# encoding=utf8
import logging
from NiaPy.algorithms.modified import SelfAdaptiveBatAlgorithm
from NiaPy.algorithms.basic.de import CrossBest1
logging.basicConfig()
logger = logging.getLogger('NiaPy.algorithms.modified')
logger.setLevel('INFO')
__all__ = ['HybridSelfAdaptiveBatAlgorithm']
[docs]class HybridSelfAdaptiveBatAlgorithm(SelfAdaptiveBatAlgorithm):
r"""Implementation of Hybrid self adaptive bat algorithm.
Algorithm:
Hybrid self adaptive bat algorithm
Date:
April 2019
Author:
Klemen Berkovič
License:
MIT
Reference paper:
Fister, Iztok, Simon Fong, and Janez Brest. "A novel hybrid self-adaptive bat algorithm." The Scientific World Journal 2014 (2014).
Reference URL:
https://www.hindawi.com/journals/tswj/2014/709738/cta/
Attributes:
Name (List[str]): List of strings representing algorithm name.
F (float): Scaling factor for local search.
CR (float): Probability of crossover for local search.
CrossMutt (Callable[[numpy.ndarray, int, numpy.ndarray, float, float, mtrand.RandomState, Dict[str, Any]): Local search method based of Differential evolution strategy.
See Also:
* :class:`NiaPy.algorithms.basic.BatAlgorithm`
"""
Name = ['HybridSelfAdaptiveBatAlgorithm', 'HSABA']
[docs] @staticmethod
def algorithmInfo():
r"""Get basic information about the algorithm.
Returns:
str: Basic information.
See Also:
* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
"""
return r"""Fister, Iztok, Simon Fong, and Janez Brest. "A novel hybrid self-adaptive bat algorithm." The Scientific World Journal 2014 (2014)."""
[docs] @staticmethod
def typeParameters():
r"""Get dictionary with functions for checking values of parameters.
Returns:
Dict[str, Callable]: Additional arguments.
See Also:
* :func:`NiaPy.algorithms.basic.BatAlgorithm.typeParameters`
"""
d = SelfAdaptiveBatAlgorithm.typeParameters()
d.update({
'F': lambda x: isinstance(x, (int, float)) and x > 0,
'CR': lambda x: isinstance(x, float) and 0 <= x <= 1
})
return d
[docs] def setParameters(self, F=0.9, CR=0.85, CrossMutt=CrossBest1, **ukwargs):
r"""Set core parameters of HybridBatAlgorithm algorithm.
Arguments:
F (Optional[float]): Scaling factor for local search.
CR (Optional[float]): Probability of crossover for local search.
CrossMutt (Optional[Callable[[numpy.ndarray, int, numpy.ndarray, float, float, mtrand.RandomState, Dict[str, Any], numpy.ndarray]]): Local search method based of Differential evolution strategy.
ukwargs (Dict[str, Any]): Additional arguments.
See Also:
* :func:`NiaPy.algorithms.basic.BatAlgorithm.setParameters`
"""
SelfAdaptiveBatAlgorithm.setParameters(self, **ukwargs)
self.F, self.CR, self.CrossMutt = F, CR, CrossMutt
[docs] def getParameters(self):
r"""Get parameters of the algorithm.
Returns:
Dict[str, Any]: Parameters of the algorithm.
See Also:
* :func:`NiaPy.algorithms.modified.AdaptiveBatAlgorithm.getParameters`
"""
d = SelfAdaptiveBatAlgorithm.getParameters(self)
d.update({
'F': self.F,
'CR': self.CR
})
return d
[docs] def localSearch(self, best, A, i, Sol, task, **kwargs):
r"""Improve the best solution.
Args:
best (numpy.ndarray): Global best individual.
task (Task): Optimization task.
i (int): Index of current individual.
Sol (numpy.ndarray): Current best population.
**kwargs (Dict[str, Any]):
Returns:
numpy.ndarray: New solution based on global best individual.
"""
return task.repair(self.CrossMutt(Sol, i, best, self.F, self.CR, rnd=self.Rand), rnd=self.Rand)
# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
