# encoding=utf8
import logging
from numpy import apply_along_axis, pi, fabs, sin, cos
from NiaPy.algorithms.algorithm import Algorithm
logging.basicConfig()
logger = logging.getLogger('NiaPy.algorithms.basic.SineCosineAlgorithm')
logger.setLevel('INFO')
__all__ = ['SineCosineAlgorithm']
[docs]class SineCosineAlgorithm(Algorithm):
r"""Implementation of sine cosine algorithm.
Algorithm:
Sine Cosine Algorithm
Date:
2018
Authors:
Klemen Berkovič
License:
MIT
Reference URL:
https://www.sciencedirect.com/science/article/pii/S0950705115005043
Reference paper:
Seyedali Mirjalili, SCA: A Sine Cosine Algorithm for solving optimization problems, Knowledge-Based Systems, Volume 96, 2016, Pages 120-133, ISSN 0950-7051, https://doi.org/10.1016/j.knosys.2015.12.022.
Attributes:
Name (List[str]): List of string representing algorithm names.
a (float): Parameter for control in :math:`r_1` value
Rmin (float): Minimu value for :math:`r_3` value
Rmax (float): Maximum value for :math:`r_3` value
See Also:
* :class:`NiaPy.algorithms.Algorithm`
"""
Name = ['SineCosineAlgorithm', 'SCA']
[docs] @staticmethod
def algorithmInfo():
r"""Get basic information of algorithm.
Returns:
str: Basic information of algorithm.
See Also:
* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
"""
return r"""Seyedali Mirjalili, SCA: A Sine Cosine Algorithm for solving optimization problems, Knowledge-Based Systems, Volume 96, 2016, Pages 120-133, ISSN 0950-7051, https://doi.org/10.1016/j.knosys.2015.12.022."""
[docs] @staticmethod
def typeParameters():
r"""Get dictionary with functions for checking values of parameters.
Returns:
Dict[str, Callable]:
* a (Callable[[Union[float, int]], bool]): TODO
* Rmin (Callable[[Union[float, int]], bool]): TODO
* Rmax (Callable[[Union[float, int]], bool]): TODO
See Also:
* :func:`NiaPy.algorithms.Algorithm.typeParameters`
"""
d = Algorithm.typeParameters()
d.update({
'a': lambda x: isinstance(x, (float, int)) and x > 0,
'Rmin': lambda x: isinstance(x, (float, int)),
'Rmax': lambda x: isinstance(x, (float, int))
})
return d
[docs] def setParameters(self, NP=25, a=3, Rmin=0, Rmax=2, **ukwargs):
r"""Set the arguments of an algorithm.
Args:
NP (Optional[int]): Number of individual in population
a (Optional[float]): Parameter for control in :math:`r_1` value
Rmin (Optional[float]): Minimu value for :math:`r_3` value
Rmax (Optional[float]): Maximum value for :math:`r_3` value
See Also:
* :func:`NiaPy.algorithms.algorithm.Algorithm.setParameters`
"""
Algorithm.setParameters(self, NP=NP, **ukwargs)
self.a, self.Rmin, self.Rmax = a, Rmin, Rmax
[docs] def getParameters(self):
r"""Get algorithm parameters values.
Returns:
Dict[str, Any]:
See Also:
* :func:`NiaPy.algorithms.algorithm.Algorithm.getParameters`
"""
d = Algorithm.getParameters(self)
d.update({
'a': self.a,
'Rmin': self.Rmin,
'Rmax': self.Rmax
})
return d
[docs] def nextPos(self, x, x_b, r1, r2, r3, r4, task):
r"""Move individual to new position in search space.
Args:
x (numpy.ndarray): Individual represented with components.
x_b (nmppy.ndarray): Best individual represented with components.
r1 (float): Number dependent on algorithm iteration/generations.
r2 (float): Random number in range of 0 and 2 * PI.
r3 (float): Random number in range [Rmin, Rmax].
r4 (float): Random number in range [0, 1].
task (Task): Optimization task.
Returns:
numpy.ndarray: New individual that is moved based on individual ``x``.
"""
return task.repair(x + r1 * (sin(r2) if r4 < 0.5 else cos(r2)) * fabs(r3 * x_b - x), self.Rand)
[docs] def initPopulation(self, task):
r"""Initialize the individuals.
Args:
task (Task): Optimization task
Returns:
Tuple[numpy.ndarray, numpy.ndarray, Dict[str, Any]]:
1. Initialized population of individuals
2. Function/fitness values for individuals
3. Additional arguments
"""
return Algorithm.initPopulation(self, task)
[docs] def runIteration(self, task, P, P_f, xb, fxb, **dparams):
r"""Core function of Sine Cosine Algorithm.
Args:
task (Task): Optimization task.
P (numpy.ndarray): Current population individuals.
P_f (numpy.ndarray[float]): Current population individulas function/fitness values.
xb (numpy.ndarray): Current best solution to optimization task.
fxb (float): Current best function/fitness value.
dparams (Dict[str, Any]): Additional parameters.
Returns:
Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, Dict[str, Any]]:
1. New population.
2. New populations fitness/function values.
3. New global best solution.
4. New global best fitness/objective value.
5. Additional arguments.
"""
r1, r2, r3, r4 = self.a - task.Iters * (self.a / task.Iters), self.uniform(0, 2 * pi), self.uniform(self.Rmin, self.Rmax), self.rand()
P = apply_along_axis(self.nextPos, 1, P, xb, r1, r2, r3, r4, task)
P_f = apply_along_axis(task.eval, 1, P)
xb, fxb = self.getBest(P, P_f, xb, fxb)
return P, P_f, xb, fxb, {}
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