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# encoding=utf8 |
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# pylint: disable=mixed-indentation, trailing-whitespace, line-too-long, multiple-statements, attribute-defined-outside-init, logging-not-lazy, no-self-use, arguments-differ, bad-continuation |
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import logging |
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from numpy import apply_along_axis, pi, fabs, sin, cos |
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from NiaPy.algorithms.algorithm import Algorithm |
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logging.basicConfig() |
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logger = logging.getLogger('NiaPy.algorithms.basic.SineCosineAlgorithm') |
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logger.setLevel('INFO') |
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__all__ = ['SineCosineAlgorithm'] |
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# FIXME test if algorithm realy works OK |
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class SineCosineAlgorithm(Algorithm): |
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r"""Implementation of sine cosine algorithm. |
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Algorithm: |
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Sine Cosine Algorithm |
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Date: |
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2018 |
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Authors: |
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Klemen Berkovič |
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License: |
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MIT |
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Reference URL: |
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https://www.sciencedirect.com/science/article/pii/S0950705115005043 |
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Reference paper: |
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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. |
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Attributes: |
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Name (List[str]): List of string representing algorithm names. |
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a (float): Parameter for control in :math:`r_1` value |
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Rmin (float): Minimu value for :math:`r_3` value |
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Rmax (float): Maximum value for :math:`r_3` value |
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See Also: |
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* :class:`NiaPy.algorithms.Algorithm` |
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""" |
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Name = ['SineCosineAlgorithm', 'SCA'] |
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@staticmethod |
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def algorithmInfo(): |
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r"""Get basic information of algorithm. |
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Returns: |
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str: Basic information of algorithm. |
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See Also: |
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* :func:`NiaPy.algorithms.Algorithm.algorithmInfo` |
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""" |
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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.""" |
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@staticmethod |
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def typeParameters(): |
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r"""Get dictionary with functions for checking values of parameters. |
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Returns: |
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Dict[str, Callable]: |
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* a (Callable[[Union[float, int]], bool]): TODO |
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* Rmin (Callable[[Union[float, int]], bool]): TODO |
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* Rmax (Callable[[Union[float, int]], bool]): TODO |
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See Also: |
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* :func:`NiaPy.algorithms.Algorithm.typeParameters` |
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""" |
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d = Algorithm.typeParameters() |
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d.update({ |
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'a': lambda x: isinstance(x, (float, int)) and x > 0, |
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'Rmin': lambda x: isinstance(x, (float, int)), |
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'Rmax': lambda x: isinstance(x, (float, int)) |
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}) |
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return d |
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def setParameters(self, NP=25, a=3, Rmin=0, Rmax=2, **ukwargs): |
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r"""Set the arguments of an algorithm. |
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Args: |
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NP (Optional[int]): Number of individual in population |
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a (Optional[float]): Parameter for control in :math:`r_1` value |
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Rmin (Optional[float]): Minimu value for :math:`r_3` value |
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Rmax (Optional[float]): Maximum value for :math:`r_3` value |
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See Also: |
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* :func:`NiaPy.algorithms.algorithm.Algorithm.setParameters` |
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""" |
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Algorithm.setParameters(self, NP=NP, **ukwargs) |
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self.a, self.Rmin, self.Rmax = a, Rmin, Rmax |
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if ukwargs: logger.info('Unused arguments: %s' % (ukwargs)) |
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def nextPos(self, x, x_b, r1, r2, r3, r4, task): |
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r"""Move individual to new position in search space. |
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Args: |
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x (numpy.ndarray): Individual represented with components. |
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x_b (nmppy.ndarray): Best individual represented with components. |
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r1 (float): Number dependent on algorithm iteration/generations. |
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r2 (float): Random number in range of 0 and 2 * PI. |
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r3 (float): Random number in range [Rmin, Rmax]. |
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r4 (float): Random number in range [0, 1]. |
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task (Task): Optimization task. |
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Returns: |
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numpy.ndarray: New individual that is moved based on individual ``x``. |
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""" |
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return task.repair(x + r1 * (sin(r2) if r4 < 0.5 else cos(r2)) * fabs(r3 * x_b - x), self.Rand) |
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def initPopulation(self, task): |
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r"""Initialize the individuals. |
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Args: |
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task (Task): Optimization task |
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Returns: |
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Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]: |
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1. Initialized population of individuals |
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2. Function/fitness values for individuals |
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3. Additional arguments |
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""" |
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return Algorithm.initPopulation(self, task) |
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def runIteration(self, task, P, P_f, xb, fxb, **dparams): |
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r"""Core function of Sine Cosine Algorithm. |
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Args: |
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task (Task): Optimization task. |
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P (numpy.ndarray): Current population individuals. |
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P_f (numpy.ndarray[float]): Current population individulas function/fitness values. |
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xb (numpy.ndarray): Current best solution to optimization task. |
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fxb (float): Current best function/fitness value. |
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dparams (Dict[str, Any]): Additional parameters. |
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Returns: |
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Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]: |
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1. New population. |
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2. New populations fitness/function values. |
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3. Additional arguments. |
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""" |
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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() |
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P = apply_along_axis(self.nextPos, 1, P, xb, r1, r2, r3, r4, task) |
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P_f = apply_along_axis(task.eval, 1, P) |
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return P, P_f, {} |
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# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3 |
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NiaOrg /
NiaPy
