instance='BB20019',
path='../resources',
score_list=[SumOfPairs(),
PercentageOfTotallyConservedColumns()])
# creates the algorithm
max_evaluations = 25000
reference_point = [-0.6, 11000]
algorithm = NSGAII(
problem=problem,
population_size=100,
offspring_population_size=100,
mutation=ShiftClosedGapGroups(probability=0.3),
crossover=SPXMSA(probability=0.7),
dominance_comparator=GDominanceComparator(reference_point),
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations))
algorithm.observable.register(observer=ProgressBarObserver(
max=max_evaluations))
algorithm.observable.register(observer=PlotFrontToFileObserver(
output_directory='fronts_zdt1'))
algorithm.run()
front = algorithm.get_result()
# plot front
plot_front = Plot(title='Pareto front approximation',
axis_labels=['%SOP', '%TC'])
plot_front.plot(front, label='NSGAII-BB20019', filename='NSGAII-BB20019')
if __name__ == '__main__':
problem = ZDT2()
problem.reference_front = read_solutions(
filename='../../resources/reference_front/{}.pf'.format(
problem.get_name()))
max_evaluations = 25000
algorithm = GDE3(
problem=problem,
population_size=100,
cr=0.5,
f=0.5,
termination_criterion=StoppingByEvaluations(max=max_evaluations),
dominance_comparator=GDominanceComparator([0.5, 0.5]))
algorithm.observable.register(observer=ProgressBarObserver(
max=max_evaluations))
algorithm.observable.register(observer=VisualizerObserver(
reference_point=([0.5, 0.5])))
algorithm.run()
front = algorithm.get_result()
# Save results to file
print_function_values_to_file(
front, 'FUN.' + algorithm.get_name() + "." + problem.get_name())
print_variables_to_file(
front, 'VAR.' + algorithm.get_name() + "." + problem.get_name())