# 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): Initial loudness.
pulse_rate (float): Initial pulse rate.
alpha (float): Parameter for controlling loudness decrease.
gamma (float): Parameter for controlling pulse rate increase.
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=1.0, pulse_rate=1.0, alpha=0.97, gamma=0.1, min_frequency=0.0,
max_frequency=2.0, *args, **kwargs):
"""Initialize BatAlgorithm.
Args:
population_size (Optional[int]): Population size.
loudness (Optional[float]): Initial loudness.
pulse_rate (Optional[float]): Initial pulse rate.
alpha (Optional[float]): Parameter for controlling loudness decrease.
gamma (Optional[float]): Parameter for controlling pulse rate increase.
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.alpha = alpha
self.gamma = gamma
self.min_frequency = min_frequency
self.max_frequency = max_frequency
[docs] def set_parameters(self, population_size=20, loudness=1.0, pulse_rate=1.0, alpha=0.97, gamma=0.1, 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]): Initial loudness.
pulse_rate (Optional[float]): Initial pulse rate.
alpha (Optional[float]): Parameter for controlling loudness decrease.
gamma (Optional[float]): Parameter for controlling pulse rate increase.
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.alpha = alpha
self.gamma = gamma
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,
'alpha': self.alpha,
'gamma': self.gamma,
'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.
* alpha (float): Previous iterations loudness.
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, 'loudness': self.loudness})
return population, fitness, d
[docs] def local_search(self, best, loudness, task, **kwargs):
r"""Improve the best solution according to the Yang (2010).
Args:
best (numpy.ndarray): Global best individual.
loudness (float): Current loudness.
task (Task): Optimization task.
Returns:
numpy.ndarray: New solution based on global best individual.
"""
return task.repair(best + 0.1 * self.standard_normal(task.dimension) * loudness)
[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.
* alpha (float): Previous iterations loudness.
"""
velocities = params.pop('velocities')
loudness = params.pop('loudness') * self.alpha
pulse_rate = self.pulse_rate * (1 - np.exp(-self.gamma * task.iters))
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() < pulse_rate:
solution = self.local_search(best=best_x, loudness=loudness, 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() > 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, 'loudness': loudness}