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# Author: Simon Blanke
# Email: [email protected]
# License: MIT License
import numpy as np
from ..local_opt import StochasticHillClimbingOptimizer
from ...search import Search
class SimulatedAnnealingOptimizer(StochasticHillClimbingOptimizer, Search):
name = "Simulated Annealing"
def __init__(
self,
*args,
annealing_rate=0.97,
start_temp=1,
**kwargs,
):
super().__init__(*args, **kwargs)
self.annealing_rate = annealing_rate
self.start_temp = start_temp
self.temp = start_temp
def _accept_default(self):
return np.exp(-self._score_norm_default() / self.temp)
def _accept_adapt(self):
return self._score_norm_adapt() / self.temp
def evaluate(self, score_new):
StochasticHillClimbingOptimizer.evaluate(self, score_new)
self.temp = self.temp * self.annealing_rate
SimonBlanke /
Gradient-Free-Optimizers
