorithm.get_name())
print('Problem: ' + problem.get_name())
print('Computing time: ' + str(algorithm.total_computing_time))
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations))
algorithm.observable.register(observer=ProgressBarObserver(
max=max_evaluations))
# algorithm.observable.register(
# observer=VisualizerObserver(reference_front=problem.reference_front, display_frequency=100))
algorithm.run()
front = algorithm.get_result()
print(front)
plot_front = Plot(title='Pareto front approximation',
reference_front=problem.reference_front,
axis_labels=problem.obj_labels)
plot_front.plot(front,
label='NSGAIII-' + problem.get_name(),
filename='../images/' + algorithm.get_name() + "-" +
problem.get_name(),
format='png')
# Plot interactive front
plot_front = InteractivePlot(title='Pareto front approximation. Problem: ' +
problem.get_name(),
reference_front=problem.reference_front,
axis_labels=problem.obj_labels)
plot_front.plot(
front,
label='NSGAIII-' + problem.get_name(),
filename='../images/' + algorithm.get_name() + "-" + problem.get_name(),
)
hv = HyperVolume(reference_point=[1, 1, 1, 1]).compute(