# encoding=utf8
import logging
import numpy as np
from niapy.algorithms.algorithm import Algorithm
__all__ = ['FireflyAlgorithm']
logging.basicConfig()
logger = logging.getLogger('niapy.algorithms.basic')
logger.setLevel('INFO')
[docs]class FireflyAlgorithm(Algorithm):
r"""Implementation of Firefly algorithm.
Algorithm:
Firefly algorithm
Date:
2016
Authors:
Iztok Fister Jr, Iztok Fister and Klemen Berkovič
License:
MIT
Reference paper:
Fister, I., Fister Jr, I., Yang, X. S., & Brest, J. (2013).
A comprehensive review of firefly algorithms. Swarm and Evolutionary Computation, 13, 34-46.
Attributes:
Name (List[str]): List of strings representing algorithm name.
alpha (float): Step size.
beta_min (float): Minimum value for beta.
gamma (float): Absorption coefficient.
See Also:
* :class:`niapy.algorithms.Algorithm`
"""
Name = ['FireflyAlgorithm', 'FA']
[docs] @staticmethod
def info():
r"""Get algorithms information.
Returns:
str: Algorithm information.
See Also:
* :func:`niapy.algorithms.Algorithm.info`
"""
return r"""Fister, I., Fister Jr, I., Yang, X. S., & Brest, J. (2013). A comprehensive review of firefly algorithms. Swarm and Evolutionary Computation, 13, 34-46."""
[docs] def __init__(self, population_size=20, alpha=1, beta_min=1, gamma=2, *args, **kwargs):
"""Initialize FireflyAlgorithm.
Args:
population_size (Optional[int]): Population size.
alpha (Optional[float]): Step size.
beta_min (Optional[float]): Minimum value of beta.
gamma (Optional[float]): Absorption coefficient.
kwargs (Dict[str, Any]): Additional arguments.
See Also:
* :func:`niapy.algorithms.Algorithm.__init__`
"""
super().__init__(population_size, *args, **kwargs)
self.alpha = alpha
self.beta_min = beta_min
self.gamma = gamma
[docs] def set_parameters(self, population_size=20, alpha=1, beta_min=1, gamma=2, **kwargs):
r"""Set the parameters of the algorithm.
Args:
population_size (Optional[int]): Population size.
alpha (Optional[float]): Step size.
beta_min (Optional[float]): Minimum value of beta.
gamma (Optional[float]): Absorption coefficient.
kwargs (Dict[str, Any]): Additional arguments.
See Also:
* :func:`niapy.algorithms.Algorithm.set_parameters`
"""
Algorithm.set_parameters(self, population_size=population_size, **kwargs)
self.alpha = alpha
self.beta_min = beta_min
self.gamma = gamma
[docs] @staticmethod
def alpha_new(a, alpha):
r"""Optionally recalculate the new alpha value.
Args:
a (float):
alpha (float):
Returns:
float: New value of parameter alpha.
"""
delta = 1.0 - pow(pow(10.0, -4.0) / 0.9, 1.0 / float(a))
return (1 - delta) * alpha
[docs] def move_ffa(self, i, fireflies, intensity, o_fireflies, alpha, task):
r"""Move fireflies.
Args:
i (int): Index of current individual.
fireflies (numpy.ndarray):
intensity (numpy.ndarray):
o_fireflies (numpy.ndarray):
alpha (float):
task (Task): Optimization task.
Returns:
Tuple[numpy.ndarray, bool]:
1. New individual
2. ``True`` if individual was moved, ``False`` if individual was not moved
"""
moved = False
for j in range(self.population_size):
r = np.sum((fireflies[i] - fireflies[j]) ** 2) ** (1 / 2)
if intensity[i] <= intensity[j]:
continue
beta = (1.0 - self.beta_min) * np.exp(-self.gamma * r ** 2.0) + self.beta_min
tmp_f = alpha * (self.random(task.dimension) - 0.5) * task.range
fireflies[i] = task.repair(fireflies[i] * (1.0 - beta) + o_fireflies[j] * beta + tmp_f, rng=self.rng)
moved = True
return fireflies[i], moved
[docs] def init_population(self, task):
r"""Initialize the starting 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:
* alpha (float): Step size.
See Also:
* :func:`niapy.algorithms.Algorithm.init_population`
"""
fireflies, intensity, _ = Algorithm.init_population(self, task)
return fireflies, intensity, {'alpha': self.alpha}
[docs] def run_iteration(self, task, population, population_fitness, best_x, best_fitness, **params):
r"""Core function of Firefly Algorithm.
Args:
task (Task): Optimization task.
population (numpy.ndarray): Current population.
population_fitness (numpy.ndarray): Current population function/fitness values.
best_x (numpy.ndarray): Global best individual.
best_fitness (float): Global best individual fitness/function value.
**params (Dict[str, Any]): Additional 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 solutions fitness/objective value
5. Additional arguments:
* alpha (float): Step size.
See Also:
* :func:`niapy.algorithms.basic.FireflyAlgorithm.move_ffa`
"""
alpha = params.pop('alpha')
alpha = self.alpha_new(task.max_evals / self.population_size, alpha)
sorted_index = np.argsort(population_fitness)
tmp = [self.move_ffa(i, population[sorted_index], population_fitness[sorted_index], population, alpha, task)
for i in range(self.population_size)]
population = np.asarray([tmp[i][0] for i in range(len(tmp))])
moved = np.asarray([tmp[i][1] for i in range(len(tmp))])
population_fitness[np.where(moved)] = np.apply_along_axis(task.eval, 1, population[np.where(moved)])
best_x, best_fitness = self.get_best(population, population_fitness, best_x, best_fitness)
return population, population_fitness, best_x, best_fitness, {'alpha': alpha}
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