def main() -> None:
problem = Kursawe()
algorithm = NSGAII[FloatSolution, List[FloatSolution]](
problem=problem,
population_size=100,
max_evaluations=25000,
mutation=Polynomial(1.0/problem.number_of_variables, distribution_index=20),
crossover=SBX(1.0, distribution_index=20),
selection = BinaryTournament(RankingAndCrowdingDistanceComparator()))
algorithm.run()
result = algorithm.get_result()
SolutionListOutput[FloatSolution].print_function_values_to_file("FUN."+problem.get_name(), result)
logger.info("Algorithm (continuous problem): " + algorithm.get_name())
logger.info("Problem: " + problem.get_name())
def main() -> None:
problem = Kursawe()
algorithm = SMPSO(problem=problem,
swarm_size=100,
max_evaluations=25000,
mutation=Polynomial(1.0 / problem.number_of_variables,
distribution_index=20),
leaders=CrowdingDistanceArchive(100))
algorithm.run()
result = algorithm.get_result()
SolutionListOutput[FloatSolution].print_function_values_to_file(
"FUN." + problem.get_name(), result)
logger.info("Algorithm (continuous problem): " + algorithm.get_name())
logger.info("Problem: " + problem.get_name())
def main() -> None:
problem = Kursawe()
algorithm = NSGAII[FloatSolution, List[FloatSolution]](
problem,
population_size=100,
max_evaluations=25000,
mutation=Polynomial(1.0 / problem.number_of_variables,
distribution_index=20),
crossover=SBX(1.0, distribution_index=20),
selection=BinaryTournamentSelection())
algorithm.run()
result = algorithm.get_result()
SolutionListOutput[FloatSolution].plot_scatter_to_file(result,
file_name="FUN." +
problem.get_name(),
output_format='eps',
dpi=200)
SolutionListOutput[FloatSolution].plot_scatter_to_screen(result)
logger.info("Algorithm (continuous problem): " + algorithm.get_name())
logger.info("Problem: " + problem.get_name())
def test_should_get_name_return_the_right_name(self):
problem = Kursawe()
self.assertEqual("Kursawe", problem.get_name())
def test_should_get_name_return_the_right_name(self):
problem = Kursawe()
self.assertEqual("Kursawe", problem.get_name())