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# encoding=utf8 |
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# pylint: disable=mixed-indentation, multiple-statements, logging-not-lazy, attribute-defined-outside-init, line-too-long, arguments-differ, singleton-comparison, bad-continuation, dangerous-default-value, consider-using-enumerate, unused-argument, keyword-arg-before-vararg |
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import logging |
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from numpy import random as rand, concatenate, asarray, argsort |
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from NiaPy.algorithms.basic.de import DifferentialEvolution |
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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__ = [ |
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'AdaptiveArchiveDifferentialEvolution', |
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'CrossRandCurr2Pbest' |
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] |
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def CrossRandCurr2Pbest(pop, ic, x_b, f, cr, p=0.2, arc=None, rnd=rand, *args): |
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r"""Mutation strategy with crossover. |
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Mutation strategy uses two different random individuals from population to perform mutation. |
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Mutation: |
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Name: DE/curr2pbest/1 |
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Args: |
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pop (numpy.ndarray): Current population. |
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ic (int): Index of current individual. |
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x_b (numpy.ndarray): Global best individual. |
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f (float): Scale factor. |
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cr (float): Crossover probability. |
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p (float): Procentage of best individuals to use. |
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arc (numpy.ndarray): Achived individuals. |
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rnd (mtrand.RandomState): Random generator. |
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*args (Dict[str, Any]): Additional argumets. |
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Returns: |
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numpy.ndarray: New position. |
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""" |
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# Get random index from current population |
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pb = [1 / (len(pop) - 1) if i != ic else 0 for i in range(len(pop))] if len(pop) > 1 else None |
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r = rnd.choice(len(pop), 1, replace=not len(pop) >= 3, p=pb) |
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# Get pbest index |
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index, pi = argsort(pop), int(len(pop) * p) |
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ppop = pop[index[:pi]] |
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pb = [1 / len(ppop) for i in range(pi)] if len(ppop) > 1 else None |
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rp = rnd.choice(pi, 1, replace=not len(ppop) >= 1, p=pb) |
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# Get union population and archive index |
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apop = concatenate((pop, arc)) if arc is not None else pop |
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pb = [1 / (len(apop) - 1) if i != ic else 0 for i in range(len(apop))] if len(apop) > 1 else None |
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ra = rnd.choice(len(apop), 1, replace=not len(apop) >= 1, p=pb) |
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# Generate new positoin |
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j = rnd.randint(len(pop[ic])) |
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x = [pop[ic][i] + f * (ppop[rp[0]][i] - pop[ic][i]) + f * (pop[r[0]][i] - apop[ra[0]][i]) if rnd.rand() < cr or i == j else pop[ic][i] for i in range(len(pop[ic]))] |
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return asarray(x) |
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View Code Duplication |
class AdaptiveArchiveDifferentialEvolution(DifferentialEvolution): |
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r"""Implementation of Adaptive Differential Evolution With Optional External Archive algorithm. |
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Algorithm: |
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Adaptive Differential Evolution With Optional External Archive |
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Date: |
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2019 |
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Author: |
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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://ieeexplore.ieee.org/document/5208221 |
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Reference paper: |
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Zhang, Jingqiao, and Arthur C. Sanderson. "JADE: adaptive differential evolution with optional external archive." IEEE Transactions on evolutionary computation 13.5 (2009): 945-958. |
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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.basic.DifferentialEvolution` |
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""" |
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Name = ['AdaptiveArchiveDifferentialEvolution', 'JADE'] |
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@staticmethod |
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def algorithmInfo(): |
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r"""Get algorithm information. |
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Returns: |
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str: Alogrithm information. |
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See Also: |
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:func:`NiaPy.algorithms.algorithm.Algorithm.algorithmInfo` |
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""" |
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return r"""Zhang, Jingqiao, and Arthur C. Sanderson. "JADE: adaptive differential evolution with optional external archive." IEEE Transactions on evolutionary computation 13.5 (2009): 945-958.""" |
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def setParameters(self, **kwargs): |
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DifferentialEvolution.setParameters(self, **kwargs) |
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# TODO add parameters of the algorithm |
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def getParameters(self): |
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d = DifferentialEvolution.getParameters(self) |
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# TODO add paramters values |
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return d |
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def runIteration(self, task, pop, fpop, xb, fxb, **dparams): |
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# TODO Implement algorithm |
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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
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