# encoding=utf8
import logging
import numpy as np
from niapy.algorithms.algorithm import Algorithm
logging.basicConfig()
logger = logging.getLogger('niapy.algorithms.basic')
logger.setLevel('INFO')
__all__ = ['BatAlgorithm']
[docs]class BatAlgorithm(Algorithm):
r"""Implementation of Bat algorithm.
Algorithm:
Bat algorithm
Date:
2015
Authors:
Iztok Fister Jr., Marko Burjek and Klemen Berkovič
License:
MIT
Reference paper:
Yang, Xin-She. "A new metaheuristic bat-inspired algorithm." Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin, Heidelberg, 2010. 65-74.
Attributes:
Name (List[str]): List of strings representing algorithm name.
loudness (float): Loudness.
pulse_rate (float): Pulse rate.
min_frequency (float): Minimum frequency.
max_frequency (float): Maximum frequency.
See Also:
* :class:`niapy.algorithms.Algorithm`
"""
Name = ['BatAlgorithm', 'BA']
[docs] @staticmethod
def info():
r"""Get algorithms information.
Returns:
str: Algorithm information.
See Also:
* :func:`niapy.algorithms.Algorithm.info`
"""
return r"""Yang, Xin-She. "A new metaheuristic bat-inspired algorithm." Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin, Heidelberg, 2010. 65-74."""
[docs] def __init__(self, population_size=40, loudness=0.5, pulse_rate=0.5, min_frequency=0.0, max_frequency=2.0, *args,
**kwargs):
"""Initialize BatAlgorithm.
Args:
population_size (Optional[int]): Population size.
loudness (Optional[float]): Loudness.
pulse_rate (Optional[float]): Pulse rate.
min_frequency (Optional[float]): Minimum frequency.
max_frequency (Optional[float]): Maximum frequency.
See Also:
:func:`niapy.algorithms.Algorithm.__init__`
"""
super().__init__(population_size, *args, **kwargs)
self.loudness = loudness
self.pulse_rate = pulse_rate
self.min_frequency = min_frequency
self.max_frequency = max_frequency
[docs] def set_parameters(self, population_size=40, loudness=0.5, pulse_rate=0.5, min_frequency=0.0, max_frequency=2.0,
**kwargs):
r"""Set the parameters of the algorithm.
Args:
population_size (Optional[int]): Population size.
loudness (Optional[float]): Loudness.
pulse_rate (Optional[float]): Pulse rate.
min_frequency (Optional[float]): Minimum frequency.
max_frequency (Optional[float]): Maximum frequency.
See Also:
* :func:`niapy.algorithms.Algorithm.set_parameters`
"""
super().set_parameters(population_size=population_size, **kwargs)
self.loudness = loudness
self.pulse_rate = pulse_rate
self.min_frequency = min_frequency
self.max_frequency = max_frequency
[docs] def get_parameters(self):
r"""Get parameters of the algorithm.
Returns:
Dict[str, Any]: Algorithm parameters.
"""
d = super().get_parameters()
d.update({
'loudness': self.loudness,
'pulse_rate': self.pulse_rate,
'min_frequency': self.min_frequency,
'max_frequency': self.max_frequency
})
return d
[docs] def init_population(self, task):
r"""Initialize the starting population.
Parameters:
task (Task): Optimization task
Returns:
Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]:
1. New population.
2. New population fitness/function values.
3. Additional arguments:
* velocities (numpy.ndarray[float]): Velocities
See Also:
* :func:`niapy.algorithms.Algorithm.init_population`
"""
population, fitness, d = super().init_population(task)
velocities = np.zeros((self.population_size, task.dimension))
d.update({'velocities': velocities})
return population, fitness, d
[docs] def local_search(self, best, task, **kwargs):
r"""Improve the best solution according to the Yang (2010).
Args:
best (numpy.ndarray): Global best individual.
task (Task): Optimization task.
Returns:
numpy.ndarray: New solution based on global best individual.
"""
return task.repair(best + 0.001 * self.standard_normal(task.dimension))
[docs] def run_iteration(self, task, population, population_fitness, best_x, best_fitness, **params):
r"""Core function of Bat Algorithm.
Parameters:
task (Task): Optimization task.
population (numpy.ndarray): Current population
population_fitness (numpy.ndarray[float]): Current population fitness/function values
best_x (numpy.ndarray): Current best individual
best_fitness (float): Current best individual function/fitness value
params (Dict[str, Any]): Additional algorithm arguments
Returns:
Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, Dict[str, Any]]:
1. New population
2. New population fitness/function values
3. New global best solution
4. New global best fitness/objective value
5. Additional arguments:
* velocities (numpy.ndarray): Velocities
"""
velocities = params.pop('velocities')
for i in range(self.population_size):
frequency = self.min_frequency + (self.max_frequency - self.min_frequency) * self.random()
velocities[i] += (population[i] - best_x) * frequency
if self.random() > self.pulse_rate:
solution = self.local_search(best=best_x, task=task, i=i, population=population)
else:
solution = task.repair(population[i] + velocities[i], rng=self.rng)
new_fitness = task.eval(solution)
if (new_fitness <= population_fitness[i]) and (self.random() < self.loudness):
population[i], population_fitness[i] = solution, new_fitness
if new_fitness <= best_fitness:
best_x, best_fitness = solution.copy(), new_fitness
return population, population_fitness, best_x, best_fitness, {'velocities': velocities}
# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3