def main() -> None:
problem = ZDT1()
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=BinaryTournamentSelection(RankingAndCrowdingDistanceComparator()))
selection=BinaryTournament2Selection([
SolutionAttributeComparator("dominance_ranking"),
SolutionAttributeComparator("crowding_distance",
lowest_is_best=False)
]))
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())
logger.info("Computing time: " + str(algorithm.total_computing_time))
def test_should_constructor_create_a_valid_problem_with_default_settings(self) -> None:
problem = ZDT1()
self.assertEqual(30, problem.number_of_variables)
self.assertEqual(2, problem.number_of_objectives)
self.assertEqual(0, problem.number_of_constraints)
self.assertEqual(30 * [0.0], problem.lower_bound)
self.assertEqual(30 * [1.0], problem.upper_bound)
def test_should_create_solution_create_a_valid_float_solution(self) -> None:
problem = ZDT1()
solution = problem.create_solution()
self.assertEqual(30, solution.number_of_variables)
self.assertEqual(30, len(solution.variables))
self.assertEqual(2, solution.number_of_objectives)
self.assertEqual(2, len(solution.objectives))
self.assertEqual(0, problem.number_of_constraints)
self.assertEqual(30 * [0.0], problem.lower_bound)
self.assertEqual(30 * [1.0], problem.upper_bound)
self.assertTrue(all(value >= 0.0 for value in solution.variables))
self.assertTrue(all(value <= 1.0 for value in solution.variables))
def main() -> None:
problem = ZDT1()
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=BinaryTournament(RankingAndCrowdingDistanceComparator()))
selection = BinaryTournament2([SolutionAttributeComparator("dominance_ranking"),
SolutionAttributeComparator("crowding_distance", lowest_is_best=False)]))
observer = AlgorithmObserver(animation_speed=1*10e-8)
algorithm.observable.register(observer=observer)
algorithm.run()
logger.info("Algorithm (continuous problem): " + algorithm.get_name())
logger.info("Problem: " + problem.get_name())
def main() -> None:
problem = ZDT1()
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 = BinaryTournament(RankingAndCrowdingDistanceComparator()))
observer = BasicAlgorithmConsumer(1000)
algorithm.observable.register(observer=observer)
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())
logger.info("Computing time: " + str(algorithm.total_computing_time))
def test_should_constructor_create_a_non_null_object(self) -> None:
problem = ZDT1()
self.assertIsNotNone(problem)
def test_should_get_name_return_the_right_name(self):
problem = ZDT1()
self.assertEqual("ZDT1", problem.get_name())
# Solving SCH Problem using NSGAII
from jmetal.algorithm.multiobjective.nsgaii import NSGAII
from jmetal.operator import SBXCrossover, PolynomialMutation
from jmetal.util.termination_criterion import StoppingByEvaluations
from jmetal.util.observer import ProgressBarObserver, VisualizerObserver
from jmetal.lab.visualization import Plot, InteractivePlot
from jmetal.problem.multiobjective.zdt import ZDT1
if __name__ == '__main__':
problem = ZDT1()
max_evaluations = 20000
algorithm = NSGAII(
problem=problem,
population_size=100,
offspring_population_size=100,
mutation=PolynomialMutation(probability=0.05, distribution_index=20),
crossover=SBXCrossover(probability=1.0, distribution_index=20),
termination_criterion=StoppingByEvaluations(max=max_evaluations))
algorithm.observable.register(observer=ProgressBarObserver(
max=max_evaluations))
algorithm.observable.register(observer=VisualizerObserver(
reference_front=problem.reference_front))
algorithm.run()
front = algorithm.get_result()
# Plot front
plot_front = Plot(plot_title='Pareto front approximation',
reference_front=problem.reference_front,
axis_labels=problem.obj_labels)
plot_front.plot(front,
label=algorithm.label,