def update(self, *args, **kwargs):
problem = kwargs['PROBLEM']
solutions = kwargs['SOLUTIONS']
if solutions:
if isinstance(problem, DynamicProblem):
termination_criterion_is_met = kwargs.get('TERMINATION_CRITERIA_IS_MET', None)
if termination_criterion_is_met:
print_function_values_to_file(solutions, '{}/FUN.{}'.format(self.directory, self.counter))
self.counter += 1
else:
print_function_values_to_file(solutions, '{}/FUN.{}'.format(self.directory, self.counter))
self.counter += 1
process, final = Experiment(smrseeds)
front = get_non_dominated_solutions(final) #process[-1]
process.append(front)
smrprocessruns.append(process)
reference += front
reference_front = get_non_dominated_solutions(reference)
plot_front = Plot(plot_title='Pareto front approximation',
axis_labels=['TRT', 'TDC'],
reference_front=None)
plot_front.plot(reference_front, label=' ', filename=fm, format='png')
# Save results to file
print_function_values_to_file(
reference_front, 'FUN.' + algorithm.get_name() + "-" + problem.get_name())
print_variables_to_file(
reference_front, 'VAR.' + algorithm.get_name() + "-" + problem.get_name())
# Normalization
timelist = []
costlist = []
for s in reference_front:
timelist.append(s.objectives[0])
costlist.append(s.objectives[1])
mintime = min(timelist)
maxtime = max(timelist)
mincost = min(costlist)
maxcost = max(costlist)
print("Time:", mintime, maxtime)
print("Cost:", mincost, maxcost)
if __name__ == '__main__':
problem = Rastrigin(10)
max_evaluations = 250000
algorithm = SimulatedAnnealing(
problem=problem,
mutation=PolynomialMutation(probability=1.0 /
problem.number_of_variables,
distribution_index=20.0),
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: ' + str(result.variables[0]))
print('Fitness: ' + str(result.objectives[0]))
print('Computing time: ' + str(algorithm.total_computing_time))
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())
# Plot interactive front
plot_front = InteractivePlot(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())
# Save results to file
print_function_values_to_file(front, 'FUN.' + algorithm.label)
print_variables_to_file(front, 'VAR.' + algorithm.label)
print('Algorithm (continuous problem): ' + algorithm.get_name())
print('Problem: ' + problem.get_name())
print('Computing time: ' + str(algorithm.total_computing_time))
from jmetal.algorithm.multiobjective.nsgaii import NSGAII
from jmetal.operator import SBXCrossover, PolynomialMutation
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__':
problem = ZDT1Modified()
max_evaluations = 100
algorithm = NSGAII(
problem=problem,
population_size=10,
offspring_population_size=10,
mutation=PolynomialMutation(probability=1.0 /
problem.number_of_variables,
distribution_index=20),
crossover=SBXCrossover(probability=1.0, distribution_index=20),
termination_criterion=StoppingByEvaluations(max=max_evaluations))
algorithm.run()
front = algorithm.get_result()
# Save results to file
print_function_values_to_file(front, 'FUN.NSGAII.ZDT1')
print_variables_to_file(front, 'VAR.NSGAII.ZDT1')
print('Algorithm (continuous problem): ' + algorithm.get_name())
print('Problem: ' + problem.get_name())
print('Computing time: ' + str(algorithm.total_computing_time))
