# encoding=utf8
import logging
from numpy import apply_along_axis, argsort
from scipy.stats import levy
from NiaPy.algorithms.algorithm import Algorithm
logging.basicConfig()
logger = logging.getLogger('NiaPy.algorithms.basic')
logger.setLevel('INFO')
__all__ = ['CuckooSearch']
[docs]class CuckooSearch(Algorithm):
r"""Implementation of Cuckoo behaviour and levy flights.
Algorithm:
Cuckoo Search
Date:
2018
Authors:
Klemen Berkovič
License:
MIT
Reference:
Yang, Xin-She, and Suash Deb. "Cuckoo search via Lévy flights." Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on. IEEE, 2009.
Attributes:
Name (List[str]): list of strings representing algorithm names.
N (int): Population size.
pa (float): Proportion of worst nests.
alpha (float): Scale factor for levy flight.
See Also:
* :class:`NiaPy.algorithms.Algorithm`
"""
Name = ['CuckooSearch', 'CS']
[docs] @staticmethod
def algorithmInfo():
r"""Get algorithms information.
Returns:
str: Algorithm information.
See Also:
* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
"""
return r"""Yang, Xin-She, and Suash Deb. "Cuckoo search via Lévy flights." Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on. IEEE, 2009."""
[docs] @staticmethod
def typeParameters():
r"""TODO.
Returns:
Dict[str, Callable]:
* N (Callable[[int], bool]): TODO
* pa (Callable[[float], bool]): TODO
* alpha (Callable[[Union[int, float]], bool]): TODO
"""
return {
'N': lambda x: isinstance(x, int) and x > 0,
'pa': lambda x: isinstance(x, float) and 0 <= x <= 1,
'alpha': lambda x: isinstance(x, (float, int)),
}
[docs] def setParameters(self, N=50, pa=0.2, alpha=0.5, **ukwargs):
r"""Set the arguments of an algorithm.
Arguments:
N (int): Population size :math:`\in [1, \infty)`
pa (float): factor :math:`\in [0, 1]`
alpah (float): TODO
**ukwargs (Dict[str, Any]): Additional arguments
See Also:
* :func:`NiaPy.algorithms.Algorithm.setParameters`
"""
ukwargs.pop('NP', None)
Algorithm.setParameters(self, NP=N, **ukwargs)
self.pa, self.alpha = pa, alpha
[docs] def getParameters(self):
d = Algorithm.getParameters(self)
d.pop('NP', None)
d.update({
'N': self.NP,
'pa': self.pa,
'alpha': self.alpha
})
return d
[docs] def emptyNests(self, pop, fpop, pa_v, task):
r"""Empty ensts.
Args:
pop (numpy.ndarray): Current population
fpop (numpy.ndarray[float]): Current population fitness/funcion values
pa_v (): TODO.
task (Task): Optimization task
Returns:
Tuple[numpy.ndarray, numpy.ndarray[float]]:
1. New population
2. New population fitness/function values
"""
si = argsort(fpop)[:int(pa_v):-1]
pop[si] = task.Lower + self.rand(task.D) * task.bRange
fpop[si] = apply_along_axis(task.eval, 1, pop[si])
return pop, fpop
[docs] def initPopulation(self, task):
r"""Initialize starting population.
Args:
task (Task): Optimization task.
Returns:
Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]:
1. Initialized population.
2. Initialized populations fitness/function values.
3. Additional arguments:
* pa_v (float): TODO
See Also:
* :func:`NiaPy.algorithms.Algorithm.initPopulation`
"""
N, N_f, d = Algorithm.initPopulation(self, task)
d.update({'pa_v': self.NP * self.pa})
return N, N_f, d
[docs] def runIteration(self, task, pop, fpop, xb, fxb, pa_v, **dparams):
r"""Core function of CuckooSearch algorithm.
Args:
task (Task): Optimization task.
pop (numpy.ndarray): Current population.
fpop (numpy.ndarray): Current populations fitness/function values.
xb (numpy.ndarray): Global best individual.
fxb (float): Global best individual function/fitness values.
pa_v (float): TODO
**dparams (Dict[str, Any]): Additional arguments.
Returns:
Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, Dict[str, Any]]:
1. Initialized population.
2. Initialized populations fitness/function values.
3. New global best solution
4. New global best solutions fitness/objective value
5. Additional arguments:
* pa_v (float): TODO
"""
i = self.randint(self.NP)
Nn = task.repair(pop[i] + self.alpha * levy.rvs(size=[task.D], random_state=self.Rand), rnd=self.Rand)
Nn_f = task.eval(Nn)
j = self.randint(self.NP)
while i == j: j = self.randint(self.NP)
if Nn_f <= fpop[j]: pop[j], fpop[j] = Nn, Nn_f
pop, fpop = self.emptyNests(pop, fpop, pa_v, task)
xb, fxb = self.getBest(pop, fpop, xb, fxb)
return pop, fpop, xb, fxb, {'pa_v': pa_v}
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