Source code for NiaPy.algorithms.other.ts

# encoding=utf8

from numpy import random as rand

from NiaPy.algorithms.algorithm import Algorithm

__all__ = ['TabuSearch']

# TODO implement algorithm

def TabuSearchF(task, SR=None, TL_size=25, rnd=rand):
    if SR == None: SR = task.bRange
    x = rnd.uniform(task.Lower, task.Upper)
    x_f = task.eval(x)
    # while not task.stopCondI():
    # Generate neigours
    # evaluate x not in ts
    # get best of of evaluated
    # compare new best with best
    return x, x_f

class TabuSearch(Algorithm):
    r"""Implementation of Tabu Search Algorithm.

    Algorithm:
        Tabu Search Algorithm

    Date:
        2018

    Authors:
        Klemen Berkovič

    License:
        MIT

    Reference URL:
        http://www.cleveralgorithms.com/nature-inspired/stochastic/tabu_search.html

    Reference paper:

    Attributes:
        Name (List[str]): List of strings representing algorithm name.
    """
    Name = ['TabuSearch', 'TS']

    @staticmethod
    def typeParameters(): return {
        'NP': lambda x: isinstance(x, int) and x > 0
    }

    def setParameters(self, **ukwargs):
        r"""Set the algorithm parameters/arguments."""
        Algorithm.setParameters(self, **ukwargs)

    def move(self): return list()

    def runIteration(self, task, pop, fpop, xb, fxb, **dparams):
        r"""Core function of the algorithm.

        Args:
            task (Task): Optimization task.
            pop (numpy.ndarray): Current population.
            fpop (numpy.ndarray): Individuals fitness/objective values.
            xb (numpy.ndarray): Global best solution.
            fxb (float): Global best solutions fitness/objective value.
            **dparams (dict):

        Returns:
            Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, dict]:
        """
        return pop, fpop, xb, fxb, dparams