NiaOrg / NiaPy

			P.append(pop[i] if pop[i].f < e.f else e)

		return asarray(P)

class DynNpDifferentialEvolution(DifferentialEvolution):

	r"""Implementation of Dynamic poulation size Differential evolution algorithm.

	Algorithm:

		Dynamic poulation size Differential evolution algorithm

	Date:

		2018

	Author:

		Klemen Berkovič

	License:

		MIT

	Attributes:

		Name (List[str]): List of strings representing algorithm names.

		pmax (int): TODO

		rp (int): TODO

	See Also:

		* :class:`NiaPy.algorithms.basic.DifferentialEvolution`

	"""

	Name = ['DynNpDifferentialEvolution', 'dynNpDE']

	@staticmethod

	def typeParameters():

		r"""Get dictionary with functions for checking values of parameters.

		Returns:

			Dict[str, Callable]:

				* rp (Callable[[Union[float, int]], bool]): TODO

				* pmax (Callable[[int], bool]): TODO

		See Also:

			* :func:`NiaPy.algorithms.basic.DifferentialEvolution.typeParameters`

		"""

		r = DifferentialEvolution.typeParameters()

		r['rp'] = lambda x: isinstance(x, (float, int)) and x > 0

		r['pmax'] = lambda x: isinstance(x, int) and x > 0

		return r

	def setParameters(self, pmax=50, rp=3, **ukwargs):

		r"""Set the algorithm parameters.

		Arguments:

			pmax (Optional[int]): TODO

			rp (Optional[int]): TODO

		See Also:

			* :func:`NiaPy.algorithms.basic.DifferentialEvolution.setParameters`

		"""

		DifferentialEvolution.setParameters(self, **ukwargs)

		self.pmax, self.rp = pmax, rp

		if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))

	def postSelection(self, pop, task, **kwargs):

		r"""Post selection operator.

		In this algorithm the post selection operator decrements the population at specific iterations/generations.

		Args:

			pop (numpy.ndarray[Individual]): Current population.

			task (Task): Optimization task.

			kwargs (Dict[str, Any]): Additional arguments.

		Returns:

			numpy.ndarray[Individual]: Changed current population.

		"""

		Gr = task.nFES // (self.pmax * len(pop)) + self.rp

		nNP = len(pop) // 2

		if task.Iters == Gr and len(pop) > 3: pop = objects2array([pop[i] if pop[i].f < pop[i + nNP].f else pop[i + nNP] for i in range(nNP)])

		return pop

def proportional(Lt_min, Lt_max, mu, x_f, avg, *args):

	r"""Proportional calculation of age of individual.

		self.mu = abs(self.LT_max - self.LT_min) / 2

		if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))

class DynNpSelfAdaptiveDifferentialEvolutionAlgorithm(SelfAdaptiveDifferentialEvolution):

	r"""Implementation of Dynamic population size self-adaptive differential evolution algorithm.

	Algorithm:

		Dynamic population size self-adaptive differential evolution algorithm

	Date:

		2018

	Author:

		Jan Popič and Klemen Berkovič

	License:

		MIT

	Reference URL:

		https://link.springer.com/article/10.1007/s10489-007-0091-x

	Reference paper:

		Brest, Janez, and Mirjam Sepesy Maučec. Population size reduction for the differential evolution algorithm. Applied Intelligence 29.3 (2008): 228-247.

	Attributes:

		Name (List[str]): List of strings representing algorithm name.

		rp (int): Small non-negative number which is added to value of genp.

		pmax (int): Number of population reductions.

	See Also:

		* :class:`NiaPy.algorithms.modified.SelfAdaptiveDifferentialEvolution`

	"""

	Name = ['DynNpSelfAdaptiveDifferentialEvolutionAlgorithm', 'dynNPjDE']

	@staticmethod

	def typeParameters():

		d = SelfAdaptiveDifferentialEvolution.typeParameters()

		d['rp'] = lambda x: isinstance(x, (float, int)) and x > 0

		d['pmax'] = lambda x: isinstance(x, int) and x > 0

		return d

	def setParameters(self, rp=0, pmax=10, **ukwargs):

		r"""Set the parameters of an algorithm.

		Arguments:

			rp (Optional[int]): Small non-negative number which is added to value of genp (if it's not divisible).

			pmax (Optional[int]): Number of population reductions.

		See Also:

			* :func:`NiaPy.algorithms.modified.SelfAdaptiveDifferentialEvolution.setParameters`

		"""

		SelfAdaptiveDifferentialEvolution.setParameters(self, **ukwargs)

		self.rp, self.pmax = rp, pmax

		if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))

	def postSelection(self, pop, task, **kwargs):

		r"""Post selection operator.

		Args:

			pop (numpy.ndarray[Individual]): Current population.

			task (Task): Optimization task.

		Returns:

			numpy.ndarray[Individual]: New population.

		"""

		Gr, nNP = task.nFES // (self.pmax * len(pop)) + self.rp, len(pop) // 2

		if task.Iters == Gr and len(pop) > 3: return objects2array([pop[i] if pop[i].f < pop[i + nNP].f else pop[i + nNP] for i in range(nNP)])

		return pop

class MultiStrategySelfAdaptiveDifferentialEvolution(SelfAdaptiveDifferentialEvolution):

	r"""Implementation of self-adaptive differential evolution algorithm with multiple mutation strategys.