def test_should_the_solution_change__if_the_probability_is_one(self):
operator = IntegerPolynomialMutation(1.0)
solution = IntegerSolution([-5, -5, -5], [5, 5, 5], 2)
solution.variables = [1, 2, 3]
mutated_solution = operator.execute(solution)
self.assertNotEqual([1, 2, 3], mutated_solution.variables)
self.assertEqual(
[True, True, True],
[isinstance(x, int) for x in mutated_solution.variables])
def test_should_the_solution_remain_unchanged_if_the_probability_is_zero(
self):
operator = IntegerPolynomialMutation(0.0)
solution = IntegerSolution(2, 1, [-5, -5, -5], [5, 5, 5])
solution.variables = [1, 2, 3]
mutated_solution = operator.execute(solution)
self.assertEqual([1, 2, 3], mutated_solution.variables)
self.assertEqual(
[True, True, True],
[isinstance(x, int) for x in mutated_solution.variables])
def configurar_algoritmo(self,
hora_show: datetime,
hora_minima: datetime = datetime.time(0, 0),
algoritmo: str = 'MOGA',
mutation_probability: float = 0.25,
max_evaluations: int = 500,
population: int = 100):
hora_minima, hora_show = self.restricciones_hora(
hora_minima, hora_show)
if (hora_minima == 0):
hora_minima = hora_show - 180
restricciones_baja = list([hora_minima, -100, -100, 0, 0])
restricciones_alta = list([hora_show, 0, 0, 100, 100])
self.problem = HVAC(lower_bound=restricciones_baja,
upper_bound=restricciones_alta,
number_of_configurations=3)
print("algoritmo: ", algoritmo)
if algoritmo == 'MOGA':
algorithm = MOGA(problem=self.problem,
population_size=population,
offspring_population_size=population,
mutation=IntegerPolynomialMutationD(
probability=mutation_probability,
distribution_index=20),
crossover=SBXCrossoverD(
probability=mutation_probability,
distribution_index=20),
termination_criterion=StoppingByEvaluations(
max=max_evaluations),
dominance_comparator=DominanceComparator())
elif algoritmo == "NSGAII":
algorithm = NSGAII(problem=self.problem,
population_size=population,
offspring_population_size=population,
mutation=IntegerPolynomialMutationD(
probability=mutation_probability,
distribution_index=20),
crossover=SBXCrossoverD(
probability=mutation_probability,
distribution_index=20),
termination_criterion=StoppingByEvaluations(
max=max_evaluations),
dominance_comparator=DominanceComparator())
elif algoritmo == 'OMOPSO':
algorithm = OMOPSO(problem=self.problem,
swarm_size=population,
epsilon=0.0075,
uniform_mutation=UniformMutation(
probability=mutation_probability,
perturbation=0.5),
non_uniform_mutation=NonUniformMutation(
probability=mutation_probability,
perturbation=0.5),
leaders=CrowdingDistanceArchive(100),
termination_criterion=StoppingByEvaluations(
max=max_evaluations))
elif algoritmo == 'SMPSO':
algorithm = SMPSO(problem=self.problem,
swarm_size=population,
mutation=IntegerPolynomialMutation(
probability=mutation_probability,
distribution_index=20),
leaders=CrowdingDistanceArchive(100),
termination_criterion=StoppingByEvaluations(
max=max_evaluations))
elif algoritmo == 'SPEA2':
algorithm = SPEA2(problem=self.problem,
population_size=population,
offspring_population_size=population,
mutation=IntegerPolynomialMutationD(
probability=mutation_probability,
distribution_index=20),
crossover=SBXCrossoverD(
probability=mutation_probability,
distribution_index=20),
termination_criterion=StoppingByEvaluations(
max=max_evaluations),
dominance_comparator=DominanceComparator())
else:
print("Algoritmo no válido. Creando MOGA por defecto...")
algorithm = MOGA(problem=self.problem,
population_size=population,
offspring_population_size=population,
mutation=IntegerPolynomialMutationD(
probability=mutation_probability,
distribution_index=20),
crossover=SBXCrossoverD(
probability=mutation_probability,
distribution_index=20),
termination_criterion=StoppingByEvaluations(
max=max_evaluations),
dominance_comparator=DominanceComparator())
self.algoritmo = algorithm
self.algoritmo.observable.register(observer=ProgressBarObserver(
max=max_evaluations))
return algorithm
def test_should_constructor_raise_an_exception_if_the_probability_is_lower_than_zero(
self):
with self.assertRaises(Exception):
IntegerPolynomialMutation(-12)
def test_should_constructor_create_a_valid_operator(self):
operator = IntegerPolynomialMutation(0.5, 20)
self.assertEqual(0.5, operator.probability)
self.assertEqual(20, operator.distribution_index)
def test_should_constructor_create_a_non_null_object(self):
operator = IntegerPolynomialMutation(1.0)
self.assertIsNotNone(operator)
def test_should_constructor_raises_an_exception_is_probability_is_higher_than_one(
self) -> None:
with self.assertRaises(Exception):
IntegerPolynomialMutation(1.01)
def test_should_constructor_raises_an_exception_is_probability_is_negative(
self) -> None:
with self.assertRaises(Exception):
IntegerPolynomialMutation(-1)
from extract_data import create, keep_trace1
import numpy as np
import statistics
solutions = []
problem = MOOC()
all2 = np.zeros([31, 5])
all3 = []
print(all2)
algorithm1 = NSGAIII(
problem=problem,
population_size=174,
#offspring_population_size=400,
reference_directions=UniformReferenceDirectionFactory(5, n_points=180),
mutation=IntegerPolynomialMutation(probability=1 /
problem.number_of_variables,
distribution_index=20),
crossover=IntegerSBXCrossover(probability=0.9, distribution_index=20),
termination_criterion=StoppingByEvaluations(max_evaluations=25000))
progress_bar = ProgressBarObserver(max=25000)
algorithm1.observable.register(progress_bar)
print("NSGAiii")
def transform():
for i in range(2):
print(i)
algorithm1.run()