def setUp(self) -> None:
problem1 = OneMax(number_of_bits=512)
self.emas1 = Emas(
problem=problem1,
initial_population_size=1000,
initial_inidividual_energy=10,
reproduction_threshold=20,
energy_exchange_operator=FractionEnergyExchange(0.5),
death_operator=ThresholdDeath(threshold=5, neighbours_operator=RandomNeighbours()),
termination_criterion=StoppingByEvaluations(max_evaluations=100000),
neighbours_operator=RandomNeighbours(),
reproduction_operator=FractionEnergyReproduction(0.5, BitFlipMutation(0.5), SPXCrossover(0.5))
)
problem2 = Sphere(number_of_variables=10)
self.emas2 = Emas(
problem=problem2,
initial_population_size=1000,
initial_inidividual_energy=10,
reproduction_threshold=20,
energy_exchange_operator=FractionEnergyExchange(0.5),
death_operator=ThresholdDeath(threshold=5, neighbours_operator=RandomNeighbours()),
termination_criterion=StoppingByEvaluations(max_evaluations=50000),
neighbours_operator=RandomNeighbours(),
reproduction_operator=FractionEnergyReproduction(0.5, PolynomialMutation(0.5), SBXCrossover(0.5))
)
from jmetal.algorithm.singleobjective.simulated_annealing import SimulatedAnnealing
from jmetal.operator import BitFlipMutation
from jmetal.problem import OneMax
from jmetal.util.solution import print_function_values_to_file, print_variables_to_file
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == '__main__':
problem = OneMax(number_of_bits=1024)
max_evaluations = 20000
algorithm = SimulatedAnnealing(
problem=problem,
mutation=BitFlipMutation(probability=1.0 / problem.number_of_bits),
termination_criterion=StoppingByEvaluations(max=max_evaluations))
algorithm.run()
result = algorithm.get_result()
# Save results to file
print_function_values_to_file(
result, 'FUN.' + algorithm.get_name() + "." + problem.get_name())
print_variables_to_file(
result, 'VAR.' + algorithm.get_name() + "." + problem.get_name())
print('Algorithm: ' + algorithm.get_name())
print('Problem: ' + problem.get_name())
print('Solution: ' + result.get_binary_string())
print('Fitness: ' + str(result.objectives[0]))
print('Computing time: ' + str(algorithm.total_computing_time))
from jmetal.algorithm.singleobjective.simulated_annealing import SimulatedAnnealing
from jmetal.operator import BitFlipMutation
from jmetal.problem import OneMax
from jmetal.util.observer import PrintObjectivesObserver
from jmetal.util.solution_list import print_function_values_to_file, print_variables_to_file
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == '__main__':
problem = OneMax(number_of_bits=512)
max_evaluations = 20000
algorithm = SimulatedAnnealing(
problem=problem,
mutation=BitFlipMutation(probability=1.0 / problem.number_of_bits),
termination_criterion=StoppingByEvaluations(max=max_evaluations))
objectives_observer = PrintObjectivesObserver(frequency=1000)
algorithm.observable.register(observer=objectives_observer)
algorithm.run()
result = algorithm.get_result()
# Save results to file
print_function_values_to_file(
result, 'FUN.' + algorithm.get_name() + "." + problem.get_name())
print_variables_to_file(
result, 'VAR.' + algorithm.get_name() + "." + problem.get_name())
print('Algorithm: ' + algorithm.get_name())
print('Problem: ' + problem.get_name())
print('Solution: ' + result.get_binary_string())
from jmetal.algorithm.multiobjective.nsgaii import NSGAII
from jmetal.operator import BitFlipMutation, SPXCrossover
from jmetal.problem import OneMax
from jmetal.util.comparator import DominanceComparator
from jmetal.util.observer import PrintObjectivesObserver
from jmetal.util.solutions_utils import print_function_values_to_file, print_variables_to_file
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == '__main__':
binary_string_length = 512
problem = OneMax(binary_string_length)
max_evaluations = 20000
algorithm = NSGAII(
problem=problem,
population_size=100,
offspring_population_size=1,
mutation=BitFlipMutation(probability=1.0 / binary_string_length),
crossover=SPXCrossover(probability=1.0),
termination_criterion=StoppingByEvaluations(max=max_evaluations),
dominance_comparator=DominanceComparator()
)
algorithm.observable.register(observer=PrintObjectivesObserver(1000))
algorithm.run()
front = algorithm.get_result()
# Save results to file
print_function_values_to_file(front, 'FUN.'+ algorithm.get_name()+"-"+problem.get_name())