def configurar_OMOPSO(self):
algorithm = OMOPSO(
problem=self.problema,
swarm_size=self.maxima_poblacion,
epsilon=0.0075,
uniform_mutation=UniformMutation(probability=self.probabilidad, perturbation=0.5),
non_uniform_mutation=NonUniformMutation(probability=self.probabilidad, perturbation=0.5),
leaders=CrowdingDistanceArchive(100),
termination_criterion=StoppingByEvaluations(max_evaluations=self.evaluaciones))
return algorithm
if __name__ == '__main__':
problem = ZDT1Modified()
problem.reference_front = read_solutions(
filename='../../resources/reference_front/{}.pf'.format(
problem.get_name()))
mutation_probability = 1.0 / problem.number_of_variables
max_evaluations = 100
swarm_size = 10
algorithm = OMOPSO(
problem=problem,
swarm_size=swarm_size,
epsilon=0.0075,
uniform_mutation=UniformMutation(probability=mutation_probability,
perturbation=0.5),
non_uniform_mutation=NonUniformMutation(
mutation_probability,
perturbation=0.5,
max_iterations=max_evaluations / swarm_size),
leaders=CrowdingDistanceArchive(10),
termination_criterion=StoppingByEvaluations(max=max_evaluations),
swarm_evaluator=SparkEvaluator(),
)
algorithm.run()
front = algorithm.get_result()
# Save results to file
print_function_values_to_file(
front, 'FUN.' + algorithm.get_name() + "." + problem.get_name())
print_variables_to_file(
front, 'VAR.' + algorithm.get_name() + "." + problem.get_name())
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
from jmetal.util.solution import read_solutions
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == '__main__':
problem = ZDT1()
problem.reference_front = read_solutions(filename='resources/reference_front/ZDT1.pf')
mutation_probability = 1.0 / problem.number_of_variables
max_evaluations = 25000
swarm_size = 100
algorithm = OMOPSO(
problem=problem,
swarm_size=swarm_size,
epsilon=0.0075,
uniform_mutation=UniformMutation(probability=mutation_probability, perturbation=0.5),
non_uniform_mutation=NonUniformMutation(mutation_probability, perturbation=0.5,
max_iterations=int(max_evaluations / swarm_size)),
leaders=CrowdingDistanceArchive(100),
termination_criterion=StoppingByEvaluations(max=max_evaluations)
)
algorithm.run()
front = algorithm.get_result()
# Save results to file
print_function_values_to_file(front, 'FUN.' + algorithm.label)
print_variables_to_file(front, 'VAR.'+ algorithm.label)
print(f'Algorithm: ${algorithm.get_name()}')
print(f'Problem: ${problem.get_name()}')
print(f'Computing time: ${algorithm.total_computing_time}')
from jmetal.util.solutions import read_solutions
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == '__main__':
problem = ZDT1()
problem.reference_front = read_solutions(filename='resources/reference_front/ZDT1.pf')
mutation_probability = 1.0 / problem.number_of_variables
max_evaluations = 25000
swarm_size = 100
algorithm = OMOPSO(
problem=problem,
swarm_size=swarm_size,
epsilon=0.0075,
uniform_mutation=UniformMutation(probability=mutation_probability, perturbation=0.5),
non_uniform_mutation=NonUniformMutation(mutation_probability, perturbation=0.5,
max_iterations=int(max_evaluations / swarm_size)),
leaders=CrowdingDistanceArchive(100),
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, filename=algorithm.get_name())