NiaPy.algorithms.basic.cro.CoralReefsOptimization.getParameters() - Code Metrics - Inspection of "Development" - NiaOrg/NiaPy - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — master (#233)

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created 2019-11-11 18:30 UTC

CoralReefsOptimization.getParameters() A

↳ Parent: NiaPy.algorithms.basic.cro

Complexity

Conditions

Size

Total Lines	17
Code Lines	11

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	11
nop	1
dl	0
loc	17
rs	9.85
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 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 typeParameters():
109			r"""Get dictionary with functions for checking values of parameters.
110
111			Returns:
112			Dict[str, Callable]:
113			* N (func): TODO
114			* phi (func): TODO
115			* Fa (func): TODO
116			* Fb (func): TODO
117			* Fd (func): TODO
118			* k (func): TODO
119			"""
120			return {
121			# TODO funkcije za testiranje
122			'N': False,
123			'phi': False,
124			'Fa': False,
125			'Fb': False,
126			'Fd': False,
127			'k': False
128			}
129
130			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):
131			r"""Set the parameters of the algorithm.
132
133			Arguments:
134			N (int): population size for population initialization.
135			phi (int): TODO.
136			Fa (float): Value $\in [0, 1]$ for Asexual reproduction size.
137			Fb (float): Value $\in [0, 1]$ for Brooding size.
138			Fd (float): Value $\in [0, 1]$ for Depredation size.
139			k (int): Trys for larvae setting.
140			SexualCrossover (Callable[[numpy.ndarray, float, Task, mtrand.RandomState, Dict[str, Any]], Tuple[numpy.ndarray, numpy.ndarray]]): Crossover function.
141			P_Cr (float): Crossover rate $\in [0, 1]$.
142			Brooding (Callable[[numpy.ndarray, float, Task, mtrand.RandomState, Dict[str, Any]], Tuple[numpy.ndarray, numpy.ndarray]]): Brooding function.
143			P_F (float): Crossover rate $\in [0, 1]$.
144			Distance (Callable[[numpy.ndarray, numpy.ndarray], float]): Funciton for calculating distance between corals.
145
146			See Also:
147			* :func:`NiaPy.algorithms.Algorithm.setParameters`
148			"""
149			ukwargs.pop('NP', None)
150			Algorithm.setParameters(self, NP=N, **ukwargs)
151			self.phi, self.k, self.P_Cr, self.P_F = phi, k, P_Cr, P_F
152			self.Fa, self.Fb, self.Fd = int(self.NP * Fa), int(self.NP * Fb), int(self.NP * Fd)
153			self.SexualCrossover, self.Brooding, self.Distance = SexualCrossover, Brooding, Distance
154
155			def getParameters(self):
156			r"""Get parameters values of the algorithm.
157
158			Returns:
159			Dict[str, Any]: TODO.
160			"""
161			d = Algorithm.getParameters(self)
162			d.update({
163			'phi': self.phi,
164			'k': self.k,
165			'P_Cr': self.P_Cr,
166			'P_F': self.P_F,
167			'Fa': self.Fa,
168			'Fd': self.Fd,
169			'Fb': self.Fb
170			})
171			return d
172
173			def asexualReprodution(self, Reef, Reef_f, xb, fxb, task):
174			r"""Asexual reproduction of corals.
175
176			Args:
177			Reef (numpy.ndarray): Current population of reefs.
178			Reef_f (numpy.ndarray): Current populations function/fitness values.
179			task (Task): Optimization task.
180
181			Returns:
182			Tuple[numpy.ndarray, numpy.ndarray]:
183			1. New population.
184			2. New population fitness/funciton values.
185
186			See Also:
187			* :func:`NiaPy.algorithms.basic.CoralReefsOptimization.setting`
188			* :func:`NiaPy.algorithms.basic.BroodingSimple`
189			"""
190			I = argsort(Reef_f)[:self.Fa]
191			Reefn, Reefn_f = self.Brooding(Reef[I], self.P_F, task, rnd=self.Rand)
192			xb, fxb = self.getBest(Reefn, Reefn_f, xb, fxb)
193			Reef, Reef_f, xb, fxb = self.setting(Reef, Reef_f, Reefn, Reefn_f, xb, fxb, task)
194			return Reef, Reef_f, xb, fxb
195
196			def depredation(self, Reef, Reef_f):
197			r"""Depredation operator for reefs.
198
199			Args:
200			Reef (numpy.ndarray): Current reefs.
201			Reef_f (numpy.ndarray): Current reefs function/fitness values.
202
203			Returns:
204			Tuple[numpy.ndarray, numpy.ndarray]:
205			1. Best individual
206			2. Best individual fitness/function value
207			"""
208			I = argsort(Reef_f)[::-1][:self.Fd]
209			return delete(Reef, I), delete(Reef_f, I)
210
211			def setting(self, X, X_f, Xn, Xn_f, xb, fxb, task):
212			r"""Operator for setting reefs.
213
214			New reefs try to seatle to selected position in search space.
215			New reefs are successful if theyr fitness values is better or if they have no reef ocupying same search space.
216
217			Args:
218			X (numpy.ndarray): Current population of reefs.
219			X_f (numpy.ndarray): Current populations function/fitness values.
220			Xn (numpy.ndarray): New population of reefs.
221			Xn_f (array of float): New populations function/fitness values.
222			xb (numpy.ndarray): Global best solution.
223			fxb (float): Global best solutions fitness/objective value.
224			task (Task): Optimization task.
225
226			Returns:
227			Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float]:
228			1. New seatled population.
229			2. New seatled population fitness/function values.
230			"""
231			def update(A, phi, xb, fxb):
232			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...
233			I = unique(where(D < phi)[0])
234			if I.any():
235			Xn[I], Xn_f[I] = MoveCorals(Xn[I], self.P_F, self.P_F, task, rnd=self.Rand)
236			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...
237			return xb, fxb
238			for i in range(self.k):
239			xb, fxb = update(X, self.phi, xb, fxb)
240			xb, fxb = update(Xn, self.phi, xb, fxb)
241			D = asarray([sqrt(sum((X - e) ** 2, axis=1)) for e in Xn])
242			I = unique(where(D >= self.phi)[0])
243			return append(X, Xn[I], 0), append(X_f, Xn_f[I], 0), xb, fxb
244
245			def runIteration(self, task, Reef, Reef_f, xb, fxb, **dparams):
246			r"""Core function of Coral Reefs Optimization algorithm.
247
248			Args:
249			task (Task): Optimization task.
250			Reef (numpy.ndarray): Current population.
251			Reef_f (numpy.ndarray): Current population fitness/function value.
252			xb (numpy.ndarray): Global best solution.
253			fxb (float): Global best solution fitness/function value.
254			**dparams: Additional arguments
255
256			Returns:
257			Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, Dict[str, Any]]:
258			1. New population.
259			2. New population fitness/function values.
260			3. New global bset solution
261			4. New global best solutions fitness/objective value
262			5. Additional arguments:
263
264			See Also:
265			* :func:`NiaPy.algorithms.basic.CoralReefsOptimization.SexualCrossover`
266			* :func:`NiaPy.algorithms.basic.CoralReefsOptimization.Brooding`
267			"""
268			I = self.Rand.choice(len(Reef), size=self.Fb, replace=False)
269			Reefn_s, Reefn_s_f = self.SexualCrossover(Reef[I], self.P_Cr, task, rnd=self.Rand)
270			xb, fxb = self.getBest(Reefn_s, Reefn_s_f, xb, fxb)
271			Reefn_b, Reffn_b_f = self.Brooding(delete(Reef, I, 0), self.P_F, task, rnd=self.Rand)
272			xb, fxb = self.getBest(Reefn_s, Reefn_s_f, xb, fxb)
273			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)
274			Reef, Reef_f, xb, fxb = self.asexualReprodution(Reefn, Reefn_f, xb, fxb, task)
275			if task.Iters % self.k == 0: Reef, Reef_f = self.depredation(Reef, Reef_f)
276			return Reef, Reef_f, xb, fxb, {}
277
278			# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
279

NiaOrg / NiaPy

Pull Request — master (#233)

CoralReefsOptimization.getParameters() A

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