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 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.solution_list import print_function_values_to_file, print_variables_to_file
from jmetal.util.solution_list import read_solutions, \
SparkEvaluator
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.nsgaii import NSGAII
from jmetal.operator import SBXCrossover, PolynomialMutation, BinaryTournamentSelection
from jmetal.problem import ZDT1, ZDT4
from jmetal.util.comparator import RankingAndCrowdingDistanceComparator, DominanceComparator
from jmetal.util.observer import ProgressBarObserver, VisualizerObserver
from jmetal.util.solution_list import read_solutions, print_function_values_to_file, print_variables_to_file
from jmetal.util.termination_criterion import StoppingByEvaluations
from jmetal.util.visualization import Plot, InteractivePlot
if __name__ == '__main__':
problem = ZDT4()
problem.reference_front = read_solutions(
filename='../../resources/reference_front/ZDT1.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),
selection=BinaryTournamentSelection(
comparator=RankingAndCrowdingDistanceComparator()),
termination_criterion=StoppingByEvaluations(max=max_evaluations),
dominance_comparator=DominanceComparator())
algorithm.observable.register(observer=ProgressBarObserver(
max=max_evaluations))
algorithm.observable.register(observer=VisualizerObserver(
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')