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# encoding=utf8 |
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import logging |
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from numpy import random as rand |
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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.other') |
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logger.setLevel('INFO') |
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__all__ = ['TabuSearch'] |
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# TODO implement algorithm |
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def TabuSearchF(task, SR=None, TL_size=25, rnd=rand): |
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if SR == None: SR = task.bRange |
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x = rnd.uniform(task.Lower, task.Upper) |
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x_f = task.eval(x) |
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# while not task.stopCondI(): |
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# Generate neigours |
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# evaluate x not in ts |
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# get best of of evaluated |
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# compare new best with best |
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return x, x_f |
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class TabuSearch(Algorithm): |
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r"""Implementation of Tabu Search Algorithm. |
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Algorithm: |
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Tabu Search 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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http://www.cleveralgorithms.com/nature-inspired/stochastic/tabu_search.html |
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Reference paper: |
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Attributes: |
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Name (List[str]): List of strings representing algorithm name. |
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""" |
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Name = ['TabuSearch', 'TS'] |
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@staticmethod |
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def typeParameters(): return { |
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'NP': lambda x: isinstance(x, int) and x > 0 |
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} |
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def setParameters(self, **ukwargs): |
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r"""Set the algorithm parameters/arguments.""" |
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Algorithm.setParameters(self, **ukwargs) |
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def move(self): return list() |
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def runIteration(self, task, pop, fpop, xb, fxb, **dparams): |
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r"""Core function of the algorithm. |
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Args: |
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task (Task): Optimization task. |
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pop (numpy.ndarray): Current population. |
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fpop (numpy.ndarray): Individuals fitness/objective values. |
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xb (numpy.ndarray): Global best solution. |
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fxb (float): Global best solutions fitness/objective value. |
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**dparams (dict): |
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Returns: |
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Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, dict]: |
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""" |
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return pop, fpop, xb, fxb, dparams |
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# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3 |
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NiaOrg /
NiaPy
