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

Passed

Pull Request — master (#233)

unknown

created 2019-11-10 17:51 UTC

CoralReefsOptimization.asexualReprodution() A

↳ Parent: NiaPy.algorithms.basic.cro

Complexity

Conditions

Size

Total Lines	22
Code Lines	6

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	6
nop	6
dl	0
loc	22
rs	10
c	0
b	0
f	0

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

NiaOrg / NiaPy

Pull Request — master (#233)

CoralReefsOptimization.asexualReprodution() A

Complexity

Size

Duplication

Importance

Duplication Side-by-Side

Filter issues like