# encoding=utf8
import logging
import numpy as np
from niapy.algorithms.algorithm import Algorithm, Individual
from niapy.util import objects_to_array
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:
endurance (float): Camel endurance.
supply (float): Camel supply.
x_past (numpy.ndarray): Camel's past position.
f_past (float): Camel's past function/fitness value.
steps (int): Age of camel.
See Also:
* :class:`niapy.algorithms.Individual`
"""
def __init__(self, endurance_init=None, supply_init=None, **kwargs):
r"""Initialize the Camel.
Args:
endurance_init (Optional[float]): Starting endurance of Camel.
supply_init (Optional[float]): Stating supply of Camel.
See Also:
* :func:`niapy.algorithms.Individual.__init__`
"""
super().__init__(**kwargs)
self.endurance = endurance_init
self.endurance_past = endurance_init
self.supply = supply_init
self.supply_past = supply_init
self.x_past = self.x
self.f_past = self.f
self.temperature = None
self.steps = 0
def next_temperature(self, min_temperature, max_temperature, rng):
r"""Apply nextT function on Camel.
Args:
min_temperature (float): Minimum temperature.
max_temperature (float): Maximum temperature.
rng (numpy.random.Generator): Random number generator.
"""
self.temperature = rng.uniform(min_temperature, max_temperature)
def next_supply(self, burden_factor, max_iters):
r"""Apply nextS on Camel.
Args:
burden_factor (float): Burden factor.
max_iters (int): Number of Camel Algorithm iterations/generations.
"""
self.supply = self.supply_past * (1 - burden_factor * self.steps / max_iters)
def next_endurance(self, max_temperature, max_iters):
r"""Apply function nextE on function on Camel.
Args:
max_iters (int): Number of Camel Algorithm iterations/generations
max_temperature (float): Maximum temperature of environment
"""
self.endurance = self.endurance_past * (1 - self.temperature / max_temperature) * (1 - self.steps / max_iters)
def next_x(self, cb, endurance_init, supply_init, task, rng):
r"""Apply function next_x on Camel.
This method/function move this Camel to new position in search space.
Args:
cb (numpy.ndarray): Best Camel in population.
endurance_init (float): Starting endurance of camel.
supply_init (float): Starting supply of camel.
task (Task): Optimization task.
rng (numpy.random.Generator): Random number generator.
"""
delta = rng.uniform(-1, 1)
self.x = self.x_past + delta * (1 - (self.endurance / endurance_init)) * np.exp(
1 - self.supply / supply_init) * (
cb - self.x_past)
if not task.is_feasible(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.x.copy()
self.f_past = self.f
self.endurance_past = self.endurance
self.supply_past = self.supply
self.steps += 1
return self
def refill(self, supply=None, endurance=None):
r"""Apply this function to Camel.
Args:
supply (float): New value of Camel supply.
endurance (float): New value of Camel endurance.
"""
self.supply = supply
self.endurance = endurance
[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.
population_size (Optional[int]): Population size :math:`\in [1, \infty)`.
burden_factor (Optional[float]): Burden factor :math:`\in [0, 1]`.
death_rate (Optional[float]): Dying rate :math:`\in [0, 1]`.
visibility (Optional[float]): View range of camel.
supply_init (Optional[float]): Initial supply :math:`\in (0, \infty)`.
endurance_init (Optional[float]): Initial endurance :math:`\in (0, \infty)`.
min_temperature (Optional[float]): Minimum temperature, must be true :math:`$T_{min} < T_{max}`.
max_temperature (Optional[float]): Maximum temperature, must be true :math:`T_{min} < T_{max}`.
See Also:
* :class:`niapy.algorithms.Algorithm`
"""
Name = ['CamelAlgorithm', 'CA']
[docs] @staticmethod
def info():
r"""Get information about algorithm.
Returns:
str: Algorithm information
See Also:
* :func:`niapy.algorithms.Algorithm.info`
"""
return r'''Ali, Ramzy. (2016). Novel Optimization Algorithm Inspired by Camel Traveling Behavior.
Iraq J. Electrical and Electronic Engineering. 12. 167-177.'''
[docs] def __init__(self, population_size=50, burden_factor=0.25, death_rate=0.5, visibility=0.5, supply_init=10,
endurance_init=10, min_temperature=-10, max_temperature=10, *args, **kwargs):
r"""Initialize CamelAlgorithm.
Args:
population_size (Optional[int]): Population size :math:`\in [1, \infty)`.
burden_factor (Optional[float]): Burden factor :math:`\in [0, 1]`.
death_rate (Optional[float]): Dying rate :math:`\in [0, 1]`.
visibility (Optional[float]): View range of camel.
supply_init (Optional[float]): Initial supply :math:`\in (0, \infty)`.
endurance_init (Optional[float]): Initial endurance :math:`\in (0, \infty)`.
min_temperature (Optional[float]): Minimum temperature, must be true :math:`$T_{min} < T_{max}`.
max_temperature (Optional[float]): Maximum temperature, must be true :math:`T_{min} < T_{max}`.
See Also:
* :func:`niapy.algorithms.Algorithm.__init__`
"""
super().__init__(population_size, individual_type=kwargs.pop('individual_type', Camel),
initialization_function=kwargs.pop('initialization_function', self.init_pop), *args, **kwargs)
self.burden_factor = burden_factor
self.death_rate = death_rate
self.visibility = visibility
self.supply_init = supply_init
self.endurance_init = endurance_init
self.min_temperature = min_temperature
self.max_temperature = max_temperature
[docs] def set_parameters(self, population_size=50, burden_factor=0.25, death_rate=0.5, visibility=0.5, supply_init=10,
endurance_init=10, min_temperature=-10, max_temperature=10, **kwargs):
r"""Set the arguments of an algorithm.
Args:
population_size (Optional[int]): Population size :math:`\in [1, \infty)`.
burden_factor (Optional[float]): Burden factor :math:`\in [0, 1]`.
death_rate (Optional[float]): Dying rate :math:`\in [0, 1]`.
visibility (Optional[float]): View range of camel.
supply_init (Optional[float]): Initial supply :math:`\in (0, \infty)`.
endurance_init (Optional[float]): Initial endurance :math:`\in (0, \infty)`.
min_temperature (Optional[float]): Minimum temperature, must be true :math:`$T_{min} < T_{max}`.
max_temperature (Optional[float]): Maximum temperature, must be true :math:`T_{min} < T_{max}`.
See Also:
* :func:`niapy.algorithms.Algorithm.set_parameters`
"""
super().set_parameters(population_size=population_size, individual_type=Camel,
initialization_function=kwargs.pop('initialization_function', self.init_pop), **kwargs)
self.burden_factor = burden_factor
self.death_rate = death_rate
self.visibility = visibility
self.supply_init = supply_init
self.endurance_init = endurance_init
self.min_temperature = min_temperature
self.max_temperature = max_temperature
[docs] def get_parameters(self):
r"""Get parameters of the algorithm.
Returns:
Dict[str, Any]: Algorithm Parameters.
"""
d = super().get_parameters()
d.update({
'burden_factor': self.burden_factor,
'death_rate': self.death_rate,
'visibility': self.visibility,
'supply_init': self.supply_init,
'endurance_init': self.endurance_init,
'min_temperature': self.min_temperature,
'max_temperature': self.max_temperature
})
return d
[docs] def init_pop(self, task, population_size, rng, individual_type, **_kwargs):
r"""Initialize starting population.
Args:
task (Task): Optimization task.
population_size (int): Number of camels in population.
rng (numpy.random.Generator): Random number generator.
individual_type (Type[Individual]): Individual type.
Returns:
Tuple[numpy.ndarray[Camel], numpy.ndarray[float]]:
1. Initialize population of camels.
2. Initialized populations function/fitness values.
"""
caravan = objects_to_array(
[individual_type(endurance_init=self.endurance_init, supply_init=self.supply_init, task=task, rng=rng,
e=True) for _ in range(population_size)])
return caravan, np.asarray([c.f for c in caravan])
[docs] def walk(self, camel, best_x, task):
r"""Move the camel in search space.
Args:
camel (Camel): Camel that we want to move.
best_x (numpy.ndarray): Global best coordinates.
task (Task): Optimization task.
Returns:
Camel: Camel that moved in the search space.
"""
camel.next_temperature(self.min_temperature, self.max_temperature, self.rng)
camel.next_supply(self.burden_factor, task.max_iters)
camel.next_endurance(self.max_temperature, task.max_iters)
camel.next_x(best_x, self.endurance_init, self.supply_init, task, self.rng)
return camel
[docs] def oasis(self, c):
r"""Apply oasis function to camel.
Args:
c (Camel): Camel to apply oasis on.
Returns:
Camel: Camel with applied oasis on.
"""
if self.random() > (1 - self.visibility) and c.f < c.f_past:
c.refill(self.supply_init, self.endurance_init)
return c
[docs] def life_cycle(self, camel, task):
r"""Apply life cycle to Camel.
Args:
camel (Camel): Camel to apply life cycle.
task (Task): Optimization task.
Returns:
Camel: Camel with life cycle applied to it.
"""
if camel.f_past < self.death_rate * camel.f:
return Camel(self.endurance_init, self.supply_init, rng=self.rng, task=task)
else:
return camel.next()
[docs] def run_iteration(self, task, population, population_fitness, best_x, best_fitness, **params):
r"""Core function of Camel Algorithm.
Args:
task (Task): Optimization task.
population (numpy.ndarray[Camel]): Current population of Camels.
population_fitness (numpy.ndarray[float]): Current population fitness/function values.
best_x (numpy.ndarray): Current best Camel.
best_fitness (float): Current best Camel fitness/function value.
**params (Dict[str, Any]): Additional arguments.
Returns:
Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, 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
"""
new_caravan = objects_to_array([self.walk(c, best_x, task) for c in population])
new_caravan = objects_to_array([self.oasis(c) for c in new_caravan])
new_caravan = objects_to_array([self.life_cycle(c, task) for c in new_caravan])
new_caravan_fitness = np.asarray([c.f for c in new_caravan])
best_x, best_fitness = self.get_best(new_caravan, new_caravan_fitness, best_x, best_fitness)
return new_caravan, new_caravan_fitness, best_x, best_fitness, {}
