def configure_experiment(problems: dict, n_run: int):
jobs = []
max_evaluations = 25000
for run in range(n_run):
for problem_tag, problem in problems.items():
jobs.append(
Job(
algorithm=NSGAII(
problem=problem,
population_size=100,
offspring_population_size=100,
mutation=PolynomialMutation(
probability=1.0 / problem.number_of_variables,
distribution_index=20),
crossover=SBXCrossover(probability=1.0,
distribution_index=20),
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations),
),
algorithm_tag="NSGAII",
problem_tag=problem_tag,
run=run,
))
jobs.append(
Job(
algorithm=GDE3(
problem=problem,
population_size=100,
cr=0.5,
f=0.5,
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations),
),
algorithm_tag="GDE3",
problem_tag=problem_tag,
run=run,
))
jobs.append(
Job(
algorithm=SMPSO(
problem=problem,
swarm_size=100,
mutation=PolynomialMutation(
probability=1.0 / problem.number_of_variables,
distribution_index=20),
leaders=CrowdingDistanceArchive(100),
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations),
),
algorithm_tag="SMPSO",
problem_tag=problem_tag,
run=run,
))
return jobs
from jmetal.algorithm.multiobjective.gde3 import GDE3
from jmetal.problem import ZDT1
from jmetal.util.solution import (
print_function_values_to_file,
print_variables_to_file,
read_solutions,
)
from jmetal.util.termination_criterion import StoppingByKeyboard
if __name__ == "__main__":
problem = ZDT1()
problem.reference_front = read_solutions(
filename="resources/reference_front/ZDT1.pf")
algorithm = GDE3(problem=problem,
population_size=100,
cr=0.5,
f=0.5,
termination_criterion=StoppingByKeyboard())
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.comparator import GDominanceComparator
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == "__main__":
problem = ZDT2()
problem.reference_front = read_solutions(
filename="resources/reference_front/{}.pf".format(problem.get_name()))
max_evaluations = 25000
reference_point = [0.2, 0.5]
algorithm = GDE3(
problem=problem,
population_size=100,
cr=0.5,
f=0.5,
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations),
dominance_comparator=GDominanceComparator(reference_point),
)
algorithm.observable.register(
observer=VisualizerObserver(reference_front=problem.reference_front,
reference_point=reference_point))
algorithm.run()
front = algorithm.get_result()
# Plot front
plot_front = Plot(plot_title="Pareto front approximation",
reference_front=problem.reference_front,
from jmetal.algorithm.multiobjective.gde3 import GDE3
from jmetal.problem.singleobjective.unconstrained import Rastrigin
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 = Rastrigin(10)
algorithm = GDE3(
problem=problem,
population_size=100,
cr=0.5,
f=0.5,
termination_criterion=StoppingByEvaluations(50000)
)
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())
print_variables_to_file(front, 'VAR.' + algorithm.get_name() + "." + problem.get_name())
print('Algorithm (continuous problem): ' + algorithm.get_name())
print('Problem: ' + problem.get_name())
print('Computing time: ' + str(algorithm.total_computing_time))
from examples.multiobjective.zdt1_modified import ZDT1Modified
from jmetal.algorithm.multiobjective.gde3 import GDE3
from jmetal.util.evaluator import SparkEvaluator
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 = ZDT1Modified()
algorithm = GDE3(problem=problem,
population_size=10,
cr=0.5,
f=0.5,
termination_criterion=StoppingByEvaluations(max=100),
population_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())
print('Algorithm (continuous problem): ' + algorithm.get_name())
print('Problem: ' + problem.get_name())
print('Computing time: ' + str(algorithm.total_computing_time))
def configure_experiment(problems: dict, n_run: int):
jobs = []
num_processes = config.num_cpu
population = 10
generations = 10
max_evaluations = population * generations
for run in range(n_run):
for problem_tag, problem in problems.items():
reference_point = FloatSolution([0, 0], [1, 1], problem.number_of_objectives, )
reference_point.objectives = np.repeat(1, problem.number_of_objectives).tolist()
jobs.append(
Job(
algorithm=NSGAIII(
problem=problem,
population_size=population,
mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables,
distribution_index=20),
crossover=SBXCrossover(probability=1.0, distribution_index=20),
population_evaluator=MultiprocessEvaluator(processes=num_processes),
termination_criterion=StoppingByEvaluations(max_evaluations=max_evaluations),
reference_directions=UniformReferenceDirectionFactory(4, n_points=100),
),
algorithm_tag='NSGAIII',
problem_tag=problem_tag,
run=run,
)
)
jobs.append(
Job(
algorithm=GDE3(
problem=problem,
population_size=population,
population_evaluator=MultiprocessEvaluator(processes=num_processes),
termination_criterion=StoppingByEvaluations(max_evaluations=max_evaluations),
cr=0.5,
f=0.5,
),
algorithm_tag='GDE3',
problem_tag=problem_tag,
run=run,
)
)
jobs.append(
Job(
algorithm=SPEA2(
problem=problem,
population_size=population,
mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables,
distribution_index=20),
crossover=SBXCrossover(probability=1.0, distribution_index=20),
population_evaluator=MultiprocessEvaluator(processes=num_processes),
termination_criterion=StoppingByEvaluations(max_evaluations=max_evaluations),
offspring_population_size=population,
),
algorithm_tag='SPEA2',
problem_tag=problem_tag,
run=run,
)
)
return jobs