class DefaultTermination(ToleranceBasedTermination):
def __init__(self,
x_tol,
cv_tol,
f_tol,
nth_gen=5,
n_last=20,
n_max_gen=1000,
n_max_evals=100000,
**kwargs) -> None:
"""
Parameters
----------
cv_tol
x_tol
f_tol
n_max_gen : int
A limit on maximum number of generations to avoid it never terminates
(set to `None` or infinity to disable)
n_max_evals : int
A limit on maximum number of function evaluations to avoid it never terminates
(set to `None` or infinity to disable)
nth_gen
n_last
kwargs
"""
super().__init__(n_last=n_last, nth_gen=nth_gen, **kwargs)
self.n_max_gen = MaximumGenerationTermination(n_max_gen)
self.n_max_evals = MaximumFunctionCallTermination(n_max_evals)
self.x_tol = DesignSpaceToleranceTermination(tol=x_tol,
n_last=n_last,
nth_gen=nth_gen)
self.cv = ConstraintViolationToleranceTermination(tol=cv_tol,
n_last=n_last,
nth_gen=nth_gen)
self.f_tol = f_tol
def _store(self, algorithm):
return {
"n_max_gen": self.n_max_gen.do_continue(algorithm),
"n_max_evals": self.n_max_evals.do_continue(algorithm),
"x_tol": self.x_tol.do_continue(algorithm),
"cv": self.cv.do_continue(algorithm),
"f_tol": self.f_tol.do_continue(algorithm)
}
def _decide(self):
metric = self.history[-1]
return metric["n_max_gen"] and metric["n_max_evals"] and metric[
"x_tol"] and (metric["cv"] or metric["f_tol"])
def __init__(self,
x_tol,
cv_tol,
f_tol,
nth_gen=5,
n_last=20,
n_max_gen=1000,
n_max_evals=100000,
**kwargs) -> None:
"""
Parameters
----------
cv_tol
x_tol
f_tol
n_max_gen : int
A limit on maximum number of generations to avoid it never terminates
(set to `None` or infinity to disable)
n_max_evals : int
A limit on maximum number of function evaluations to avoid it never terminates
(set to `None` or infinity to disable)
nth_gen
n_last
kwargs
"""
super().__init__(n_last=n_last, nth_gen=nth_gen, **kwargs)
self.n_max_gen = MaximumGenerationTermination(n_max_gen)
self.n_max_evals = MaximumFunctionCallTermination(n_max_evals)
self.x_tol = DesignSpaceToleranceTermination(tol=x_tol,
n_last=n_last,
nth_gen=nth_gen)
self.cv = ConstraintViolationToleranceTermination(tol=cv_tol,
n_last=n_last,
nth_gen=nth_gen)
self.f_tol = f_tol
def __init__(self,
x_tol=1e-32,
cv_tol=1e-6,
f_tol=1e-6,
nth_gen=5,
n_last=20,
**kwargs) -> None:
super().__init__(DesignSpaceToleranceTermination(tol=x_tol, n_last=n_last),
ConstraintViolationToleranceTermination(tol=cv_tol, n_last=n_last),
NicheSingleObjectiveSpaceToleranceTermination(tol=f_tol, n_last=n_last, nth_gen=nth_gen),
**kwargs)
def __init__(self,
x_tol=1e-8,
cv_tol=1e-6,
f_tol=0.0025,
nth_gen=5,
n_last=30,
**kwargs) -> None:
super().__init__(
DesignSpaceToleranceTermination(tol=x_tol, n_last=n_last),
ConstraintViolationToleranceTermination(tol=cv_tol, n_last=n_last),
MultiObjectiveSpaceToleranceTermination(tol=f_tol,
n_last=n_last,
nth_gen=nth_gen), **kwargs)
def calculate_pareto_front(
state_space_equation,
tau,
y_measured,
x0,
xl: Union[np.ndarray, None],
xu: Union[np.ndarray, None],
n_var: int,
n_obj: int,
normalize_quaternions: bool,
n_constr=0,
pop_size=100,
n_max_gen=1000,
verbose=True,
display=MyDisplay(),
) -> Result:
# calculate pareto frontier
problem = UUVParameterProblem(
state_space_equation,
tau,
y_measured,
x0,
n_var=n_var,
n_obj=n_obj,
n_constr=n_constr,
xl=xl,
xu=xu,
normalize_quaternions=normalize_quaternions,
)
algorithm = NSGA2(
pop_size=pop_size,
# n_offsprings=int(pop_size / 2),
crossover=SimulatedBinaryCrossover(eta=15, prob=0.9),
mutation=PolynomialMutation(prob=None, eta=15),
)
termination_default = MultiObjectiveDefaultTermination(n_max_gen=n_max_gen)
termination_design_space = DesignSpaceToleranceTermination(n_max_gen=n_max_gen)
termination_generations = get_termination("n_gen", 100)
res = minimize(
problem,
algorithm,
termination_default,
verbose=verbose,
display=display,
)
return res
from pymoo.algorithms.nsga2 import NSGA2
from pymoo.factory import get_problem
from pymoo.optimize import minimize
from pymoo.util.termination.x_tol import DesignSpaceToleranceTermination
problem = get_problem("zdt3")
algorithm = NSGA2(pop_size=100)
termination = DesignSpaceToleranceTermination(tol=0.0025, n_last=20)
res = minimize(problem,
algorithm,
termination,
pf=problem.pareto_front(),
seed=1,
verbose=False)
print(res.algorithm.n_gen)