mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables,
distribution_index=20),
leaders=CrowdingDistanceArchive(100),
termination_criterion=StoppingByEvaluations(max=max_evaluations)
),
algorithm_tag='SMPSO',
problem_tag=problem_tag,
run=run,
)
)
return jobs
if __name__ == '__main__':
# Configure the experiments
jobs = configure_experiment(problems={'ZDT1': ZDT1(), 'ZDT2': ZDT2(), 'ZDT3': ZDT3()}, n_run=31)
# Run the study
output_directory = 'data'
experiment = Experiment(output_dir=output_directory, jobs=jobs)
experiment.run()
# Generate summary file
generate_summary_from_experiment(
input_dir=output_directory,
reference_fronts='/home/user/jMetalPy/resources/reference_front',
quality_indicators=[GenerationalDistance(), EpsilonIndicator(), HyperVolume([1.0, 1.0])]
)
for r, _, fl in os.walk(output_directory):
for file in fl:
if 'FUN' in file and prb in r:
with open(os.path.join(r, file), 'r') as f:
line = f.readline()
while line:
tokens = line.split()
fitness = [float(x) for x in tokens]
s = Solution(None, fitness)
population.append(s)
line = f.readline()
population = non_dominated_population(population,
maximize=False,
filter_duplicate=False)
# save to file
pf_file = os.path.dirname(output_directory).join(prb + ".pf")
with open(pf_file, 'w') as f:
for s in population:
f.write("".join([str(v) + "\t" for v in s.fitness]))
f.write("\n")
# Generate summary file
generate_summary_from_experiment(
input_dir=output_directory,
quality_indicators=[
GenerationalDistance(reference_front=output_directory),
EpsilonIndicator(reference_front=output_directory),
HyperVolume([0, 0])
])
termination_criterion=StoppingByEvaluationsCustom(max_evaluations=max_evaluations,
reference_point=REFERENCE_POINT,
AlgorithmName='SMPSO')
),
algorithm_tag='SMPSO',
problem_tag=problem_tag,
run=run,
)
)
return jobs
if __name__ == '__main__':
# Configure the experiments
jobs = configure_experiment(problems={'OTN': problemOTN}, n_run=TIMES_TO_RUN)
# Run the study
output_directory = 'data'
experiment = Experiment(output_dir=output_directory, jobs=jobs)
experiment.run()
Filter.RemovePenalty(output_directory)
# Reference fronts is the folder where is the reference to be compared with.
generate_summary_from_experiment(
input_dir=output_directory,
reference_fronts='C:\\Users\\aryss\\Documents\\Repositories\\OTN_Mastering\\Output\\CT3\\8 services',
quality_indicators=[InvertedGenerationalDistance(), EpsilonIndicator(), HyperVolume(REFERENCE_POINT)]
#quality_indicators = [HyperVolume(REFERENCE_POINT)]
)