if __name__ == "__main__":
# Defining problem Srinivas on the fly
def f1(x: [float]):
return 2.0 + (x[0] - 2.0) * (x[0] - 2.0) + (x[1] - 1.0) * (x[1] - 1.0)
def f2(x: [float]):
return 9.0 * x[0] - (x[1] - 1.0) * (x[1] - 1.0)
def c1(x: [float]):
return 1.0 - (x[0] * x[0] + x[1] * x[1]) / 225.0
def c2(x: [float]):
return (3.0 * x[1] - x[0]) / 10.0 - 1.0
problem = (OnTheFlyFloatProblem().set_name("Srinivas").add_variable(
-20.0, 20.0).add_variable(-20.0, 20.0).add_function(f1).add_function(
f2).add_constraint(c1).add_constraint(c2))
max_evaluations = 25000
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.run()
from jmetal.core.problem import OnTheFlyFloatProblem
from jmetal.operator import PolynomialMutation
from jmetal.util.archive import CrowdingDistanceArchive
from jmetal.util.solution import print_function_values_to_file, print_variables_to_file
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == '__main__':
# Defining problem Schaffer on the fly
def f1(x: [float]):
return x[0] * x[0]
def f2(x: [float]):
return (x[0] - 2) * (x[0] - 2)
problem = OnTheFlyFloatProblem()
problem \
.set_name('Schaffer') \
.add_variable(-10000.0, 10000.0) \
.add_function(f1) \
.add_function(f2)
max_evaluations = 25000
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(
from jmetal.operator import PolynomialMutation
from jmetal.util.archive import CrowdingDistanceArchive
from jmetal.util.observer import ProgressBarObserver, VisualizerObserver
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__':
# Defining problem Schaffer on the fly
def f1(x: [float]):
return x[0] * x[0]
def f2(x: [float]):
return (x[0] - 2) * (x[0] - 2)
problem = OnTheFlyFloatProblem()
problem \
.set_name('Schaffer') \
.add_variable(-10000.0, 10000.0) \
.add_function(f1) \
.add_function(f2)
max_evaluations = 25000
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=max_evaluations)
)
return 2.0 + (x[0] - 2.0) * (x[0] - 2.0) + (x[1] - 1.0) * (x[1] - 1.0)
def f2(x: [float]):
return 9.0 * x[0] - (x[1] - 1.0) * (x[1] - 1.0)
def c1(x: [float]):
return 1.0 - (x[0] * x[0] + x[1] * x[1]) / 225.0
def c2(x: [float]):
return (3.0 * x[1] - x[0]) / 10.0 - 1.0
problem = OnTheFlyFloatProblem() \
.set_name('Srinivas') \
.add_variable(-20.0, 20.0) \
.add_variable(-20.0, 20.0) \
.add_function(f1) \
.add_function(f2) \
.add_constraint(c1) \
.add_constraint(c2)
max_evaluations = 25000
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=max_evaluations))
algorithm.observable.register(observer=ProgressBarObserver(
from jmetal.operator import PolynomialMutation
from jmetal.util.archive import CrowdingDistanceArchive
from jmetal.util.observer import ProgressBarObserver, VisualizerObserver
from jmetal.util.solutions import print_function_values_to_file, print_variables_to_file
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == '__main__':
# Defining problem Schaffer on the fly
def f1(x: [float]):
return x[0] * x[0]
def f2(x: [float]):
return (x[0] - 2) * (x[0] - 2)
problem = OnTheFlyFloatProblem()
problem \
.set_name('Schaffer') \
.add_variable(-10000.0, 10000.0) \
.add_function(f1) \
.add_function(f2)
max_evaluations = 25000
algorithm = SMPSO(
problem=problem,
swarm_size=100,
mutation=PolynomialMutation(probability=1.0 /
problem.number_of_variables,
distribution_index=20),
leaders=CrowdingDistanceArchive(100),
return 2.0 + (x[0] - 2.0) * (x[0] - 2.0) + (x[1] - 1.0) * (x[1] - 1.0)
def f2(x: [float]):
return 9.0 * x[0] - (x[1] - 1.0) * (x[1] - 1.0)
def c1(x: [float]):
return 1.0 - (x[0] * x[0] + x[1] * x[1]) / 225.0
def c2(x: [float]):
return (3.0 * x[1] - x[0]) / 10.0 - 1.0
problem = OnTheFlyFloatProblem() \
.set_name('Srinivas') \
.add_variable(-20.0, 20.0) \
.add_variable(-20.0, 20.0) \
.add_function(f1) \
.add_function(f2) \
.add_constraint(c1) \
.add_constraint(c2)
max_evaluations = 25000
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=max_evaluations))
algorithm.run()
read_solutions,
)
from jmetal.util.termination_criterion import StoppingByEvaluations
"""
Program to configure and run the NSGA-II algorithm configured with standard settings.
"""
if __name__ == "__main__":
# Defining problem Schaffer on the fly
def f1(x: [float]):
return x[0] * x[0]
def f2(x: [float]):
return (x[0] - 2) * (x[0] - 2)
problem = OnTheFlyFloatProblem()
problem.set_name("Schaffer").add_variable(
-10000.0, 10000.0).add_function(f1).add_function(f2)
problem.reference_front = read_solutions(
filename="resources/reference_front/ZDT1.pf")
max_evaluations = 25000
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),
from jmetal.util.solution import get_non_dominated_solutions, read_solutions, print_function_values_to_file, \
print_variables_to_file
from jmetal.util.termination_criterion import StoppingByEvaluations
"""
Program to configure and run the NSGA-II algorithm configured with standard settings.
"""
if __name__ == '__main__':
# Defining problem Schaffer on the fly
def f1(x: [float]):
return x[0] * x[0]
def f2(x: [float]):
return (x[0] - 2) * (x[0] - 2)
problem = OnTheFlyFloatProblem()
problem \
.set_name('Schaffer') \
.add_variable(-10000.0, 10000.0) \
.add_function(f1) \
.add_function(f2)
problem.reference_front = read_solutions(
filename='resources/reference_front/ZDT1.pf')
max_evaluations = 25000
algorithm = NSGAII(problem=problem,
population_size=100,
offspring_population_size=100,
mutation=PolynomialMutation(probability=1.0 /
problem.number_of_variables,
return 2.0 + (x[0] - 2.0) * (x[0] - 2.0) + (x[1] - 1.0) * (x[1] - 1.0)
def f2(x: [float]):
return 9.0 * x[0] - (x[1] - 1.0) * (x[1] - 1.0)
def c1(x: [float]):
return 1.0 - (x[0] * x[0] + x[1] * x[1]) / 225.0
def c2(x: [float]):
return (3.0 * x[1] - x[0]) / 10.0 - 1.0
problem = OnTheFlyFloatProblem() \
.set_name('Srinivas') \
.add_variable(-20.0, 20.0) \
.add_variable(-20.0, 20.0) \
.add_function(f1) \
.add_function(f2) \
.add_constraint(c1) \
.add_constraint(c2)
problem.reference_front = read_solutions(
filename='resources/reference_front/Srinivas.pf')
max_evaluations = 25000
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,