# encoding=utf8
import logging
import numpy as np
from niapy.algorithms.algorithm import Algorithm
logging.basicConfig()
logger = logging.getLogger('niapy.algorithms.modified')
logger.setLevel('INFO')
__all__ = ['ParameterFreeBatAlgorithm']
[docs]class ParameterFreeBatAlgorithm(Algorithm):
r"""Implementation of Parameter-free Bat algorithm.
Algorithm:
Parameter-free Bat algorithm
Date:
2020
Authors:
Iztok Fister Jr.
This implementation is based on the implementation of basic BA from niapy
License:
MIT
Reference paper:
Iztok Fister Jr., Iztok Fister, Xin-She Yang. Towards the development of a parameter-free bat algorithm . In: FISTER Jr., Iztok (Ed.), BRODNIK, Andrej (Ed.). StuCoSReC : proceedings of the 2015 2nd Student Computer Science Research Conference. Koper: University of Primorska, 2015, pp. 31-34.
Attributes:
Name (List[str]): List of strings representing algorithm name.
See Also:
* :class:`niapy.algorithms.Algorithm`
"""
Name = ['ParameterFreeBatAlgorithm', 'PLBA']
[docs] @staticmethod
def info():
r"""Get algorithms information.
Returns:
str: Algorithm information.
See Also:
* :func:`niapy.algorithms.Algorithm.info`
"""
return r"""Iztok Fister Jr., Iztok Fister, Xin-She Yang. Towards the development of a parameter-free bat algorithm . In: FISTER, Iztok (Ed.), BRODNIK, Andrej (Ed.). StuCoSReC : proceedings of the 2015 2nd Student Computer Science Research Conference. Koper: University of Primorska, 2015, pp. 31-34."""
[docs] def __init__(self, *args, **kwargs):
"""Initialize ParameterFreeBatAlgorithm."""
super().__init__(80, *args, **kwargs)
self.loudness = 0.9
self.pulse_rate = 0.1
[docs] def set_parameters(self, **kwargs):
r"""Set the parameters of the algorithm.
See Also:
* :func:`niapy.algorithms.Algorithm.set_parameters`
"""
super().set_parameters(population_size=80, **kwargs)
self.loudness = 0.9
self.pulse_rate = 0.1
[docs] def init_population(self, task):
r"""Initialize the initial population.
Args:
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 = Algorithm.init_population(self, 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.normal(0, 1, task.dimension))
[docs] def run_iteration(self, task, population, population_fitness, best_x, best_fitness, **params):
r"""Core function of Parameter-free Bat Algorithm.
Args:
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')
upper, lower = task.upper, task.lower
for i in range(self.population_size):
frequency = ((upper[0] - lower[0]) / float(self.population_size)) * self.normal(0, 1)
velocities[i] += (population[i] - best_x) * frequency
if self.random() > self.pulse_rate:
solution = self.local_search(best=best_x, task=task, i=i, Sol=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}