# encoding=utf8
import logging
from numpy import argsort, sum, exp, apply_along_axis, asarray, where
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): Alpha parameter.
betamin (float): Betamin parameter.
gamma (flaot): Gamma parameter.
See Also:
* :class:`NiaPy.algorithms.Algorithm`
"""
Name = ['FireflyAlgorithm', 'FA']
[docs] @staticmethod
def algorithmInfo():
r"""Get algorithms information.
Returns:
str: Algorithm information.
See Also:
* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
"""
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] @staticmethod
def typeParameters():
r"""TODO.
Returns:
Dict[str, Callable]:
* alpha (Callable[[Union[float, int]], bool]): TODO.
* betamin (Callable[[Union[float, int]], bool]): TODO.
* gamma (Callable[[Union[float, int]], bool]): TODO.
See Also:
* :func:`NiaPy.algorithms.Algorithm.typeParameters`
"""
d = Algorithm.typeParameters()
d.update({
'alpha': lambda x: isinstance(x, (float, int)) and x > 0,
'betamin': lambda x: isinstance(x, (float, int)) and x > 0,
'gamma': lambda x: isinstance(x, (float, int)) and x > 0,
})
return d
[docs] def setParameters(self, NP=20, alpha=1, betamin=1, gamma=2, **ukwargs):
r"""Set the parameters of the algorithm.
Args:
NP (Optional[int]): Population size.
alpha (Optional[float]): Alpha parameter.
betamin (Optional[float]): Betamin parameter.
gamma (Optional[flaot]): Gamma parameter.
ukwargs (Dict[str, Any]): Additional arguments.
See Also:
* :func:`NiaPy.algorithms.Algorithm.setParameters`
"""
Algorithm.setParameters(self, NP=NP, **ukwargs)
self.alpha, self.betamin, self.gamma = alpha, betamin, gamma
[docs] def alpha_new(self, 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, oFireflies, alpha, task):
r"""Move fireflies.
Args:
i (int): Index of current individual.
Fireflies (numpy.ndarray):
Intensity (numpy.ndarray):
oFireflies (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.NP):
r = sum((Fireflies[i] - Fireflies[j]) ** 2) ** (1 / 2)
if Intensity[i] <= Intensity[j]: continue
beta = (1.0 - self.betamin) * exp(-self.gamma * r ** 2.0) + self.betamin
tmpf = alpha * (self.uniform(0, 1, task.D) - 0.5) * task.bRange
Fireflies[i] = task.repair(Fireflies[i] * (1.0 - beta) + oFireflies[j] * beta + tmpf, rnd=self.Rand)
moved = True
return Fireflies[i], moved
[docs] def initPopulation(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): TODO
See Also:
* :func:`NiaPy.algorithms.Algorithm.initPopulation`
"""
Fireflies, Intensity, _ = Algorithm.initPopulation(self, task)
return Fireflies, Intensity, {'alpha': self.alpha}
[docs] def runIteration(self, task, Fireflies, Intensity, xb, fxb, alpha, **dparams):
r"""Core function of Firefly Algorithm.
Args:
task (Task): Optimization task.
Fireflies (numpy.ndarray): Current population.
Intensity (numpy.ndarray): Current population function/fitness values.
xb (numpy.ndarray): Global best individual.
fxb (float): Global best individual fitness/function value.
alpha (float): TODO.
**dparams (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): TODO
See Also:
* :func:`NiaPy.algorithms.basic.FireflyAlgorithm.move_ffa`
"""
alpha = self.alpha_new(task.nFES / self.NP, alpha)
Index = argsort(Intensity)
tmp = asarray([self.move_ffa(i, Fireflies[Index], Intensity[Index], Fireflies, alpha, task) for i in range(self.NP)])
Fireflies, evalF = asarray([tmp[i][0] for i in range(len(tmp))]), asarray([tmp[i][1] for i in range(len(tmp))])
Intensity[where(evalF)] = apply_along_axis(task.eval, 1, Fireflies[where(evalF)])
xb, fxb = self.getBest(Fireflies, Intensity, xb, fxb)
return Fireflies, Intensity, xb, fxb, {'alpha': alpha}
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