# coding=utf-8
import logging
from numpy import array, argsort, where
from NiaPy.algorithms.algorithm import Algorithm
__all__ = ['BeesAlgorithm']
logging.basicConfig()
logger = logging.getLogger('NiaPy.algorithms.basic')
logger.setLevel('INFO')
[docs]class BeesAlgorithm(Algorithm):
r"""Implementation of Bees algorithm.
Algorithm:
The Bees algorithm
Date:
2019
Authors:
Rok Potočnik
License:
MIT
Reference paper:
DT Pham, A Ghanbarzadeh, E Koc, S Otri, S Rahim, and M Zaidi. The bees algorithm-a novel tool for complex optimisation problems. In Proceedings of the 2nd Virtual International Conference on Intelligent Production Machines and Systems (IPROMS 2006), pages 454–459, 2006
Attributes:
NP (Optional[int]): Number of scout bees parameter.
m (Optional[int]): Number of sites selected out of n visited sites parameter.
e (Optional[int]): Number of best sites out of m selected sitest parameter.
nep (Optional[int]): Number of bees recruited for best e sites parameter.
nsp (Optional[int]): Number of bees recruited for the other selected sites parameter.
ngh (Optional[float]): Initial size of patches parameter.
ukwargs (Dict[str, Any]): Additional arguments.
See Also:
* :func:`NiaPy.algorithms.Algorithm.setParameters`
"""
Name = ['BeesAlgorithm', 'BEA']
[docs] @staticmethod
def algorithmInfo():
r"""Get information about algorithm.
Returns:
str: Algorithm information
See Also:
* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
"""
return r"""DT Pham, A Ghanbarzadeh, E Koc, S Otri, S Rahim, and M Zaidi. The bees algorithm-a novel tool for complex optimisation problems. In Proceedings of the 2nd Virtual International Conference on Intelligent Production Machines and Systems (IPROMS 2006), pages 454–459, 2006"""
[docs] @staticmethod
def typeParameters():
r"""Get dictionary with functions for checking values of parameters.
Returns:
Dict[str, Callable]:
* NP (Callable[[int], bool]): Checks if number of bees parameter has a proper value.
* m (Callable[[int], bool]): Checks if number of selected sites parameter has a proper value.
* e (Callable[[int], bool]): Checks if number of elite selected sites parameter has a proper value.
* nep (Callable[[int], bool]): Checks if number of elite bees parameter has a proper value.
* nsp (Callable[[int], bool]): Checks if number of other bees parameter has a proper value.
* ngh (Callable[[float], bool]): Checks if size of patches parameter has a proper value.
See Also:
* :func:`NiaPy.algorithms.algorithm.Algorithm.typeParameters`
"""
d = Algorithm.typeParameters()
d.update({
'NP': lambda x: isinstance(x, int) and x > 0,
'm': lambda x: isinstance(x, int) and x > 0,
'e': lambda x: isinstance(x, int) and x > 0,
'nep': lambda x: isinstance(x, int) and x > 0,
'nsp': lambda x: isinstance(x, int) and x > 0,
'ngh': lambda x: isinstance(x, float) and x > 0
})
return d
[docs] def setParameters(self, NP=40, m=5, e=4, ngh=1, nep=4, nsp=2, **ukwargs):
r"""Set the parameters of the algorithm.
Args:
NP (Optional[int]): Number of scout bees parameter.
m (Optional[int]): Number of sites selected out of n visited sites parameter.
e (Optional[int]): Number of best sites out of m selected sitest parameter.
nep (Optional[int]): Number of bees recruited for best e sites parameter.
nsp (Optional[int]): Number of bees recruited for the other selected sites parameter.
ngh (Optional[float]): Initial size of patches parameter.
ukwargs (Dict[str, Any]): Additional arguments.
See Also:
* :func:`NiaPy.algorithms.Algorithm.setParameters`
"""
Algorithm.setParameters(self, NP=NP, **ukwargs)
self.n, self.m, self.e, self.nep, self.nsp, self.ngh = NP, m, e, nep, nsp, ngh
[docs] def getParameters(self):
r"""Get parameters of the algorithm.
Returns:
Dict[str, Any]:
"""
d = Algorithm.getParameters(self)
d.update({
'm': self.m,
'e': self.e,
'ngh': self.ngh,
'nep': self.nep,
'nsp': self.nsp
})
return d
[docs] def beeDance(self, x, task, ngh):
r"""Bees Dance. Search for new positions.
Args:
x (numpy.ndarray): One instance from the population.
task (Task): Optimization task.
ngh (float): A small value for patch search.
Returns:
Tuple[numpy.ndarray, float]:
1. New population.
2. New population fitness/function values.
See Also:
* :func:`NiaPy.algorithms.Algorithm.initPopulation`
"""
ind = self.randint(task.D)
y = array(x, copy=True)
y[ind] = x[ind] + self.uniform(-ngh, ngh)
y = self.repair(y, task.Lower, task.Upper)
res = task.eval(y)
return y, res
[docs] def initPopulation(self, task):
r"""Initialize the starting population.
Args:
task (Task): Optimization task
Returns:
Tuple[numpy.ndarray, numpy.ndarray, Dict[str, Any]]:
1. New population.
2. New population fitness/function values.
See Also:
* :func:`NiaPy.algorithms.Algorithm.initPopulation`
"""
BeesPosition, BeesCost, _ = Algorithm.initPopulation(self, task)
idxs = argsort(BeesCost)
BeesCost = BeesCost[idxs]
BeesPosition = BeesPosition[idxs, :]
return BeesPosition, BeesCost, {'ngh': self.ngh}
[docs] def repair(self, x, lower, upper):
r"""Truncate exceeded dimensions to the limits.
Args:
x (numpy.ndarray): Individual to repair.
lower (numpy.ndarray): Lower limits for dimensions.
upper (numpy.ndarray): Upper limits for dimensions.
Returns:
numpy.ndarray: Repaired individual.
"""
ir = where(x < lower)
x[ir] = lower[ir]
ir = where(x > upper)
x[ir] = upper[ir]
return x
[docs] def runIteration(self, task, BeesPosition, BeesCost, xb, fxb, ngh, **dparams):
r"""Core function of Forest Optimization Algorithm.
Args:
task (Task): Optimization task.
BeesPosition (numpy.ndarray[float]): Current population.
BeesCost (numpy.ndarray[float]): Current population function/fitness values.
xb (numpy.ndarray): Global best individual.
fxb (float): Global best individual fitness/function value.
ngh (float): A small value used for patches.
**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 fitness/objective value.
5. Additional arguments:
* ngh (float): A small value used for patches.
"""
for ies in range(self.e):
BestBeeCost = float('inf')
for ieb in range(self.nep):
NewBeePos, NewBeeCost = self.beeDance(BeesPosition[ies, :], task, ngh)
if NewBeeCost < BestBeeCost:
BestBeeCost = NewBeeCost
BestBeePos = NewBeePos
if BestBeeCost < BeesCost[ies]:
BeesPosition[ies, :] = BestBeePos
BeesCost[ies] = BestBeeCost
for ies in range(self.e, self.m):
BestBeeCost = float('inf')
for ieb in range(self.nsp):
NewBeePos, NewBeeCost = self.beeDance(BeesPosition[ies, :], task, ngh)
if NewBeeCost < BestBeeCost:
BestBeeCost = NewBeeCost
BestBeePos = NewBeePos
if BestBeeCost < BeesCost[ies]:
BeesPosition[ies, :] = BestBeePos
BeesCost[ies] = BestBeeCost
for ies in range(self.m, self.n):
BeesPosition[ies, :] = array(self.uniform(task.Lower, task.Upper, task.D))
BeesCost[ies] = task.eval(BeesPosition[ies, :])
idxs = argsort(BeesCost)
BeesCost = BeesCost[idxs]
BeesPosition = BeesPosition[idxs, :]
ngh = ngh * 0.95
return BeesPosition, BeesCost, BeesPosition[0].copy(), BeesCost[0], {'ngh': ngh}
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