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.algorithm.multiobjective.mocell import MOCell
from jmetal.lab.visualization import Plot
from jmetal.operator import SBXCrossover, PolynomialMutation
from jmetal.problem import ZDT1
from jmetal.util.archive import CrowdingDistanceArchive
from jmetal.util.neighborhood import C9
from jmetal.util.observer import ProgressBarObserver
from jmetal.util.solutions import read_solutions
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == '__main__':
problem = ZDT1()
problem.reference_front = read_solutions(filename='resources/reference_front/ZDT4.pf')
max_evaluations = 25000
algorithm = MOCell(
problem=problem,
population_size=100,
neighborhood=C9(10, 10),
archive=CrowdingDistanceArchive(100),
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.run()
front = algorithm.get_result()
# Plot front
Created on Mon Sep 30 10:33:43 2019
@author: Alejandro De la Cruz
"""
from jmetal.algorithm.multiobjective.nsgaii import NSGAII
from jmetal.lab.visualization import Plot, InteractivePlot
from jmetal.operator import SBXCrossover, PolynomialMutation
from jmetal.problem import ZDT1, ZDT3, ZDT4, ZDT6
from jmetal.util.observer import ProgressBarObserver, VisualizerObserver
from jmetal.util.solutions import read_solutions, print_function_values_to_screen
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == '__main__':
problem = ZDT3()
problem.reference_from = read_solutions(filename='mysolucionbonita.pf')
max_evaluations = 25000
algorithm = NSGAII(
problem=problem,
population_size=100,
offspring_population_size=100,
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))
from jmetal.algorithm.multiobjective.nsgaii import NSGAII
from jmetal.lab.visualization import Plot, InteractivePlot
from jmetal.operator import SBXCrossover, PolynomialMutation
from jmetal.problem import Srinivas
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.solutions.comparator import DominanceComparator
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 = 25000
algorithm = NSGAII(
problem=problem,
population_size=100,
offspring_population_size=100,
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),
dominance_comparator=DominanceComparator())
algorithm.observable.register(observer=ProgressBarObserver(
max=max_evaluations))
algorithm.observable.register(observer=VisualizerObserver(
reference_front=problem.reference_front))
from jmetal.algorithm.multiobjective.gde3 import GDE3
from jmetal.lab.visualization import InteractivePlot, Plot
from jmetal.problem import ZDT2
from jmetal.util.observer import VisualizerObserver
from jmetal.util.solutions import (
print_function_values_to_file,
print_variables_to_file,
read_solutions,
)
from jmetal.util.solutions.comparator import GDominanceComparator
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == "__main__":
problem = ZDT2()
problem.reference_front = read_solutions(
filename="resources/reference_front/{}.pf".format(problem.get_name()))
max_evaluations = 25000
reference_point = [0.2, 0.5]
algorithm = GDE3(
problem=problem,
population_size=100,
cr=0.5,
f=0.5,
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations),
dominance_comparator=GDominanceComparator(reference_point),
)
algorithm.observable.register(
from jmetal.algorithm.multiobjective.moead import MOEAD
from jmetal.lab.visualization import Plot, InteractivePlot
from jmetal.operator import PolynomialMutation, DifferentialEvolutionCrossover
from jmetal.problem import LZ09_F2
from jmetal.util.aggregative_function import Tschebycheff
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 = LZ09_F2()
problem.reference_front = read_solutions(filename='../../resources/reference_front/LZ09_F2.pf')
max_evaluations = 150000
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)
)
algorithm.observable.register(observer=ProgressBarObserver(max=max_evaluations))
algorithm.observable.register(
observer=VisualizerObserver(reference_front=problem.reference_front, display_frequency=1000))
from jmetal.algorithm.multiobjective.spea2 import SPEA2
from jmetal.lab.visualization import Plot, InteractivePlot
from jmetal.operator import SBXCrossover, PolynomialMutation
from jmetal.problem import DTLZ2
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 = DTLZ2()
problem.reference_front = read_solutions(filename='../../resources/reference_front/DTLZ2.3D.pf')
max_evaluations = 20000
algorithm = SPEA2(
problem=problem,
population_size=20,
offspring_population_size=20,
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()
# 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())
def generate_summary_from_experiment(input_dir: str, quality_indicators: List[QualityIndicator],
reference_fronts: str = ''):
""" Compute a list of quality indicators. The input data directory *must* met the following structure (this is generated
automatically by the Experiment class):
* <base_dir>
* algorithm_a
* problem_a
* FUN.0.tsv
* FUN.1.tsv
* VAR.0.tsv
* VAR.1.tsv
* ...
:param input_dir: Directory where all the input data is found (function values and variables).
:param reference_fronts: Directory where reference fronts are found.
:param quality_indicators: List of quality indicators to compute.
:return: None.
"""
if not quality_indicators:
quality_indicators = []
with open('QualityIndicatorSummary.csv', 'w+') as of:
of.write('Algorithm,Problem,ExecutionId,IndicatorName,IndicatorValue\n')
for dirname, _, filenames in os.walk(input_dir):
for filename in filenames:
try:
# Linux filesystem
algorithm, problem = dirname.split('/')[-2:]
except ValueError:
# Windows filesystem
algorithm, problem = dirname.split('\\')[-2:]
if 'TIME' in filename:
run_tag = [s for s in filename.split('.') if s.isdigit()].pop()
with open(os.path.join(dirname, filename), 'r') as content_file:
content = content_file.read()
with open('QualityIndicatorSummary.csv', 'a+') as of:
of.write(','.join([algorithm, problem, run_tag, 'Time', str(content)]))
of.write('\n')
if 'FUN' in filename:
solutions = read_solutions(os.path.join(dirname, filename))
run_tag = [s for s in filename.split('.') if s.isdigit()].pop()
for indicator in quality_indicators:
reference_front_file = os.path.join(reference_fronts, problem + '.pf')
# Add reference front if any
if hasattr(indicator, 'reference_front'):
if Path(reference_front_file).is_file():
indicator.reference_front = read_solutions(reference_front_file)
else:
LOGGER.warning('Reference front not found at', reference_front_file)
result = indicator.compute(solutions)
# Save quality indicator value to file
with open('QualityIndicatorSummary.csv', 'a+') as of:
of.write(','.join([algorithm, problem, run_tag, indicator.get_name(), str(result)]))
of.write('\n')
from jmetal.algorithm.multiobjective.moead import MOEADIEpsilon
from jmetal.lab.visualization import Plot, 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 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))
