# encoding=utf8
import logging
from numpy import exp
from NiaPy.algorithms.algorithm import Algorithm
logging.basicConfig()
logger = logging.getLogger('NiaPy.algorithms.other')
logger.setLevel('INFO')
__all__ = ['SimulatedAnnealing', 'coolDelta', 'coolLinear']
def coolDelta(currentT, T, deltaT, nFES, **kwargs):
r"""Calculate new temperature by differences.
Args:
currentT (float):
T (float):
kwargs (Dict[str, Any]): Additional arguments.
Returns:
float: New temperature.
"""
return currentT - deltaT
def coolLinear(currentT, T, deltaT, nFES, **kwargs):
r"""Calculate temperature with linear function.
Args:
currentT (float): Current temperature.
T (float):
deltaT (float):
nFES (int): Number of evaluations done.
kwargs (Dict[str, Any]): Additional arguments.
Returns:
float: New temperature.
"""
return currentT - T / nFES
[docs]class SimulatedAnnealing(Algorithm):
r"""Implementation of Simulated Annealing Algorithm.
Algorithm:
Simulated Annealing Algorithm
Date:
2018
Authors:
Jan Popič and Klemen Berkovič
License:
MIT
Reference URL:
Reference paper:
Attributes:
Name (List[str]): List of strings representing algorithm name.
delta (float): Movement for neighbour search.
T (float); Starting temperature.
deltaT (float): Change in temperature.
coolingMethod (Callable): Neighbourhood function.
epsilon (float): Error value.
See Also:
* :class:`NiaPy.algorithms.Algorithm`
"""
Name = ['SimulatedAnnealing', 'SA']
[docs] @staticmethod
def algorithmInfo():
r"""Get basic information of algorithm.
Returns:
str: Basic information of algorithm.
See Also:
* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
"""
return r"""None"""
[docs] @staticmethod
def typeParameters():
r"""Get dictionary with functions for checking values of parameters.
Returns:
Dict[str, Callable]:
* delta (Callable[[Union[float, int], bool]): TODO
"""
return {
'delta': lambda x: isinstance(x, (int, float)) and x > 0,
'T': lambda x: isinstance(x, (int, float)) and x > 0,
'deltaT': lambda x: isinstance(x, (int, float)) and x > 0,
'epsilon': lambda x: isinstance(x, float) and 0 < x < 1
}
[docs] def setParameters(self, delta=0.5, T=2000, deltaT=0.8, coolingMethod=coolDelta, epsilon=1e-23, **ukwargs):
r"""Set the algorithm parameters/arguments.
Arguments:
delta (Optional[float]): Movement for neighbour search.
T (Optional[float]); Starting temperature.
deltaT (Optional[float]): Change in temperature.
coolingMethod (Optional[Callable]): Neighbourhood function.
epsilon (Optional[float]): Error value.
See Also
* :func:`NiaPy.algorithms.Algorithm.setParameters`
"""
ukwargs.pop('NP', None)
Algorithm.setParameters(self, NP=1, **ukwargs)
self.delta, self.T, self.deltaT, self.cool, self.epsilon = delta, T, deltaT, coolingMethod, epsilon
[docs] def getParameters(self):
r"""Get algorithms parametes values.
Returns:
Dict[str, Any]:
See Also
* :func:`NiaPy.algorithms.Algorithm.getParameters`
"""
d = Algorithm.getParameters(self)
d.update({
'delta': self.delta,
'deltaT': self.deltaT,
'T': self.T,
'epsilon': self.epsilon
})
return d
[docs] def initPopulation(self, task):
r"""Initialize the starting population.
Args:
task (Task): Optimization task.
Returns:
Tuple[numpy.ndarray, float, dict]:
1. Initial solution
2. Initial solutions fitness/objective value
3. Additional arguments
"""
x = task.Lower + task.bcRange() * self.rand(task.D)
curT, xfit = self.T, task.eval(x)
return x, xfit, {'curT': curT}
[docs] def runIteration(self, task, x, xfit, xb, fxb, curT, **dparams):
r"""Core function of the algorithm.
Args:
task (Task):
x (numpy.ndarray):
xfit (float):
xb (numpy.ndarray):
fxb (float):
curT (float):
**dparams (dict): Additional arguments.
Returns:
Tuple[numpy.ndarray, float, numpy.ndarray, float, dict]:
1. New solution
2. New solutions fitness/objective value
3. New global best solution
4. New global best solutions fitness/objective value
5. Additional arguments
"""
c = task.repair(x - self.delta / 2 + self.rand(task.D) * self.delta, rnd=self.Rand)
cfit = task.eval(c)
deltaFit, r = cfit - xfit, self.rand()
if deltaFit < 0 or r < exp(deltaFit / curT): x, xfit = c, cfit
curT = self.cool(curT, self.T, deltaT=self.deltaT, nFES=task.nFES)
xb, fxb = self.getBest(x, xfit, xb, fxb)
return x, xfit, xb, fxb, {'curT': curT}
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