def test_should_add_work_properly_case7(self):
""" Case 7: add a non-dominated solution when the archive is full should remove all the dominated solutions.
"""
archive = CrowdingDistanceArchive(4)
solution1 = Solution(2, 2)
solution1.objectives = [0.0, 3.0]
solution2 = Solution(2, 2)
solution2.objectives = [1.0, 2.0]
solution3 = Solution(2, 2)
solution3.objectives = [2.0, 1.5]
solution4 = Solution(2, 2)
solution4.objectives = [3.0, 0.0]
new_solution = Solution(2, 2)
new_solution.objectives = [-1.0, -1.0]
archive.add(solution1)
archive.add(solution2)
archive.add(solution3)
archive.add(solution4)
archive.add(new_solution)
self.assertEqual(1, archive.size())
self.assertTrue(new_solution in archive.solution_list)
def test_should_compute_density_estimator_work_properly_case3(self):
""" Case 3: The archive contains two solutions.
"""
archive = CrowdingDistanceArchive(4)
solution1 = Solution(2, 2)
solution1.objectives = [0.0, 3.0]
solution2 = Solution(2, 2)
solution2.objectives = [1.0, 2.0]
solution3 = Solution(2, 2)
solution3.objectives = [2.0, 1.5]
archive.add(solution1)
archive.add(solution2)
archive.add(solution3)
archive.compute_density_estimator()
self.assertEqual(3, archive.size())
self.assertEqual(float("inf"),
solution1.attributes["crowding_distance"])
self.assertEqual(float("inf"),
solution3.attributes["crowding_distance"])
self.assertTrue(
solution2.attributes["crowding_distance"] < float("inf"))
def test_should_add_work_properly_case6(self):
""" Case 6: add a non-dominated solution when the archive is full should not include
the solution if it has the highest distance crowding value.
"""
archive = CrowdingDistanceArchive(4)
solution1 = Solution(2, 2)
solution1.objectives = [0.0, 3.0]
solution2 = Solution(2, 2)
solution2.objectives = [1.0, 2.0]
solution3 = Solution(2, 2)
solution3.objectives = [2.0, 1.5]
solution4 = Solution(2, 2)
solution4.objectives = [3.0, 0.0]
new_solution = Solution(2, 2)
new_solution.objectives = [1.1, 1.9]
archive.add(solution1)
archive.add(solution2)
archive.add(solution3)
archive.add(solution4)
archive.add(new_solution)
self.assertEqual(4, archive.size())
self.assertTrue(new_solution not in archive.solution_list)
def test_should_compute_density_estimator_work_properly_case1(self):
""" Case 1: The archive contains one solution.
"""
archive = CrowdingDistanceArchive(4)
solution1 = Solution(2, 2)
solution1.objectives = [0.0, 3.0]
archive.add(solution1)
archive.compute_density_estimator()
self.assertEqual(1, archive.size())
self.assertEqual(float("inf"),
solution1.attributes["crowding_distance"])
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())
from jmetal.component.archive import CrowdingDistanceArchive
from jmetal.problem import ZDT1
from jmetal.operator import Polynomial
from jmetal.util.graphic import ScatterMatplotlib
from jmetal.util.solution_list_output import SolutionList
if __name__ == '__main__':
problem = ZDT1()
algorithm = SMPSO(problem=problem,
swarm_size=100,
max_evaluations=25000,
mutation=Polynomial(probability=1.0 /
problem.number_of_variables,
distribution_index=20),
leaders=CrowdingDistanceArchive(100))
observer = VisualizerObserver(problem)
progress_bar = ProgressBarObserver(step=100, maximum=25000)
algorithm.observable.register(observer=observer)
algorithm.observable.register(observer=progress_bar)
algorithm.run()
front = algorithm.get_result()
# Plot frontier to file
pareto_front = ScatterMatplotlib(
plot_title='SMPSO for ' + problem.get_name(),
number_of_objectives=problem.number_of_objectives)
pareto_front.plot(front,
reference=problem.get_reference_front(),
algorithm = [(NSGAII, {
'population_size':
100,
'max_evaluations':
25000,
'mutation':
NullMutation(),
'crossover':
SBX(1.0, 20),
'selection':
BinaryTournamentSelection(RankingAndCrowdingDistanceComparator())
}),
(NSGAII(population_size=100,
max_evaluations=25000,
mutation=NullMutation(),
crossover=SBX(1.0, 20),
selection=BinaryTournamentSelection(
RankingAndCrowdingDistanceComparator()),
problem=ZDT1()), {}),
(SMPSO, {
'swarm_size': 100,
'max_evaluations': 25000,
'mutation': NullMutation(),
'leaders': CrowdingDistanceArchive(100)
})]
metric = [HyperVolume(reference_point=[1, 1])]
problem = [(ZDT1, {}), (ZDT2, {})]
results = experiment(algorithm, metric, problem)
display(results)