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# encoding=utf8 |
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# pylint: disable=mixed-indentation, trailing-whitespace, multiple-statements, attribute-defined-outside-init, logging-not-lazy, unused-argument, arguments-differ, bad-continuation |
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import logging |
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from numpy import exp |
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from NiaPy.algorithms.algorithm import Algorithm |
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logging.basicConfig() |
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logger = logging.getLogger('NiaPy.algorithms.other') |
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logger.setLevel('INFO') |
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__all__ = ['SimulatedAnnealing', 'coolDelta', 'coolLinear'] |
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def coolDelta(currentT, T, deltaT, nFES, **kwargs): |
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r"""Calculate new temperature by differences. |
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Args: |
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currentT (float): |
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T (float): |
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kwargs (Dict[str, Any]): Additional arguments. |
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Returns: |
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float: New temperature. |
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""" |
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return currentT - deltaT |
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def coolLinear(currentT, T, deltaT, nFES, **kwargs): |
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r"""Calculate temperature with linear function. |
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Args: |
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currentT (float): Current temperature. |
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T (float): |
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deltaT (float): |
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nFES (int): Number of evaluations done. |
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kwargs (Dict[str, Any]): Additional arguments. |
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Returns: |
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float: New temperature. |
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""" |
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return currentT - T / nFES |
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class SimulatedAnnealing(Algorithm): |
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r"""Implementation of Simulated Annealing Algorithm. |
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Algorithm: |
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Simulated Annealing Algorithm |
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Date: |
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2018 |
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Authors: |
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Jan Popič and Klemen Berkovič |
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License: |
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MIT |
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Reference URL: |
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Reference paper: |
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Attributes: |
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Name (List[str]): List of strings representing algorithm name. |
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delta (float): Movement for neighbour search. |
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T (float); Starting temperature. |
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deltaT (float): Change in temperature. |
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coolingMethod (Callable): Neighbourhood function. |
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epsilon (float): Error value. |
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See Also: |
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* :class:`NiaPy.algorithms.Algorithm` |
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""" |
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Name = ['SimulatedAnnealing', 'SA'] |
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@staticmethod |
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def algorithmInfo(): |
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r"""Get basic information of algorithm. |
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Returns: |
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str: Basic information of algorithm. |
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See Also: |
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* :func:`NiaPy.algorithms.Algorithm.algorithmInfo` |
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""" |
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return r"""None""" |
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View Code Duplication |
@staticmethod |
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def typeParameters(): |
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r"""Get dictionary with functions for checking values of parameters. |
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Returns: |
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Dict[str, Callable]: |
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* delta (Callable[[Union[float, int], bool]): TODO |
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""" |
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return { |
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'delta': lambda x: isinstance(x, (int, float)) and x > 0, |
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'T': lambda x: isinstance(x, (int, float)) and x > 0, |
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'deltaT': lambda x: isinstance(x, (int, float)) and x > 0, |
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'epsilon': lambda x: isinstance(x, float) and 0 < x < 1 |
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} |
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def setParameters(self, delta=0.5, T=2000, deltaT=0.8, coolingMethod=coolDelta, epsilon=1e-23, **ukwargs): |
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r"""Set the algorithm parameters/arguments. |
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Arguments: |
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delta (Optional[float]): Movement for neighbour search. |
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T (Optional[float]); Starting temperature. |
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deltaT (Optional[float]): Change in temperature. |
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coolingMethod (Optional[Callable]): Neighbourhood function. |
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epsilon (Optional[float]): Error value. |
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See Also |
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* :func:`NiaPy.algorithms.Algorithm.setParameters` |
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""" |
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ukwargs.pop('NP', None) |
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Algorithm.setParameters(self, NP=1, **ukwargs) |
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self.delta, self.T, self.deltaT, self.cool, self.epsilon = delta, T, deltaT, coolingMethod, epsilon |
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if ukwargs: logger.info('Unused arguments: %s' % (ukwargs)) |
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def initPopulation(self, task): |
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x = task.Lower + task.bcRange() * self.rand(task.D) |
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curT, xfit = self.T, task.eval(x) |
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return x, xfit, {'curT': curT} |
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def runIteration(self, task, x, xfit, xb, fxb, curT, **dparams): |
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c = task.repair(x - self.delta / 2 + self.rand(task.D) * self.delta, rnd=self.Rand) |
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cfit = task.eval(c) |
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deltaFit, r = cfit - xfit, self.rand() |
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if deltaFit < 0 or r < exp(deltaFit / curT): x, xfit = c, cfit |
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curT = self.cool(curT, self.T, deltaT=self.deltaT, nFES=task.nFES) |
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return x, xfit, {'curT': curT} |
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# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3 |
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NiaOrg /
NiaPy
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