NiaPy.algorithms.basic.cro.BroodingSimple() - Code Metrics - Inspection of "Merge pull request #262 from lucijabrezocnik/maste..." - NiaOrg/NiaPy - Measure and Improve Code Quality continuously with Scrutinizer

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NiaPy.algorithms.basic.cro.BroodingSimple() A

↳ Parent: NiaPy.algorithms.basic.cro

Complexity

Conditions

Size

Total Lines	17
Code Lines	3

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	3
eloc	3
nop	5
dl	0
loc	17
rs	10
c	0
b	0
f	0

# encoding=utf8
import logging
from scipy.spatial.distance import euclidean
from numpy import apply_along_axis, argsort, where, random as rand, asarray, delete, sqrt, sum, unique, append
from NiaPy.algorithms.algorithm import Algorithm

logging.basicConfig()
logger = logging.getLogger('NiaPy.algorithms.basic')
logger.setLevel('INFO')

__all__ = ['CoralReefsOptimization']

def SexualCrossoverSimple(pop, p, task, rnd=rand, **kwargs):
	r"""Sexual reproduction of corals.

	Args:
		pop (numpy.ndarray): Current population.
		p (float): Probability in range [0, 1].
		task (Task): Optimization task.
		rnd (mtrand.RandomState): Random generator.
		**kwargs (Dict[str, Any]): Additional arguments.

	Returns:
		Tuple[numpy.ndarray, numpy.ndarray]:
			1. New population.
			2. New population function/fitness values.
	"""
	for i in range(len(pop) // 2): pop[i] = asarray([pop[i, d] if rnd.rand() < p else pop[i * 2, d] for d in range(task.D)])
	return pop, apply_along_axis(task.eval, 1, pop)

def BroodingSimple(pop, p, task, rnd=rand, **kwargs):
	r"""Brooding or internal sexual reproduction of corals.

	Args:
		pop (numpy.ndarray): Current population.
		p (float): Probability in range [0, 1].
		task (Task): Optimization task.
		rnd (mtrand.RandomState): Random generator.
		**kwargs (Dict[str, Any]): Additional arguments.

	Returns:
		Tuple[numpy.ndarray, numpy.ndarray]:
			1. New population.
			2. New population function/fitness values.
	"""
	for i in range(len(pop)): pop[i] = task.repair(asarray([pop[i, d] if rnd.rand() < p else task.Lower[d] + task.bRange[d] * rnd.rand() for d in range(task.D)]), rnd=rnd)
	return pop, apply_along_axis(task.eval, 1, pop)

def MoveCorals(pop, p, F, task, rnd=rand, **kwargs):
	r"""Move corals.

	Args:
		pop (numpy.ndarray): Current population.
		p (float): Probability in range [0, 1].
		F (float): Factor.
		task (Task): Optimization task.
		rnd (mtrand.RandomState): Random generator.
		**kwargs (Dict[str, Any]): Additional arguments.

	Returns:
		Tuple[numpy.ndarray, numpy.ndarray]:
			1. New population.
			2. New population function/fitness values.
	"""
	for i in range(len(pop)): pop[i] = task.repair(asarray([pop[i, d] if rnd.rand() < p else pop[i, d] + F * rnd.rand() for d in range(task.D)]), rnd=rnd)
	return pop, apply_along_axis(task.eval, 1, pop)

class CoralReefsOptimization(Algorithm):
	r"""Implementation of Coral Reefs Optimization Algorithm.

	Algorithm:
		Coral Reefs Optimization Algorithm

	Date:
		2018

	Authors:
		Klemen Berkovič

	License:
		MIT

	Reference Paper:
		S. Salcedo-Sanz, J. Del Ser, I. Landa-Torres, S. Gil-López, and J. A. Portilla-Figueras, “The Coral Reefs Optimization Algorithm: A Novel Metaheuristic for Efficiently Solving Optimization Problems,” The Scientific World Journal, vol. 2014, Article ID 739768, 15 pages, 2014.

	Reference URL:
		https://doi.org/10.1155/2014/739768.

	Attributes:
		Name (List[str]): List of strings representing algorithm name.
		phi (float): Range of neighborhood.
		Fa (int): Number of corals used in asexsual reproduction.
		Fb (int): Number of corals used in brooding.
		Fd (int): Number of corals used in depredation.
		k (int): Nomber of trys for larva setting.
		P_F (float): Mutation variable :math:`\in [0, \infty]`.
		P_Cr(float): Crossover rate in [0, 1].
		Distance (Callable[[numpy.ndarray, numpy.ndarray], float]): Funciton for calculating distance between corals.
		SexualCrossover (Callable[[numpy.ndarray, float, Task, mtrand.RandomState, Dict[str, Any]], Tuple[numpy.ndarray, numpy.ndarray[float]]]): Crossover function.
		Brooding (Callable[[numpy.ndarray, float, Task, mtrand.RandomState, Dict[str, Any]], Tuple[numpy.ndarray, numpy.ndarray]]): Brooding function.

	See Also:
		* :class:`NiaPy.algorithms.Algorithm`
	"""
	Name = ['CoralReefsOptimization', 'CRO']

	@staticmethod
	def algorithmInfo():
		r"""Get algorithms information.

		Returns:
			str: Algorithm information.

		See Also:
			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
		"""
		return r"""S. Salcedo-Sanz, J. Del Ser, I. Landa-Torres, S. Gil-López, and J. A. Portilla-Figueras, “The Coral Reefs Optimization Algorithm: A Novel Metaheuristic for Efficiently Solving Optimization Problems,” The Scientific World Journal, vol. 2014, Article ID 739768, 15 pages, 2014."""

	@staticmethod
	def typeParameters():
		r"""Get dictionary with functions for checking values of parameters.

		Returns:
			Dict[str, Callable]:
				* N (func): TODO
				* phi (func): TODO
				* Fa (func): TODO
				* Fb (func): TODO
				* Fd (func): TODO
				* k (func): TODO
		"""
		return {
			# TODO funkcije za testiranje
			'N': False,
			'phi': False,
			'Fa': False,
			'Fb': False,
			'Fd': False,
			'k': False
		}

	def setParameters(self, N=25, phi=0.4, Fa=0.5, Fb=0.5, Fd=0.3, k=25, P_Cr=0.5, P_F=0.36, SexualCrossover=SexualCrossoverSimple, Brooding=BroodingSimple, Distance=euclidean, **ukwargs):
		r"""Set the parameters of the algorithm.

		Arguments:
			N (int): population size for population initialization.
			phi (int): TODO.
			Fa (float): Value $\in [0, 1]$ for Asexual reproduction size.
			Fb (float): Value $\in [0, 1]$ for Brooding size.
			Fd (float): Value $\in [0, 1]$ for Depredation size.
			k (int): Trys for larvae setting.
			SexualCrossover (Callable[[numpy.ndarray, float, Task, mtrand.RandomState, Dict[str, Any]], Tuple[numpy.ndarray, numpy.ndarray]]): Crossover function.
			P_Cr (float): Crossover rate $\in [0, 1]$.
			Brooding (Callable[[numpy.ndarray, float, Task, mtrand.RandomState, Dict[str, Any]], Tuple[numpy.ndarray, numpy.ndarray]]): Brooding function.
			P_F (float): Crossover rate $\in [0, 1]$.
			Distance (Callable[[numpy.ndarray, numpy.ndarray], float]): Funciton for calculating distance between corals.

		See Also:
			* :func:`NiaPy.algorithms.Algorithm.setParameters`
		"""
		ukwargs.pop('NP', None)
		Algorithm.setParameters(self, NP=N, **ukwargs)
		self.phi, self.k, self.P_Cr, self.P_F = phi, k, P_Cr, P_F
		self.Fa, self.Fb, self.Fd = int(self.NP * Fa), int(self.NP * Fb), int(self.NP * Fd)
		self.SexualCrossover, self.Brooding, self.Distance = SexualCrossover, Brooding, Distance

	def getParameters(self):
		r"""Get parameters values of the algorithm.

		Returns:
			Dict[str, Any]: TODO.
		"""
		d = Algorithm.getParameters(self)
		d.update({
			'phi': self.phi,
			'k': self.k,
			'P_Cr': self.P_Cr,
			'P_F': self.P_F,
			'Fa': self.Fa,
			'Fd': self.Fd,
			'Fb': self.Fb
		})
		return d

	def asexualReprodution(self, Reef, Reef_f, xb, fxb, task):
		r"""Asexual reproduction of corals.

		Args:
			Reef (numpy.ndarray): Current population of reefs.
			Reef_f (numpy.ndarray): Current populations function/fitness values.
			task (Task): Optimization task.

		Returns:
			Tuple[numpy.ndarray, numpy.ndarray]:
				1. New population.
				2. New population fitness/funciton values.

		See Also:
			* :func:`NiaPy.algorithms.basic.CoralReefsOptimization.setting`
			* :func:`NiaPy.algorithms.basic.BroodingSimple`
		"""
		I = argsort(Reef_f)[:self.Fa]
		Reefn, Reefn_f = self.Brooding(Reef[I], self.P_F, task, rnd=self.Rand)
		xb, fxb = self.getBest(Reefn, Reefn_f, xb, fxb)
		Reef, Reef_f, xb, fxb = self.setting(Reef, Reef_f, Reefn, Reefn_f, xb, fxb, task)
		return Reef, Reef_f, xb, fxb

	def depredation(self, Reef, Reef_f):
		r"""Depredation operator for reefs.

		Args:
			Reef (numpy.ndarray): Current reefs.
			Reef_f (numpy.ndarray): Current reefs function/fitness values.

		Returns:
			Tuple[numpy.ndarray, numpy.ndarray]:
				1. Best individual
				2. Best individual fitness/function value
		"""
		I = argsort(Reef_f)[::-1][:self.Fd]
		return delete(Reef, I), delete(Reef_f, I)

	def setting(self, X, X_f, Xn, Xn_f, xb, fxb, task):
		r"""Operator for setting reefs.

		New reefs try to seatle to selected position in search space.
		New reefs are successful if theyr fitness values is better or if they have no reef ocupying same search space.

		Args:
			X (numpy.ndarray): Current population of reefs.
			X_f (numpy.ndarray): Current populations function/fitness values.
			Xn (numpy.ndarray): New population of reefs.
			Xn_f (array of float): New populations function/fitness values.
			xb (numpy.ndarray): Global best solution.
			fxb (float): Global best solutions fitness/objective value.
			task (Task): Optimization task.

		Returns:
			Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float]:
				1. New seatled population.
				2. New seatled population fitness/function values.
		"""
		def update(A, phi, xb, fxb):
			D = asarray([sqrt(sum((A - e) ** 2, axis=1)) for e in Xn])

			I = unique(where(D < phi)[0])
			if I.any():
				Xn[I], Xn_f[I] = MoveCorals(Xn[I], self.P_F, self.P_F, task, rnd=self.Rand)
				xb, fxb = self.getBest(Xn[I], Xn_f[I], xb, fxb)

			return xb, fxb
		for i in range(self.k):
			xb, fxb = update(X, self.phi, xb, fxb)
			xb, fxb = update(Xn, self.phi, xb, fxb)
		D = asarray([sqrt(sum((X - e) ** 2, axis=1)) for e in Xn])
		I = unique(where(D >= self.phi)[0])
		return append(X, Xn[I], 0), append(X_f, Xn_f[I], 0), xb, fxb

	def runIteration(self, task, Reef, Reef_f, xb, fxb, **dparams):
		r"""Core function of Coral Reefs Optimization algorithm.

		Args:
			task (Task): Optimization task.
			Reef (numpy.ndarray): Current population.
			Reef_f (numpy.ndarray): Current population fitness/function value.
			xb (numpy.ndarray): Global best solution.
			fxb (float): Global best solution fitness/function value.
			**dparams: Additional arguments

		Returns:
			Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, Dict[str, Any]]:
				1. New population.
				2. New population fitness/function values.
				3. New global bset solution
				4. New global best solutions fitness/objective value
				5. Additional arguments:

		See Also:
			* :func:`NiaPy.algorithms.basic.CoralReefsOptimization.SexualCrossover`
			* :func:`NiaPy.algorithms.basic.CoralReefsOptimization.Brooding`
		"""
		I = self.Rand.choice(len(Reef), size=self.Fb, replace=False)
		Reefn_s, Reefn_s_f = self.SexualCrossover(Reef[I], self.P_Cr, task, rnd=self.Rand)
		xb, fxb = self.getBest(Reefn_s, Reefn_s_f, xb, fxb)
		Reefn_b, Reffn_b_f = self.Brooding(delete(Reef, I, 0), self.P_F, task, rnd=self.Rand)
		xb, fxb = self.getBest(Reefn_s, Reefn_s_f, xb, fxb)
		Reefn, Reefn_f, xb, fxb = self.setting(Reef, Reef_f, append(Reefn_s, Reefn_b, 0), append(Reefn_s_f, Reffn_b_f, 0), xb, fxb, task)
		Reef, Reef_f, xb, fxb = self.asexualReprodution(Reefn, Reefn_f, xb, fxb, task)
		if task.Iters % self.k == 0: Reef, Reef_f = self.depredation(Reef, Reef_f)
		return Reef, Reef_f, xb, fxb, {}

# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3


1			# encoding=utf8
2			import logging
3			from scipy.spatial.distance import euclidean
4			from numpy import apply_along_axis, argsort, where, random as rand, asarray, delete, sqrt, sum, unique, append
5			from NiaPy.algorithms.algorithm import Algorithm
6
7			logging.basicConfig()
8			logger = logging.getLogger('NiaPy.algorithms.basic')
9			logger.setLevel('INFO')
10
11			__all__ = ['CoralReefsOptimization']
12
13			def SexualCrossoverSimple(pop, p, task, rnd=rand, **kwargs):
14			r"""Sexual reproduction of corals.
15
16			Args:
17			pop (numpy.ndarray): Current population.
18			p (float): Probability in range [0, 1].
19			task (Task): Optimization task.
20			rnd (mtrand.RandomState): Random generator.
21			**kwargs (Dict[str, Any]): Additional arguments.
22
23			Returns:
24			Tuple[numpy.ndarray, numpy.ndarray]:
25			1. New population.
26			2. New population function/fitness values.
27			"""
28			for i in range(len(pop) // 2): pop[i] = asarray([pop[i, d] if rnd.rand() < p else pop[i * 2, d] for d in range(task.D)])
29			return pop, apply_along_axis(task.eval, 1, pop)
30
31			def BroodingSimple(pop, p, task, rnd=rand, **kwargs):
32			r"""Brooding or internal sexual reproduction of corals.
33
34			Args:
35			pop (numpy.ndarray): Current population.
36			p (float): Probability in range [0, 1].
37			task (Task): Optimization task.
38			rnd (mtrand.RandomState): Random generator.
39			**kwargs (Dict[str, Any]): Additional arguments.
40
41			Returns:
42			Tuple[numpy.ndarray, numpy.ndarray]:
43			1. New population.
44			2. New population function/fitness values.
45			"""
46			for i in range(len(pop)): pop[i] = task.repair(asarray([pop[i, d] if rnd.rand() < p else task.Lower[d] + task.bRange[d] * rnd.rand() for d in range(task.D)]), rnd=rnd)
47			return pop, apply_along_axis(task.eval, 1, pop)
48
49			def MoveCorals(pop, p, F, task, rnd=rand, **kwargs):
50			r"""Move corals.
51
52			Args:
53			pop (numpy.ndarray): Current population.
54			p (float): Probability in range [0, 1].
55			F (float): Factor.
56			task (Task): Optimization task.
57			rnd (mtrand.RandomState): Random generator.
58			**kwargs (Dict[str, Any]): Additional arguments.
59
60			Returns:
61			Tuple[numpy.ndarray, numpy.ndarray]:
62			1. New population.
63			2. New population function/fitness values.
64			"""
65			for i in range(len(pop)): pop[i] = task.repair(asarray([pop[i, d] if rnd.rand() < p else pop[i, d] + F * rnd.rand() for d in range(task.D)]), rnd=rnd)
66			return pop, apply_along_axis(task.eval, 1, pop)
67
68			class CoralReefsOptimization(Algorithm):
69			r"""Implementation of Coral Reefs Optimization Algorithm.
70
71			Algorithm:
72			Coral Reefs Optimization Algorithm
73
74			Date:
75			2018
76
77			Authors:
78			Klemen Berkovič
79
80			License:
81			MIT
82
83			Reference Paper:
84			S. Salcedo-Sanz, J. Del Ser, I. Landa-Torres, S. Gil-López, and J. A. Portilla-Figueras, “The Coral Reefs Optimization Algorithm: A Novel Metaheuristic for Efficiently Solving Optimization Problems,” The Scientific World Journal, vol. 2014, Article ID 739768, 15 pages, 2014.
85
86			Reference URL:
87			https://doi.org/10.1155/2014/739768.
88
89			Attributes:
90			Name (List[str]): List of strings representing algorithm name.
91			phi (float): Range of neighborhood.
92			Fa (int): Number of corals used in asexsual reproduction.
93			Fb (int): Number of corals used in brooding.
94			Fd (int): Number of corals used in depredation.
95			k (int): Nomber of trys for larva setting.
96			P_F (float): Mutation variable :math:`\in [0, \infty]`.
97			P_Cr(float): Crossover rate in [0, 1].
98			Distance (Callable[[numpy.ndarray, numpy.ndarray], float]): Funciton for calculating distance between corals.
99			SexualCrossover (Callable[[numpy.ndarray, float, Task, mtrand.RandomState, Dict[str, Any]], Tuple[numpy.ndarray, numpy.ndarray[float]]]): Crossover function.
100			Brooding (Callable[[numpy.ndarray, float, Task, mtrand.RandomState, Dict[str, Any]], Tuple[numpy.ndarray, numpy.ndarray]]): Brooding function.
101
102			See Also:
103			* :class:`NiaPy.algorithms.Algorithm`
104			"""
105			Name = ['CoralReefsOptimization', 'CRO']
106
107			@staticmethod
108			def algorithmInfo():
109			r"""Get algorithms information.
110
111			Returns:
112			str: Algorithm information.
113
114			See Also:
115			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
116			"""
117			return r"""S. Salcedo-Sanz, J. Del Ser, I. Landa-Torres, S. Gil-López, and J. A. Portilla-Figueras, “The Coral Reefs Optimization Algorithm: A Novel Metaheuristic for Efficiently Solving Optimization Problems,” The Scientific World Journal, vol. 2014, Article ID 739768, 15 pages, 2014."""
118
119			@staticmethod
120			def typeParameters():
121			r"""Get dictionary with functions for checking values of parameters.
122
123			Returns:
124			Dict[str, Callable]:
125			* N (func): TODO
126			* phi (func): TODO
127			* Fa (func): TODO
128			* Fb (func): TODO
129			* Fd (func): TODO
130			* k (func): TODO
131			"""
132			return {
133			# TODO funkcije za testiranje
134			'N': False,
135			'phi': False,
136			'Fa': False,
137			'Fb': False,
138			'Fd': False,
139			'k': False
140			}
141
142			def setParameters(self, N=25, phi=0.4, Fa=0.5, Fb=0.5, Fd=0.3, k=25, P_Cr=0.5, P_F=0.36, SexualCrossover=SexualCrossoverSimple, Brooding=BroodingSimple, Distance=euclidean, **ukwargs):
143			r"""Set the parameters of the algorithm.
144
145			Arguments:
146			N (int): population size for population initialization.
147			phi (int): TODO.
148			Fa (float): Value $\in [0, 1]$ for Asexual reproduction size.
149			Fb (float): Value $\in [0, 1]$ for Brooding size.
150			Fd (float): Value $\in [0, 1]$ for Depredation size.
151			k (int): Trys for larvae setting.
152			SexualCrossover (Callable[[numpy.ndarray, float, Task, mtrand.RandomState, Dict[str, Any]], Tuple[numpy.ndarray, numpy.ndarray]]): Crossover function.
153			P_Cr (float): Crossover rate $\in [0, 1]$.
154			Brooding (Callable[[numpy.ndarray, float, Task, mtrand.RandomState, Dict[str, Any]], Tuple[numpy.ndarray, numpy.ndarray]]): Brooding function.
155			P_F (float): Crossover rate $\in [0, 1]$.
156			Distance (Callable[[numpy.ndarray, numpy.ndarray], float]): Funciton for calculating distance between corals.
157
158			See Also:
159			* :func:`NiaPy.algorithms.Algorithm.setParameters`
160			"""
161			ukwargs.pop('NP', None)
162			Algorithm.setParameters(self, NP=N, **ukwargs)
163			self.phi, self.k, self.P_Cr, self.P_F = phi, k, P_Cr, P_F
164			self.Fa, self.Fb, self.Fd = int(self.NP * Fa), int(self.NP * Fb), int(self.NP * Fd)
165			self.SexualCrossover, self.Brooding, self.Distance = SexualCrossover, Brooding, Distance
166
167			def getParameters(self):
168			r"""Get parameters values of the algorithm.
169
170			Returns:
171			Dict[str, Any]: TODO.
172			"""
173			d = Algorithm.getParameters(self)
174			d.update({
175			'phi': self.phi,
176			'k': self.k,
177			'P_Cr': self.P_Cr,
178			'P_F': self.P_F,
179			'Fa': self.Fa,
180			'Fd': self.Fd,
181			'Fb': self.Fb
182			})
183			return d
184
185			def asexualReprodution(self, Reef, Reef_f, xb, fxb, task):
186			r"""Asexual reproduction of corals.
187
188			Args:
189			Reef (numpy.ndarray): Current population of reefs.
190			Reef_f (numpy.ndarray): Current populations function/fitness values.
191			task (Task): Optimization task.
192
193			Returns:
194			Tuple[numpy.ndarray, numpy.ndarray]:
195			1. New population.
196			2. New population fitness/funciton values.
197
198			See Also:
199			* :func:`NiaPy.algorithms.basic.CoralReefsOptimization.setting`
200			* :func:`NiaPy.algorithms.basic.BroodingSimple`
201			"""
202			I = argsort(Reef_f)[:self.Fa]
203			Reefn, Reefn_f = self.Brooding(Reef[I], self.P_F, task, rnd=self.Rand)
204			xb, fxb = self.getBest(Reefn, Reefn_f, xb, fxb)
205			Reef, Reef_f, xb, fxb = self.setting(Reef, Reef_f, Reefn, Reefn_f, xb, fxb, task)
206			return Reef, Reef_f, xb, fxb
207
208			def depredation(self, Reef, Reef_f):
209			r"""Depredation operator for reefs.
210
211			Args:
212			Reef (numpy.ndarray): Current reefs.
213			Reef_f (numpy.ndarray): Current reefs function/fitness values.
214
215			Returns:
216			Tuple[numpy.ndarray, numpy.ndarray]:
217			1. Best individual
218			2. Best individual fitness/function value
219			"""
220			I = argsort(Reef_f)[::-1][:self.Fd]
221			return delete(Reef, I), delete(Reef_f, I)
222
223			def setting(self, X, X_f, Xn, Xn_f, xb, fxb, task):
224			r"""Operator for setting reefs.
225
226			New reefs try to seatle to selected position in search space.
227			New reefs are successful if theyr fitness values is better or if they have no reef ocupying same search space.
228
229			Args:
230			X (numpy.ndarray): Current population of reefs.
231			X_f (numpy.ndarray): Current populations function/fitness values.
232			Xn (numpy.ndarray): New population of reefs.
233			Xn_f (array of float): New populations function/fitness values.
234			xb (numpy.ndarray): Global best solution.
235			fxb (float): Global best solutions fitness/objective value.
236			task (Task): Optimization task.
237
238			Returns:
239			Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float]:
240			1. New seatled population.
241			2. New seatled population fitness/function values.
242			"""
243			def update(A, phi, xb, fxb):
244			D = asarray([sqrt(sum((A - e) ** 2, axis=1)) for e in Xn])
			0 ignored issues – show Comprehensibility Best Practice introduced 2019-04-15 10:29 UTC by Report Bug Copy Issue Report The variable `Xn` does not seem to be defined. Loading history...
245			I = unique(where(D < phi)[0])
246			if I.any():
247			Xn[I], Xn_f[I] = MoveCorals(Xn[I], self.P_F, self.P_F, task, rnd=self.Rand)
248			xb, fxb = self.getBest(Xn[I], Xn_f[I], xb, fxb)
			0 ignored issues – show Comprehensibility Best Practice introduced 2019-11-10 16:54 UTC by Report Bug Copy Issue Report The variable `Xn_f` does not seem to be defined. Loading history...
249			return xb, fxb
250			for i in range(self.k):
251			xb, fxb = update(X, self.phi, xb, fxb)
252			xb, fxb = update(Xn, self.phi, xb, fxb)
253			D = asarray([sqrt(sum((X - e) ** 2, axis=1)) for e in Xn])
254			I = unique(where(D >= self.phi)[0])
255			return append(X, Xn[I], 0), append(X_f, Xn_f[I], 0), xb, fxb
256
257			def runIteration(self, task, Reef, Reef_f, xb, fxb, **dparams):
258			r"""Core function of Coral Reefs Optimization algorithm.
259
260			Args:
261			task (Task): Optimization task.
262			Reef (numpy.ndarray): Current population.
263			Reef_f (numpy.ndarray): Current population fitness/function value.
264			xb (numpy.ndarray): Global best solution.
265			fxb (float): Global best solution fitness/function value.
266			**dparams: Additional arguments
267
268			Returns:
269			Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, Dict[str, Any]]:
270			1. New population.
271			2. New population fitness/function values.
272			3. New global bset solution
273			4. New global best solutions fitness/objective value
274			5. Additional arguments:
275
276			See Also:
277			* :func:`NiaPy.algorithms.basic.CoralReefsOptimization.SexualCrossover`
278			* :func:`NiaPy.algorithms.basic.CoralReefsOptimization.Brooding`
279			"""
280			I = self.Rand.choice(len(Reef), size=self.Fb, replace=False)
281			Reefn_s, Reefn_s_f = self.SexualCrossover(Reef[I], self.P_Cr, task, rnd=self.Rand)
282			xb, fxb = self.getBest(Reefn_s, Reefn_s_f, xb, fxb)
283			Reefn_b, Reffn_b_f = self.Brooding(delete(Reef, I, 0), self.P_F, task, rnd=self.Rand)
284			xb, fxb = self.getBest(Reefn_s, Reefn_s_f, xb, fxb)
285			Reefn, Reefn_f, xb, fxb = self.setting(Reef, Reef_f, append(Reefn_s, Reefn_b, 0), append(Reefn_s_f, Reffn_b_f, 0), xb, fxb, task)
286			Reef, Reef_f, xb, fxb = self.asexualReprodution(Reefn, Reefn_f, xb, fxb, task)
287			if task.Iters % self.k == 0: Reef, Reef_f = self.depredation(Reef, Reef_f)
288			return Reef, Reef_f, xb, fxb, {}
289
290			# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
291

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NiaPy.algorithms.basic.cro.BroodingSimple() A

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