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
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
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
# encoding=utf8
# This is temporary fix to import module from parent folder
# It will be removed when package is published on PyPI
import sys
sys.path.append('../')
# End of fix
from NiaPy.algorithms.modified import SelfAdaptiveBatAlgorithm
from NiaPy.task import StoppingTask
from NiaPy.benchmarks import Griewank
# we will run Bat Algorithm for 5 independent runs
algo = SelfAdaptiveBatAlgorithm()
for i in range(5):
task = StoppingTask(D=10,
nGEN=10000,
benchmark=Griewank(Lower=-600, Upper=600))
best = algo.run(task)
print('%s -> %s' % (best[0], best[1]))
print(algo.getParameters())
# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3