if __name__ == "__main__":
problem = LIRCMOP2()
problem.reference_front = read_solutions(
filename="resources/reference_front/LIRCMOP2.pf")
max_evaluations = 300000
algorithm = MOEADIEpsilon(
problem=problem,
population_size=300,
crossover=DifferentialEvolutionCrossover(CR=1.0, F=0.5),
mutation=PolynomialMutation(probability=1.0 /
problem.number_of_variables,
distribution_index=20),
aggregative_function=Tschebycheff(
dimension=problem.number_of_objectives),
neighbor_size=20,
neighbourhood_selection_probability=0.9,
max_number_of_replaced_solutions=2,
weight_files_path="resources/MOEAD_weights",
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations),
)
algorithm.run()
front = algorithm.get_result()
# Save results to file
print_function_values_to_file(front, "FUN." + algorithm.label)
print_variables_to_file(front, "VAR." + algorithm.label)
from jmetal.util.observer import ProgressBarObserver, VisualizerObserver
from jmetal.util.solutions import read_solutions, print_function_values_to_file, print_variables_to_file
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == '__main__':
problem = LIRCMOP2()
problem.reference_front = read_solutions(filename='../../resources/reference_front/LIRCMOP2.pf')
max_evaluations = 300000
algorithm = MOEADIEpsilon(
problem=problem,
population_size=300,
crossover=DifferentialEvolutionCrossover(CR=1.0, F=0.5, K=0.5),
mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
aggregative_function=Tschebycheff(dimension=problem.number_of_objectives),
neighbor_size=20,
neighbourhood_selection_probability=0.9,
max_number_of_replaced_solutions=2,
weight_files_path='../../resources/MOEAD_weights',
termination_criterion=StoppingByEvaluations(max=max_evaluations)
)
algorithm.observable.register(observer=ProgressBarObserver(max=max_evaluations))
algorithm.observable.register(observer=VisualizerObserver(reference_front=problem.reference_front, display_frequency=1000))
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)