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# encoding=utf8 |
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# pylint: disable=mixed-indentation, multiple-statements, logging-not-lazy, attribute-defined-outside-init, arguments-differ, bad-continuation, unused-argument |
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import logging |
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from numpy import full |
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from NiaPy.algorithms.basic import BatAlgorithm |
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from NiaPy.algorithms.basic.de import CrossBest1 |
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logging.basicConfig() |
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logger = logging.getLogger('NiaPy.algorithms.modified') |
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logger.setLevel('INFO') |
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__all__ = ['HybridBatAlgorithm'] |
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class HybridBatAlgorithm(BatAlgorithm): |
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r"""Implementation of Hybrid bat algorithm. |
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Algorithm: |
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Hybrid bat algorithm |
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Date: |
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2018 |
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Author: |
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Grega Vrbancic and Klemen Berkovič |
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License: |
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MIT |
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Reference paper: |
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Fister Jr., Iztok and Fister, Dusan and Yang, Xin-She. "A Hybrid Bat Algorithm". Elektrotehniski vestnik, 2013. 1-7. |
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Attributes: |
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Name (List[str]): List of strings representing algorithm name. |
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F (float): Scaling factor. |
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CR (float): Crossover. |
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See Also: |
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* :class:`NiaPy.algorithms.basic.BatAlgorithm` |
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""" |
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Name = ['HybridBatAlgorithm', 'HBA'] |
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@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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* F (Callable[[Union[int, float]], bool]): Scaling factor. |
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* CR (Callable[[float], bool]): Crossover probability. |
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""" |
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d = BatAlgorithm.typeParameters() |
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d['F'] = lambda x: isinstance(x, (int, float)) and x > 0 |
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d['CR'] = lambda x: isinstance(x, float) and 0 <= x <= 1 |
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return d |
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def setParameters(self, F=0.78, CR=0.35, CrossMutt=CrossBest1, **ukwargs): |
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r"""Set core parameters of HybridBatAlgorithm algorithm. |
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Arguments: |
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F (Optional[float]): Scaling factor. |
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CR (Optional[float]): Crossover. |
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See Also: |
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* :func:`NiaPy.algorithms.basic.BatAlgorithm.setParameters` |
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""" |
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BatAlgorithm.setParameters(self, **ukwargs) |
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self.F, self.CR, self.CrossMutt = F, CR, CrossMutt |
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if ukwargs: logger.info('Unused arguments: %s' % (ukwargs)) |
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def generateBest(self, best, task, i, Sol, **kwargs): |
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r"""Generate new solution based on global best known solution. |
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Args: |
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best (numpy.ndarray): Global best individual. |
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task (Task): Optimization task. |
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i (int): Index of current individual. |
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Sol (numpy.ndarray): Current best population. |
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**kwargs (Dict[str, Any]): |
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Returns: |
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numpy.ndarray: New solution based on global best individual. |
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""" |
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return task.repair(self.CrossMutt(Sol, i, best, self.F, self.CR, rnd=self.Rand), rnd=self.Rand) |
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# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3 |
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NiaOrg /
NiaPy
