def main() -> None:
problem = Kursawe()
algorithm = SMPSO(problem=problem,
swarm_size=100,
max_evaluations=25000,
mutation=Polynomial(1.0 / problem.number_of_variables,
distribution_index=20),
leaders=CrowdingDistanceArchive(100))
algorithm.run()
result = algorithm.get_result()
SolutionListOutput[FloatSolution].print_function_values_to_file(
"FUN." + problem.get_name(), result)
logger.info("Algorithm (continuous problem): " + algorithm.get_name())
logger.info("Problem: " + problem.get_name())
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)
plot_front.plot(front, label=algorithm.label, filename=algorithm.get_name())
# Plot interactive front
plot_front = InteractivePlot(plot_title='Pareto front approximation', reference_front=problem.reference_front,
axis_labels=problem.obj_labels)
plot_front.plot(front, label=algorithm.label, filename=algorithm.get_name())
# Save results to file
print_function_values_to_file(front, 'FUN.' + algorithm.label)
print_variables_to_file(front, 'VAR.' + algorithm.label)
.add_function(f2) \
.add_constraint(c1) \
.add_constraint(c2)
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.observable.register(observer=VisualizerObserver())
algorithm.run()
front = algorithm.get_result()
# Save results to file
print_function_values_to_file(
front, 'FUN.' + algorithm.get_name() + "." + problem.get_name())
print_variables_to_file(
front, 'VAR.' + algorithm.get_name() + "." + problem.get_name())
print('Algorithm (continuous problem): ' + algorithm.get_name())
print('Problem: ' + problem.get_name())
print('Computing time: ' + str(algorithm.total_computing_time))
problem = OnTheFlyFloatProblem()
problem \
.set_name('Schaffer') \
.add_variable(-10000.0, 10000.0) \
.add_function(f1) \
.add_function(f2)
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.observable.register(observer=VisualizerObserver())
algorithm.run()
front = algorithm.get_result()
# Save results to file
print_function_values_to_file(front, 'FUN.' + algorithm.get_name() + "." + problem.get_name())
print_variables_to_file(front, 'VAR.' + algorithm.get_name() + "." + problem.get_name())
print('Algorithm (continuous problem): ' + algorithm.get_name())
print('Problem: ' + problem.get_name())
print('Computing time: ' + str(algorithm.total_computing_time))
print_function_values_to_file,
print_variables_to_file,
read_solutions,
)
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == "__main__":
problem = ZDT1Modified()
problem.reference_front = read_solutions(filename="resources/reference_front/ZDT1.pf")
max_evaluations = 100
algorithm = SMPSO(
problem=problem,
swarm_size=10,
mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
leaders=CrowdingDistanceArchive(10),
termination_criterion=StoppingByEvaluations(max_evaluations=max_evaluations),
swarm_evaluator=SparkEvaluator(),
)
algorithm.run()
front = algorithm.get_result()
# Save results to file
print_function_values_to_file(front, "FUN." + algorithm.get_name() + "." + problem.get_name())
print_variables_to_file(front, "VAR." + algorithm.get_name() + "." + problem.get_name())
print(f"Algorithm: {algorithm.get_name()}")
print(f"Problem: {problem.get_name()}")
print(f"Computing time: {algorithm.total_computing_time}")
progress_bar = ProgressBarObserver(max=max_evaluations)
algorithm.observable.register(observer=progress_bar)
algorithm.observable.register(observer=VisualizerObserver(
reference_front=problem.reference_front))
algorithm.run()
front = algorithm.get_result()
# Plot front
plot_front = Plot(plot_title='Pareto front approximation',
reference_front=problem.reference_front,
axis_labels=problem.obj_labels)
plot_front.plot(front,
label=algorithm.label,
filename=algorithm.get_name())
# Plot interactive front
plot_front = InteractivePlot(plot_title='Pareto front approximation',
reference_front=problem.reference_front,
axis_labels=problem.obj_labels)
plot_front.plot(front,
label=algorithm.label,
filename=algorithm.get_name())
# Save results to file
print_function_values_to_file(front, 'FUN.' + algorithm.label)
print_variables_to_file(front, 'VAR.' + algorithm.label)
print('Algorithm (continuous problem): ' + algorithm.get_name())
print('Problem: ' + problem.get_name())
swarm_size=100,
mutation=PolynomialMutation(probability=1.0 /
problem.number_of_variables,
distribution_index=20),
leaders=CrowdingDistanceArchive(100),
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))
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)
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()
# Plot front
label = '{}-{} with {} objectives'.format(algorithm.get_name(),
problem.get_name(),
problem.number_of_objectives)
plot_front = Plot(plot_title='Pareto front approximation',
reference_front=problem.reference_front,
axis_labels=problem.obj_labels)
plot_front.plot(front, label=label, filename=label)
# Plot interactive front
plot_front = InteractivePlot(plot_title='Pareto front approximation',
reference_front=problem.reference_front,
axis_labels=problem.obj_labels)
plot_front.plot(front, label=label, filename=label, normalize=True)
# Save variables to file
