def __init__(
self,
problem: Problem,
population_size: int,
offspring_population_size: int,
mutation: Mutation,
crossover: Crossover,
termination_criterion: TerminationCriterion = store.default_termination_criteria,
population_generator: Generator = store.default_generator,
population_evaluator: Evaluator = store.default_evaluator,
dominance_comparator: Comparator = store.default_comparator,
):
"""
:param problem: The problem to solve.
:param population_size: Size of the population.
:param mutation: Mutation operator (see :py:mod:`jmetal.operator.mutation`).
:param crossover: Crossover operator (see :py:mod:`jmetal.operator.crossover`).
"""
multi_comparator = MultiComparator(
[StrengthRanking.get_comparator(), KNearestNeighborDensityEstimator.get_comparator()]
)
selection = BinaryTournamentSelection(comparator=multi_comparator)
super(SPEA2, self).__init__(
problem=problem,
population_size=population_size,
offspring_population_size=offspring_population_size,
mutation=mutation,
crossover=crossover,
selection=selection,
termination_criterion=termination_criterion,
population_evaluator=population_evaluator,
population_generator=population_generator,
)
self.dominance_comparator = dominance_comparator
def test_should_replacement_return_the_right_value_case1(self):
"""
5 1
4 2
3 3
2
1 4
0 1 2 3 4 5
List: 1,2,3 OffspringList: 4
Expected result: 4, 1, 3
"""
ranking = StrengthRanking()
density_estimator = KNearestNeighborDensityEstimator(1)
replacement = RankingAndDensityEstimatorReplacement(ranking, density_estimator)
solution1 = Solution(2, 2)
solution1.objectives = [1, 5]
solution2 = Solution(2, 2)
solution2.objectives = [2, 4]
solution3 = Solution(2, 2)
solution3.objectives = [3, 3]
solution4 = Solution(2, 2)
solution4.objectives = [5, 1]
solution_list = [solution1, solution2, solution3]
offspring_list = [solution4]
result_list = replacement.replace(solution_list, offspring_list)
self.assertEqual(3, len(result_list))
self.assertTrue(solution1 in result_list)
self.assertTrue(solution3 in result_list)
self.assertTrue(solution4 in result_list)
def test_should_replacement_return_the_list_if_the_offspring_list_is_empty(self):
"""
5 1
4 2
3 3
2
1 4
0 1 2 3 4 5
"""
ranking = StrengthRanking()
density_estimator = KNearestNeighborDensityEstimator(1)
replacement = RankingAndDensityEstimatorReplacement(ranking, density_estimator)
solution1 = Solution(2, 2)
solution1.objectives = [1, 5]
solution2 = Solution(2, 2)
solution2.objectives = [2, 4]
solution3 = Solution(2, 2)
solution3.objectives = [3, 3]
solution4 = Solution(2, 2)
solution4.objectives = [5, 1]
solution_list = [solution1, solution2, solution3, solution4]
result_list = replacement.replace(solution_list, [])
self.assertEqual(4, len(result_list))
self.assertEqual(0, solution1.attributes["strength_ranking"])
self.assertEqual(0, solution2.attributes["strength_ranking"])
self.assertEqual(0, solution3.attributes["strength_ranking"])
self.assertEqual(0, solution4.attributes["strength_ranking"])
def replacement(self, population: List[S],
offspring_population: List[S]) -> List[List[S]]:
ranking = StrengthRanking(self.dominance_comparator)
density_estimator = KNearestNeighborDensityEstimator()
r = RankingAndDensityEstimatorReplacement(ranking, density_estimator,
RemovalPolicyType.SEQUENTIAL)
solutions = r.replace(population, offspring_population)
front = get_non_dominated_solutions(solutions)
objective1 = -1 / self.Uobjecive1 * np.array(
[solution.objectives[0] for solution in front])
objective2 = -1 / self.Uobjecive2 * np.array(
[solution.objectives[1] for solution in front])
HV = calculateHypervolume(list(zip(objective1, objective2)))
print('Hypervolume;', HV)
self.hypervolumeByGeneration.append(HV)
# DO IGD calculation here
IGD = InvertedGenerationalDistance(self.reference_front)
igd = IGD.compute(
list(
zip(-np.array([solution.objectives[0] for solution in front]),
-np.array([solution.objectives[1]
for solution in front]))))
print('IGD1:', igd)
# obj1front=1/self.Uobjecive1*self.reference_front[:, 0]
# obj2front=1/self.Uobjecive2*self.reference_front[:,1]
# igd2 = calculateIGD(list(zip(objective1,objective2)), list(zip(obj1front,obj2front)))
# print('IGD2:',igd2)
self.IGDbyGeneration.append(igd)
return solutions
def test_should_replacement_return_the_right_value_case3(self):
""""""
points_population = [
[0.13436424411240122, 4.323216008886963],
[0.23308445025757263, 4.574937990387161],
[0.17300740157905092, 4.82329350808847],
[0.9571162814602269, 3.443495331489301],
[0.25529404008730594, 3.36387501100745],
[0.020818108509287336, 5.1051826661880515],
[0.8787178982088466, 3.2716009445324103],
[0.6744550697237632, 3.901350307095427],
[0.7881164487252263, 3.1796004913916516],
[0.1028341459863098, 4.9409270526888935],
]
points_offspring_population = [
[0.3150521745650882, 4.369120371847888],
[0.8967291504209932, 2.506948771242972],
[0.6744550697237632, 3.9361442668874504],
[0.9571162814602269, 3.4388386707431433],
[0.13436424411240122, 4.741872175943253],
[0.25529404008730594, 2.922302861104415],
[0.23308445025757263, 4.580180404770213],
[0.23308445025757263, 4.591260299892424],
[0.9571162814602269, 2.9865495383518694],
[0.25529404008730594, 3.875587748122183],
]
ranking = FastNonDominatedRanking()
density_estimator = KNearestNeighborDensityEstimator(1)
population = []
for i in range(len(points_population)):
population.append(Solution(2, 2))
population[i].objectives = points_population[i]
offspring_population = []
for i in range(len(points_offspring_population)):
offspring_population.append(Solution(2, 2))
offspring_population[i].objectives = points_offspring_population[i]
replacement = RankingAndDensityEstimatorReplacement(ranking, density_estimator)
result_list = replacement.replace(population, offspring_population)
self.assertEqual(10, len(result_list))
for solution in result_list[0:4]:
self.assertEqual(0, solution.attributes["dominance_ranking"])
for solution in result_list[5:9]:
self.assertEqual(1, solution.attributes["dominance_ranking"])
def replacement(self, population: List[S], offspring_population: List[S]) -> List[List[S]]:
"""This method joins the current and offspring populations to produce the population of the next generation
by applying the ranking and crowding distance selection.
:param population: Parent population.
:param offspring_population: Offspring population.
:return: New population after ranking and crowding distance selection is applied.
"""
ranking = StrengthRanking(self.dominance_comparator)
density_estimator = KNearestNeighborDensityEstimator()
r = RankingAndDensityEstimatorReplacement(ranking, density_estimator, RemovalPolicyType.SEQUENTIAL)
solutions = r.replace(population, offspring_population)
return solutions
def test_should_replacement_return_the_right_value_case2(self):
"""
5 1
4 2
3 3
2 5
1 4
0 1 2 3 4 5
List: 1,2,4 OffspringList: 3,5
Expected result: 1, 5, 4
"""
ranking = StrengthRanking()
density_estimator = KNearestNeighborDensityEstimator(1)
replacement = RankingAndDensityEstimatorReplacement(ranking, density_estimator)
solution1 = Solution(2, 2)
solution1.objectives = [1, 5]
solution2 = Solution(2, 2)
solution2.objectives = [2, 4]
solution3 = Solution(2, 2)
solution3.objectives = [3, 3]
solution4 = Solution(2, 2)
solution4.objectives = [5, 1]
solution5 = Solution(2, 2)
solution5.objectives = [2.5, 2.5]
solution_list = [solution1, solution2, solution4]
offspring_list = [solution3, solution5]
result_list = replacement.replace(solution_list, offspring_list)
self.assertEqual(0, solution1.attributes["strength_ranking"])
self.assertEqual(0, solution2.attributes["strength_ranking"])
self.assertEqual(1, solution3.attributes["strength_ranking"])
self.assertEqual(0, solution4.attributes["strength_ranking"])
self.assertEqual(0, solution5.attributes["strength_ranking"])
self.assertEqual(3, len(result_list))
self.assertTrue(solution1 in result_list)
self.assertTrue(solution5 in result_list)
self.assertTrue(solution4 in result_list)
def setUp(self):
self.knn = KNearestNeighborDensityEstimator()
class KNearestNeighborDensityEstimatorTest(unittest.TestCase):
def setUp(self):
self.knn = KNearestNeighborDensityEstimator()
def test_should_the_density_estimator_compute_the_right_distances_case1(
self):
"""
5 1
4 2
3 3
2
1 4
0 1 2 3 4 5
"""
solution1 = Solution(2, 2)
solution1.objectives = [1, 5]
solution2 = Solution(2, 2)
solution2.objectives = [2, 4]
solution3 = Solution(2, 2)
solution3.objectives = [3, 3]
solution4 = Solution(2, 2)
solution4.objectives = [5, 1]
solution_list = [solution1, solution2, solution3, solution4]
self.knn.compute_density_estimator(solution_list)
self.assertEqual(sqrt(2), solution1.attributes["knn_density"])
self.assertEqual(sqrt(2), solution2.attributes["knn_density"])
self.assertEqual(sqrt(2), solution3.attributes["knn_density"])
self.assertEqual(sqrt(2 * 2 + 2 * 2),
solution4.attributes["knn_density"])
# self.knn.sort(solution_list)
def test_should_the_density_estimator_sort_the_solution_list(self):
"""
5 1
4 2
3 3
2 5
1 4
0 1 2 3 4 5
List: 1,2,3,4,5
Expected result: 4, 1, 2, 5, 3
"""
solution1 = Solution(2, 2)
solution1.objectives = [1, 5]
solution2 = Solution(2, 2)
solution2.objectives = [2, 4]
solution3 = Solution(2, 2)
solution3.objectives = [3, 3]
solution4 = Solution(2, 2)
solution4.objectives = [5, 1]
solution5 = Solution(2, 2)
solution5.objectives = [3, 2]
solution_list = [solution1, solution2, solution3, solution4, solution5]
self.knn.compute_density_estimator(solution_list)
self.knn.sort(solution_list)
self.assertEqual(solution_list[0], solution4)
self.assertEqual(solution_list[1], solution1)
self.assertEqual(solution_list[2], solution2)
self.assertEqual(solution_list[3], solution5)
def test_should_the_density_estimator_sort_the_solution_list_considering_the_draws(
self):
"""
5 1
4 2
3 3
2
1 4
0 1 2 3 4 5
Expected result after sort: 4, 3, 1, 2
"""
solution1 = Solution(2, 2)
solution1.objectives = [1, 5]
solution2 = Solution(2, 2)
solution2.objectives = [2, 4]
solution3 = Solution(2, 2)
solution3.objectives = [3, 3]
solution4 = Solution(2, 2)
solution4.objectives = [5, 1]
solution_list = [solution1, solution2, solution3, solution4]
self.knn.compute_density_estimator(solution_list)
self.knn.sort(solution_list)
self.assertEqual(solution_list[0], solution4)
self.assertEqual(solution_list[1], solution3)
self.assertEqual(solution_list[2], solution1)
self.assertEqual(solution_list[3], solution2)
def test_should_the_density_estimator_sort_the_solution_list_considering_the_draws_case2(
self):
"""
0.13436424411240122 4.323216008886963
0.020818108509287336 5.1051826661880515
0.1028341459863098 4.9409270526888935
0.8967291504209932 2.506948771242972
0.25529404008730594 2.922302861104415
"""
points = [
[0.13436424411240122, 4.323216008886963],
[0.020818108509287336, 5.1051826661880515],
[0.1028341459863098, 4.9409270526888935],
[0.8967291504209932, 2.506948771242972],
[0.25529404008730594, 2.922302861104415],
]
population = []
for i in range(len(points)):
population.append(Solution(2, 2))
population[i].objectives = points[i]
self.knn.compute_density_estimator(population)
self.knn.sort(population)
self.assertEqual(5, len(population))
self.assertEqual([0.1028341459863098, 4.9409270526888935],
population[4].objectives)