# encoding=utf8
import logging
from niapy.algorithms.basic.de import cross_best1
from niapy.algorithms.modified.saba import SelfAdaptiveBatAlgorithm
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, numpy.random.Generator, Dict[str, Any]): Local search method based of Differential evolution strategy.
See Also:
* :class:`niapy.algorithms.basic.BatAlgorithm`
"""
Name = ['HybridSelfAdaptiveBatAlgorithm', 'HSABA']
[docs] @staticmethod
def info():
r"""Get basic information about the algorithm.
Returns:
str: Basic information.
See Also:
* :func:`niapy.algorithms.Algorithm.info`
"""
return r"""Fister, Iztok, Simon Fong, and Janez Brest. "A novel hybrid self-adaptive bat algorithm." The Scientific World Journal 2014 (2014)."""
[docs] def __init__(self, differential_weight=0.9, crossover_probability=0.85, strategy=cross_best1, *args, **kwargs):
"""Initialize HybridSelfAdaptiveBatAlgorithm.
Args:
differential_weight (Optional[float]): Scaling factor for local search.
crossover_probability (Optional[float]): Probability of crossover for local search.
strategy (Optional[Callable[[numpy.ndarray, int, numpy.ndarray, float, float, numpy.random.Generator, Dict[str, Any], numpy.ndarray]]): Local search method based of Differential evolution strategy.
See Also:
* :func:`niapy.algorithms.basic.BatAlgorithm.__init__`
"""
super().__init__(*args, **kwargs)
self.differential_weight = differential_weight
self.crossover_probability = crossover_probability
self.strategy = strategy
[docs] def set_parameters(self, differential_weight=0.9, crossover_probability=0.85, strategy=cross_best1, **kwargs):
r"""Set core parameters of HybridBatAlgorithm algorithm.
Args:
differential_weight (Optional[float]): Scaling factor for local search.
crossover_probability (Optional[float]): Probability of crossover for local search.
strategy (Optional[Callable[[numpy.ndarray, int, numpy.ndarray, float, float, numpy.random.Generator, Dict[str, Any], numpy.ndarray]]): Local search method based of Differential evolution strategy.
See Also:
* :func:`niapy.algorithms.basic.BatAlgorithm.set_parameters`
"""
super().set_parameters(**kwargs)
self.differential_weight = differential_weight
self.crossover_probability = crossover_probability
self.strategy = strategy
[docs] def get_parameters(self):
r"""Get parameters of the algorithm.
Returns:
Dict[str, Any]: Parameters of the algorithm.
See Also:
* :func:`niapy.algorithms.modified.AdaptiveBatAlgorithm.get_parameters`
"""
d = super().get_parameters()
d.update({
'differential_weight': self.differential_weight,
'crossover_probability': self.crossover_probability
})
return d
[docs] def local_search(self, best, loudness, task, i=None, population=None, **kwargs):
r"""Improve the best solution.
Args:
best (numpy.ndarray): Global best individual.
loudness (float): Loudness.
task (Task): Optimization task.
i (int): Index of current individual.
population (numpy.ndarray): Current best population.
Returns:
numpy.ndarray: New solution based on global best individual.
"""
return task.repair(self.strategy(population, i, self.differential_weight, self.crossover_probability, rng=self.rng, x_b=best), rng=self.rng)
