if __name__ == "__main__":
problem = DTLZ2()
problem.reference_front = read_solutions(
filename="resources/reference_front/DTLZ2.3D.pf")
max_evaluations = 25000
algorithm = NSGAIII(
problem=problem,
population_size=92,
reference_directions=UniformReferenceDirectionFactory(3, n_points=91),
mutation=PolynomialMutation(probability=1.0 /
problem.number_of_variables,
distribution_index=20),
crossover=SBXCrossover(probability=1.0, distribution_index=30),
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations),
)
algorithm.run()
front = get_non_dominated_solutions(algorithm.get_result())
# Save results to file
print_function_values_to_file(front, "FUN." + algorithm.label)
print_variables_to_file(front, "VAR." + algorithm.label)
print(f"Algorithm: {algorithm.get_name()}")
print(f"Problem: {problem.get_name()}")
print(f"Computing time: {algorithm.total_computing_time}")
problem=problem,
population_size=Configuration.population,
reference_directions=UniformReferenceDirectionFactory(
Configuration.goal_num, n_points=Configuration.population - 1),
# offspring_population_size = Configuration.population,
mutation=PolynomialMutation(probability=1.0 /
problem.number_of_variables,
distribution_index=20),
crossover=SBXCrossover(probability=1.0, distribution_index=20),
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations)
# termination_criterion = StoppingByQualityIndicator(quality_indicator=HyperVolume, expected_value=1,
# degree=0.9)
# selection = BinaryTournamentSelection()
)
"""==========================调用算法模板进行种群进化========================="""
progress_bar = ProgressBarObserver(max=max_evaluations)
algorithm.observable.register(progress_bar)
algorithm.run()
front = algorithm.get_result()
"""==================================输出结果=============================="""
# Save results to file
print_function_values_to_file(
front, os.path.join(target_dir, '/FUN.' + algorithm.label))
print_variables_to_file(
front, os.path.join(target_dir, '/VAR.' + algorithm.label))
print(f'Algorithm: ${algorithm.get_name()}')
print(f'Problem: ${problem.get_name()}')
print(f'Computing time: ${algorithm.total_computing_time}')
algorithm = NSGAIII(
problem=problem,
population_size=92,
reference_directions=UniformReferenceDirectionFactory(3,
n_points=91),
mutation=PolynomialMutation(probability=1.0 /
problem.number_of_variables,
distribution_index=20),
crossover=SBXCrossover(probability=1.0, distribution_index=30),
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations))
progress_bar = ProgressBarObserver(max=max_evaluations)
algorithm.observable.register(progress_bar)
algorithm.run()
bag += algorithm.get_result()
print(len(bag))
print_solutions_to_file(
bag, DIRECTORY_RESULTS + 'Solutions.bag.' + algorithm.label)
ranking = FastNonDominatedRanking()
ranking.compute_ranking(bag)
front = ranking.get_subfront(0)
print(len(front))
# Save results to file
print_solutions_to_file(front,
DIRECTORY_RESULTS + 'front0.' + algorithm.label)
plot_front = Plot(title='Pareto front approximation',
axis_labels=['x', 'y', 'z'])
plot_front.plot(front,
label='NSGAII-ZDT7',
