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# encoding=utf8 |
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import logging |
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from numpy import full |
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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.modified') |
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logger.setLevel('INFO') |
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__all__ = ['ParameterFreeBatAlgorithm'] |
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class ParameterFreeBatAlgorithm(Algorithm): |
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r"""Implementation of Parameter-free Bat algorithm. |
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Algorithm: |
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Parameter-free Bat algorithm |
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Date: |
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2020 |
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Authors: |
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Iztok Fister Jr. |
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This implementation is based on the implementation of basic BA from NiaPy |
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License: |
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MIT |
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Reference paper: |
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Iztok Fister Jr., Iztok Fister, Xin-She Yang. Towards the development of a parameter-free bat algorithm . In: FISTER Jr., Iztok (Ed.), BRODNIK, Andrej (Ed.). StuCoSReC : proceedings of the 2015 2nd Student Computer Science Research Conference. Koper: University of Primorska, 2015, pp. 31-34. |
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Attributes: |
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Name (List[str]): List of strings representing algorithm name. |
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See Also: |
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* :class:`NiaPy.algorithms.Algorithm` |
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""" |
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Name = ['ParameterFreeBatAlgorithm', 'PLBA'] |
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@staticmethod |
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def algorithmInfo(): |
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r"""Get algorithms information. |
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Returns: |
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str: Algorithm information. |
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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"""Iztok Fister Jr., Iztok Fister, Xin-She Yang. Towards the development of a parameter-free bat algorithm . In: FISTER, Iztok (Ed.), BRODNIK, Andrej (Ed.). StuCoSReC : proceedings of the 2015 2nd Student Computer Science Research Conference. Koper: University of Primorska, 2015, pp. 31-34.""" |
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def setParameters(self, **ukwargs): |
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r"""Set the parameters of the algorithm. |
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Args: |
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A (Optional[float]): Loudness. |
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r (Optional[float]): Pulse rate. |
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See Also: |
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* :func:`NiaPy.algorithms.Algorithm.setParameters` |
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""" |
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Algorithm.setParameters(self, NP=80, **ukwargs) |
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self.A, self.r = 0.9, 0.1 |
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View Code Duplication |
def initPopulation(self, task): |
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r"""Initialize the initial population. |
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Parameters: |
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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. New population. |
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2. New population fitness/function values. |
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3. Additional arguments: |
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* S (numpy.ndarray): Solutions |
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* Q (numpy.ndarray[float]): Frequencies |
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* v (numpy.ndarray[float]): Velocities |
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See Also: |
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* :func:`NiaPy.algorithms.Algorithm.initPopulation` |
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""" |
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Sol, Fitness, d = Algorithm.initPopulation(self, task) |
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S, Q, v = full([self.NP, task.D], 0.0), full(self.NP, 0.0), full([self.NP, task.D], 0.0) |
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d.update({'S': S, 'Q': Q, 'v': v}) |
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return Sol, Fitness, d |
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def localSearch(self, best, task, **kwargs): |
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r"""Improve the best solution according to the Yang (2010). |
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Args: |
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best (numpy.ndarray): Global best individual. |
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task (Task): Optimization task. |
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**kwargs (Dict[str, Any]): Additional arguments. |
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Returns: |
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numpy.ndarray: New solution based on global best individual. |
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""" |
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return task.repair(best + 0.001 * self.normal(0, 1, task.D)) |
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def runIteration(self, task, Sol, Fitness, xb, fxb, S, Q, v, **dparams): |
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r"""Core function of Parameter-free Bat Algorithm. |
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Parameters: |
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task (Task): Optimization task. |
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Sol (numpy.ndarray): Current population |
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Fitness (numpy.ndarray[float]): Current population fitness/funciton values |
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best (numpy.ndarray): Current best individual |
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f_min (float): Current best individual function/fitness value |
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S (numpy.ndarray): Solutions |
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Q (numpy.ndarray): Frequencies |
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v (numpy.ndarray): Velocities |
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best (numpy.ndarray): Global best used by the algorithm |
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f_min (float): Global best fitness value used by the algorithm |
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dparams (Dict[str, Any]): Additional algorithm arguments |
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Returns: |
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Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, Dict[str, Any]]: |
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1. New population |
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2. New population fitness/function vlues |
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3. New global best solution |
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4. New global best fitness/objective value |
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5. Additional arguments: |
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* S (numpy.ndarray): Solutions |
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* Q (numpy.ndarray): Frequencies |
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* v (numpy.ndarray): Velocities |
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* best (numpy.ndarray): Global best |
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* f_min (float): Global best fitness |
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""" |
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upper, lower = task.bcUpper(), task.bcLower() |
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for i in range(self.NP): |
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Q[i] = ((upper[0] - lower[0]) / float(self.NP)) * self.normal(0, 1) |
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v[i] += (Sol[i] - xb) * Q[i] |
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if self.rand() > self.r: S[i] = self.localSearch(best=xb, task=task, i=i, Sol=Sol) |
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else: S[i] = task.repair(Sol[i] + v[i], rnd=self.Rand) |
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Fnew = task.eval(S[i]) |
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if (Fnew <= Fitness[i]) and (self.rand() < self.A): Sol[i], Fitness[i] = S[i], Fnew |
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if Fnew <= fxb: xb, fxb = S[i].copy(), Fnew |
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return Sol, Fitness, xb, fxb, {'S': S, 'Q': Q, 'v': v} |
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# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3 |
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NiaOrg /
NiaPy
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