# encoding=utf8
import logging
from numpy import exp, random as rand, asarray
from NiaPy.algorithms.algorithm import Algorithm, Individual
from NiaPy.util.utility import objects2array
logging.basicConfig()
logger = logging.getLogger('NiaPy.algorithms.basic')
logger.setLevel('INFO')
__all__ = ['CamelAlgorithm']
class Camel(Individual):
r"""Implementation of population individual that is a camel for Camel algorithm.
Algorithm:
Camel algorithm
Date:
2018
Authors:
Klemen Berkovič
License:
MIT
Attributes:
E (float): Camel endurance.
S (float): Camel supply.
x_past (numpy.ndarray): Camel's past position.
f_past (float): Camel's past funciton/fitness value.
steps (int): Age of camel.
See Also:
* :class:`NiaPy.algorithms.Individual`
"""
def __init__(self, E_init=None, S_init=None, **kwargs):
r"""Initialize the Camel.
Args:
E_init (Optional[float]): Starting endurance of Camel.
S_init (Optional[float]): Stating supply of Camel.
**kwargs (Dict[str, Any]): Additional arguments.
See Also:
* :func:`NiaPy.algorithms.Individual.__init__`
"""
Individual.__init__(self, **kwargs)
self.E, self.E_past = E_init, E_init
self.S, self.S_past = S_init, S_init
self.x_past, self.f_past = self.x, self.f
self.steps = 0
def nextT(self, T_min, T_max, rnd=rand):
r"""Apply nextT function on Camel.
Args:
T_min (float): TODO
T_max (float): TODO
rnd (Optional[mtrand.RandomState]): Random number generator.
"""
self.T = (T_max - T_min) * rnd.rand() + T_min
def nextS(self, omega, n_gens):
r"""Apply nextS on Camel.
Args:
omega (float): TODO.
n_gens (int): Number of Camel Algorithm iterations/generations.
"""
self.S = self.S_past * (1 - omega * self.steps / n_gens)
def nextE(self, n_gens, T_max):
r"""Apply function nextE on function on Camel.
Args:
n_gens (int): Number of Camel Algorithm iterations/generations
T_max (float): Maximum temperature of environment
"""
self.E = self.E_past * (1 - self.T / T_max) * (1 - self.steps / n_gens)
def nextX(self, cb, E_init, S_init, task, rnd=rand):
r"""Apply function nextX on Camel.
This method/function move this Camel to new position in search space.
Args:
cb (Camel): Best Camel in population.
E_init (float): Starting endurance of camel.
S_init (float): Starting supply of camel.
task (Task): Optimization task.
rnd (Optional[mtrand.RandomState]): Random number generator.
"""
delta = -1 + rnd.rand() * 2
self.x = self.x_past + delta * (1 - (self.E / E_init)) * exp(1 - self.S / S_init) * (cb - self.x_past)
if not task.isFeasible(self.x): self.x = self.x_past
else: self.f = task.eval(self.x)
def next(self):
r"""Save new position of Camel to old position."""
self.x_past, self.f_past, self.E_past, self.S_past = self.x.copy(), self.f, self.E, self.S
self.steps += 1
return self
def refill(self, S=None, E=None):
r"""Apply this function to Camel.
Args:
S (float): New value of Camel supply.
E (float): New value of Camel endurance.
"""
self.S, self.E = S, E
[docs]class CamelAlgorithm(Algorithm):
r"""Implementation of Camel traveling behavior.
Algorithm:
Camel algorithm
Date:
2018
Authors:
Klemen Berkovič
License:
MIT
Reference URL:
https://www.iasj.net/iasj?func=fulltext&aId=118375
Reference paper:
Ali, Ramzy. (2016). Novel Optimization Algorithm Inspired by Camel Traveling Behavior. Iraq J. Electrical and Electronic Engineering. 12. 167-177.
Attributes:
Name (List[str]): List of strings representing name of the algorithm.
T_min (float): Minimal temperature of environment.
T_max (float): Maximal temperature of environment.
E_init (float): Starting value of energy.
S_init (float): Starting value of supplys.
See Also:
* :class:`NiaPy.algorithms.Algorithm`
"""
Name = ['CamelAlgorithm', 'CA']
[docs] @staticmethod
def algorithmInfo():
r"""Get information about algorithm.
Returns:
str: Algorithm information
See Also:
* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
"""
return r'''Ali, Ramzy. (2016). Novel Optimization Algorithm Inspired by Camel Traveling Behavior. Iraq J. Electrical and Electronic Engineering. 12. 167-177.'''
[docs] @staticmethod
def typeParameters():
r"""Get dictionary with functions for checking values of parameters.
Returns:
Dict[str, Callable]:
* omega (Callable[[Union[int, float]], bool])
* mu (Callable[[float], bool])
* alpha (Callable[[float], bool])
* S_init (Callable[[Union[float, int]], bool])
* E_init (Callable[[Union[float, int]], bool])
* T_min (Callable[[Union[float, int], bool])
* T_max (Callable[[Union[float, int], bool])
See Also:
* :func:`NiaPy.algorithms.Algorithm.typeParameters`
"""
d = Algorithm.typeParameters()
d.update({
'omega': lambda x: isinstance(x, (float, int)),
'mu': lambda x: isinstance(x, float) and 0 <= x <= 1,
'alpha': lambda x: isinstance(x, float) and 0 <= x <= 1,
'S_init': lambda x: isinstance(x, (float, int)) and x > 0,
'E_init': lambda x: isinstance(x, (float, int)) and x > 0,
'T_min': lambda x: isinstance(x, (float, int)) and x > 0,
'T_max': lambda x: isinstance(x, (float, int)) and x > 0
})
return d
[docs] def setParameters(self, NP=50, omega=0.25, mu=0.5, alpha=0.5, S_init=10, E_init=10, T_min=-10, T_max=10, **ukwargs):
r"""Set the arguments of an algorithm.
Arguments:
NP (Optional[int]): Population size :math:`\in [1, \infty)`.
T_min (Optional[float]): Minimum temperature, must be true :math:`$T_{min} < T_{max}`.
T_max (Optional[float]): Maximum temperature, must be true :math:`T_{min} < T_{max}`.
omega (Optional[float]): Burden factor :math:`\in [0, 1]`.
mu (Optional[float]): Dying rate :math:`\in [0, 1]`.
S_init (Optional[float]): Initial supply :math:`\in (0, \infty)`.
E_init (Optional[float]): Initial endurance :math:`\in (0, \infty)`.
See Also:
* :func:`NiaPy.algorithms.Algorithm.setParameters`
"""
Algorithm.setParameters(self, NP=NP, itype=Camel, InitPopFunc=ukwargs.pop('InitPopFunc', self.initPop), **ukwargs)
self.omega, self.mu, self.alpha, self.S_init, self.E_init, self.T_min, self.T_max = omega, mu, alpha, S_init, E_init, T_min, T_max
[docs] def getParameters(self):
r"""Get parameters of the algorithm.
Returns:
Dict[str, Any]:
"""
d = Algorithm.getParameters(self)
d.update({
'omega': self.omega,
'mu': self.mu,
'alpha': self.alpha,
'S_init': self.S_init,
'E_init': self.E_init,
'T_min': self.T_min,
'T_max': self.T_max
})
return d
[docs] def initPop(self, task, NP, rnd, itype, **kwargs):
r"""Initialize starting population.
Args:
task (Task): Optimization task.
NP (int): Number of camels in population.
rnd (mtrand.RandomState): Random number generator.
itype (Individual): Individual type.
**kwargs (Dict[str, Any]): Additional arguments.
Returns:
Tuple[numpy.ndarray[Camel], numpy.ndarray[float]]:
1. Initialize population of camels.
2. Initialized populations function/fitness values.
"""
caravan = objects2array([itype(E_init=self.E_init, S_init=self.S_init, task=task, rnd=rnd, e=True) for _ in range(NP)])
return caravan, asarray([c.f for c in caravan])
[docs] def walk(self, c, cb, task):
r"""Move the camel in search space.
Args:
c (Camel): Camel that we want to move.
cb (Camel): Best know camel.
task (Task): Optimization task.
Returns:
Camel: Camel that moved in the search space.
"""
c.nextT(self.T_min, self.T_max, self.Rand)
c.nextS(self.omega, task.nGEN)
c.nextE(task.nGEN, self.T_max)
c.nextX(cb, self.E_init, self.S_init, task, self.Rand)
return c
[docs] def oasis(self, c, rn, alpha):
r"""Apply oasis function to camel.
Args:
c (Camel): Camel to apply oasis on.
rn (float): Random number.
alpha (float): View range of Camel.
Returns:
Camel: Camel with appliyed oasis on.
"""
if rn > 1 - alpha and c.f < c.f_past: c.refill(self.S_init, self.E_init)
return c
[docs] def lifeCycle(self, c, mu, task):
r"""Apply life cycle to Camel.
Args:
c (Camel): Camel to apply life cycle.
mu (float): Vision range of camel.
task (Task): Optimization task.
Returns:
Camel: Camel with life cycle applyed to it.
"""
if c.f_past < mu * c.f: return Camel(self.E_init, self.S_init, rnd=self.Rand, task=task)
else: return c.next()
[docs] def initPopulation(self, task):
r"""Initialize population.
Args:
task (Task): Optimization taks.
Returns:
Tuple[numpy.ndarray[Camel], numpy.ndarray[float], dict]:
1. New population of Camels.
2. New population fitness/function values.
3. Additional arguments.
See Also:
* :func:`NiaPy.algorithms.Algorithm.initPopulation`
"""
caravan, fcaravan, _ = Algorithm.initPopulation(self, task)
return caravan, fcaravan, {}
[docs] def runIteration(self, task, caravan, fcaravan, cb, fcb, **dparams):
r"""Core function of Camel Algorithm.
Args:
task (Task): Optimization task.
caravan (numpy.ndarray[Camel]): Current population of Camels.
fcaravan (numpy.ndarray[float]): Current population fitness/function values.
cb (Camel): Current best Camel.
fcb (float): Current best Camel fitness/function value.
**dparams (Dict[str, Any]): Additional arguments.
Returns:
Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, folat, dict]:
1. New population
2. New population function/fitness value
3. New global best solution
4. New global best fitness/objective value
5. Additional arguments
"""
ncaravan = objects2array([self.walk(c, cb, task) for c in caravan])
ncaravan = objects2array([self.oasis(c, self.rand(), self.alpha) for c in ncaravan])
ncaravan = objects2array([self.lifeCycle(c, self.mu, task) for c in ncaravan])
fncaravan = asarray([c.f for c in ncaravan])
cb, fcb = self.getBest(ncaravan, fncaravan, cb, fcb)
return ncaravan, fncaravan, cb, fcb, {}
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