from __future__ import annotations
import warnings
from typing import TYPE_CHECKING
import optiland.backend as be
from scipy import optimize
from .base import OptimizerGeneric
if TYPE_CHECKING:
from ...problem import OptimizationProblem
[docs]
class DifferentialEvolution(OptimizerGeneric):
"""Differential Evolution optimizer for solving optimization problems.
Args:
problem (OptimizationProblem): The optimization problem to be solved.
Methods:
optimize(maxiter=1000, disp=True, workers=-1): Runs the differential
evolution optimization algorithm.
"""
def __init__(self, problem: OptimizationProblem):
"""Initializes a new instance of the DifferentialEvolution class.
Args:
problem (OptimizationProblem): The optimization problem to be
solved.
"""
super().__init__(problem)
[docs]
def optimize(self, maxiter=1000, disp=True, workers=-1, callback=None):
"""Runs the differential evolution optimization algorithm.
Args:
maxiter (int): Maximum number of iterations.
disp (bool): Set to True to display status messages.
workers (int): Number of parallel workers to use. Set to -1 to use
all available processors.
callback (callable): A callable called after each iteration.
Returns:
result (OptimizeResult): The optimization result.
Raises:
ValueError: If any variable in the problem does not have bounds.
"""
# Get initial values in backend format
x0_backend = [var.value for var in self.problem.variables]
self._x.append(x0_backend) # Store backend values
# Convert x0 to NumPy for SciPy
x0_numpy = be.to_numpy(x0_backend)
bounds = tuple([var.bounds for var in self.problem.variables])
if any(None in bound for bound in bounds):
raise ValueError(
"Differential evolution requires all variables have bounds.",
)
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
updating = "deferred" if workers == -1 else "immediate"
result = optimize.differential_evolution(
self._fun,
bounds=bounds,
maxiter=maxiter,
x0=x0_numpy,
disp=disp,
updating=updating,
workers=workers,
callback=callback,
)
for idvar, var in enumerate(self.problem.variables):
var.update(result.x[idvar])
self.problem.update_optics()
return result