def test_should_hypervolume_return_the_correct_value_when_applied_to_the_ZDT1_reference_front(
self):
problem = ZDT1()
problem.reference_front = read_solutions(
filename='resources/reference_front/ZDT1.pf')
reference_point = [1, 1]
hv = HyperVolume(reference_point)
value = hv.compute(problem.reference_front)
self.assertAlmostEqual(0.666, value, delta=0.001)
from jmetal.util.solutions import SparkEvaluator
from examples.multiobjective.parallel.zdt1_modified import ZDT1Modified
from jmetal.algorithm.multiobjective.omopso import OMOPSO
from jmetal.operator import UniformMutation
from jmetal.operator.mutation import NonUniformMutation
from jmetal.util.archive import CrowdingDistanceArchive
from jmetal.util.solutions_utils import print_function_values_to_file, print_variables_to_file
from jmetal.util.solutions_utils import read_solutions
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == '__main__':
problem = ZDT1Modified()
problem.reference_front = read_solutions(
filename='resources/reference_front/{}.pf'.format(problem.get_name()))
mutation_probability = 1.0 / problem.number_of_variables
max_evaluations = 100
swarm_size = 10
algorithm = OMOPSO(
problem=problem,
swarm_size=swarm_size,
epsilon=0.0075,
uniform_mutation=UniformMutation(probability=mutation_probability,
perturbation=0.5),
non_uniform_mutation=NonUniformMutation(
mutation_probability,
perturbation=0.5,
max_iterations=max_evaluations / swarm_size),
leaders=CrowdingDistanceArchive(10),
termination_criterion=StoppingByEvaluations(max=max_evaluations),
from jmetal.algorithm.multiobjective.spea2 import SPEA2
from jmetal.lab.visualization import Plot, InteractivePlot
from jmetal.operator import SBXCrossover, PolynomialMutation
from jmetal.problem import ZDT1
from jmetal.util.observer import ProgressBarObserver, VisualizerObserver
from jmetal.util.solutions_utils import read_solutions, print_function_values_to_file, print_variables_to_file
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == '__main__':
problem = ZDT1()
problem.reference_front = read_solutions(
filename='resources/reference_front/ZDT1.pf')
max_evaluations = 20000
algorithm = SPEA2(
problem=problem,
population_size=40,
offspring_population_size=40,
mutation=PolynomialMutation(probability=1.0 /
problem.number_of_variables,
distribution_index=20),
crossover=SBXCrossover(probability=1.0, distribution_index=20),
termination_criterion=StoppingByEvaluations(max=max_evaluations))
algorithm.observable.register(observer=ProgressBarObserver(
max=max_evaluations))
algorithm.observable.register(observer=VisualizerObserver(
reference_front=problem.reference_front))
algorithm.run()
front = algorithm.get_result()
from jmetal.algorithm.multiobjective.moead import MOEAD
from jmetal.lab.visualization import Plot, InteractivePlot
from jmetal.operator import PolynomialMutation, DifferentialEvolutionCrossover
from jmetal.problem import DTLZ2
from jmetal.util.aggregative_function import Tschebycheff
from jmetal.util.observer import ProgressBarObserver, VisualizerObserver
from jmetal.util.solutions_utils import read_solutions, print_function_values_to_file, print_variables_to_file
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 = 50000
algorithm = MOEAD(
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))
from jmetal.algorithm.multiobjective.moead import MOEADIEpsilon
from jmetal.lab.visualization import InteractivePlot
from jmetal.operator import PolynomialMutation, DifferentialEvolutionCrossover
from jmetal.problem import Srinivas
from jmetal.util.aggregative_function import Tschebycheff
from jmetal.util.observer import ProgressBarObserver, VisualizerObserver
from jmetal.util.solutions_utils import read_solutions, print_function_values_to_file, print_variables_to_file
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == '__main__':
problem = Srinivas()
problem.reference_front = read_solutions(
filename='resources/reference_front/Srinivas.pf')
max_evaluations = 10000
algorithm = MOEADIEpsilon(
problem=problem,
population_size=100,
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))
from jmetal.algorithm.multiobjective.moead import MOEADIEpsilon
from jmetal.lab.visualization import InteractivePlot
from jmetal.operator import PolynomialMutation, DifferentialEvolutionCrossover
from jmetal.problem.multiobjective.lircmop import LIRCMOP2
from jmetal.util.aggregative_function import Tschebycheff
from jmetal.util.observer import ProgressBarObserver, VisualizerObserver
from jmetal.util.solutions_utils 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))
