NiaPy.algorithms.basic.gso.GlowwormSwarmOptimization.setParameters() - Code Metrics - Inspection of "Dependencies, code style, etc. (#196)" - NiaOrg/NiaPy - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Branch — master (13db34)

by Grega

created 2019-04-15 10:27 UTC

GlowwormSwarmOptimization.setParameters() A

↳ Parent: NiaPy.algorithms.basic.gso

Complexity

Conditions

Size

Total Lines	16
Code Lines	3

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	2
eloc	3
nop	10
dl	0
loc	16
rs	10
c	0
b	0
f	0

How to fix Many Parameters

# encoding=utf8
# pylint: disable=mixed-indentation, trailing-whitespace, line-too-long, multiple-statements, attribute-defined-outside-init, logging-not-lazy, no-self-use, redefined-builtin, singleton-comparison, unused-argument, arguments-differ, no-else-return, bad-continuation
import logging

from scipy.spatial.distance import euclidean
from numpy import full, apply_along_axis, copy, sum, fmax, pi, where

from NiaPy.algorithms.algorithm import Algorithm

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

__all__ = ['GlowwormSwarmOptimization', 'GlowwormSwarmOptimizationV1', 'GlowwormSwarmOptimizationV2', 'GlowwormSwarmOptimizationV3']

class GlowwormSwarmOptimization(Algorithm):
	r"""Implementation of glowwarm swarm optimization.

	Algorithm:
		Glowwarm Swarm Optimization Algorithm

	Date:
		2018

	Authors:
		Klemen Berkovič

	License:
		MIT

	Reference URL:
		https://www.springer.com/gp/book/9783319515946

	Reference paper:
		Kaipa, Krishnanand N., and Debasish Ghose. Glowworm swarm optimization: theory, algorithms, and applications. Vol. 698. Springer, 2017.

	Attributes:
		Name (List[str]): List of strings represeinting algorithm name.
		n (int): Number of glowworms in population.
		l0 (float): Initial luciferin quantity for each glowworm.
		nt (float): --
		rs (float): Maximum sensing range.
		rho (float): Luciferin decay constant.
		gamma (float): Luciferin enhancement constant.
		beta (float): --
		s (float): --
		Distance (Callable[[numpy.ndarray, numpy.ndarray], float]]): Measure distance between two individuals.

	See Also:
		* :class:`NiaPy.algorithms.algorithm.Algorithm`
	"""
	Name = ['GlowwormSwarmOptimization', 'GSO']

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

		Returns:
			Dict[str, Callable]:
				* n (Callable[[int], bool]): TODO
				* l0 (Callable[[Union[float, int]], bool]): TODO
				* nt (Callable[[Union[float, int]], bool]): TODO
				* rho (Callable[[Union[float, int]], bool]): TODO
				* gamma (Callable[[float], bool]): TODO
				* beta (Callable[[float], bool]): TODO
				* s (Callable[[float], bool]): TODO
		"""
		return {
			'n': lambda x: isinstance(x, int) and x > 0,
			'l0': lambda x: isinstance(x, (float, int)) and x > 0,
			'nt': lambda x: isinstance(x, (float, int)) and x > 0,
			'rho': lambda x: isinstance(x, float) and 0 < x < 1,
			'gamma': lambda x: isinstance(x, float) and 0 < x < 1,
			'beta': lambda x: isinstance(x, float) and x > 0,
			's': lambda x: isinstance(x, float) and x > 0
		}

	def setParameters(self, n=25, l0=5, nt=5, rho=0.4, gamma=0.6, beta=0.08, s=0.03, Distance=euclidean, **ukwargs):
		r"""Set the arguments of an algorithm.

		Arguments:
			n (Optional[int]): Number of glowworms in population.
			l0 (Optional[float]): Initial luciferin quantity for each glowworm.
			nt (Optional[float]): --
			rs (Optional]float]): Maximum sensing range.
			rho (Optional[float]): Luciferin decay constant.
			gamma (Optional[float]): Luciferin enhancement constant.
			beta (Optional[float]): --
			s (Optional[float]): --
			Distance (Optional[Callable[[numpy.ndarray, numpy.ndarray], float]]]): Measure distance between two individuals.
		"""
		self.n, self.l0, self.nt, self.rho, self.gamma, self.beta, self.s, self.Distance = n, l0, nt, rho, gamma, beta, s, Distance
		if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))

	def randMove(self, i):
		r"""Move a glowworm to another glowworm.

		Args:
			i (int): Index of glowworm that is making a move.

		Returns:
			int: Index of glowworm to move to.
		"""
		j = i
		while i == j: j = self.randint(self.n)
		return j

	def getNeighbors(self, i, r, GS, L):
		r"""Get neighbours of glowworm.

		Args:
			i (int): Index of glowworm.
			r (float): Neighborhood distance.
			GS (numpy.ndarray):
			L (numpy.ndarray[float]): Luciferin value of glowworm.

		Returns:
			numpy.ndarray[int]: Indexes of neighborhood glowworms.
		"""
		N = full(self.n, 0)
		for j, gw in enumerate(GS): N[j] = 1 if i != j and self.Distance(GS[i], gw) <= r and L[i] >= L[j] else 0
		return N

	def probabilityes(self, i, N, L):
		r"""TODO.

		Args:
			i:
			N:
			L:

		Returns:

		"""
		d, P = sum(L[where(N == 1)] - L[i]), full(self.n, .0)
		for j in range(self.n): P[i] = ((L[j] - L[i]) / d) if N[j] == 1 else 0
		return P

	def moveSelect(self, pb, i):
		r"""TODO.

		Args:
			pb:
			i:

		Returns:

		"""
		r, b_l, b_u = self.rand(), 0, 0
		for j in range(self.n):
			b_l, b_u = b_u, b_u + pb[i]
			if b_l < r < b_u: return j
		return self.randint(self.n)

	def calcLuciferin(self, L, GS_f):
		r"""TODO.

		Args:
			L:
			GS_f:

		Returns:

		"""
		return (1 - self.rho) * L + self.gamma * GS_f

	def rangeUpdate(self, R, N, rs):
		r"""TODO.

		Args:
			R:
			N:
			rs:

		Returns:

		"""
		return R + self.beta * (self.nt - sum(N))

	def initPopulation(self, task):
		r"""Initialize population.

		Args:
			task (Task): Optimization task.

		Returns:
			Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]:
				1. Initialized population of glowwarms.
				2. Initialized populations function/fitness values.
				3. Additional arguments:
					* L (numpy.ndarray): TODO.
					* R (numpy.ndarray): TODO.
					* rs (numpy.ndarray): TODO.
		"""
		rs = euclidean(full(task.D, 0), task.bRange)
		GS, L, R = self.uniform(task.Lower, task.Upper, [self.n, task.D]), full(self.n, self.l0), full(self.n, rs)
		GS_f = apply_along_axis(task.eval, 1, GS)
		return GS, GS_f, {'L': L, 'R': R, 'rs': rs}

	def runIteration(self, task, GS, GS_f, xb, fxb, L, R, rs, **dparams):
		r"""Core function of GlowwormSwarmOptimization algorithm.

		Args:
			task (Task): Optimization taks.
			GS (numpy.ndarray): Current population.
			GS_f (numpy.ndarray[float]): Current populations fitness/function values.
			xb (numpy.ndarray): Global best individual.
			fxb (float): Global best individuals function/fitness value.
			L (numpy.ndarray):
			R (numpy.ndarray):
			rs (numpy.ndarray):
			**dparams Dict[str, Any]: Additional arguments.

		Returns:
			Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]:
				1. Initialized population of glowwarms.
				2. Initialized populations function/fitness values.
				3. Additional arguments:
					* L (numpy.ndarray): TODO.
					* R (numpy.ndarray): TODO.
					* rs (numpy.ndarray): TODO.
		"""
		GSo, Ro = copy(GS), copy(R)
		L = self.calcLuciferin(L, GS_f)
		N = [self.getNeighbors(i, Ro[i], GSo, L) for i in range(self.n)]
		P = [self.probabilityes(i, N[i], L) for i in range(self.n)]
		j = [self.moveSelect(P[i], i) for i in range(self.n)]
		for i in range(self.n): GS[i] = task.repair(GSo[i] + self.s * ((GSo[j[i]] - GSo[i]) / (self.Distance(GSo[j[i]], GSo[i]) + 1e-31)), rnd=self.Rand)
		for i in range(self.n): R[i] = max(0, min(rs, self.rangeUpdate(Ro[i], N[i], rs)))
		GS_f = apply_along_axis(task.eval, 1, GS)
		return GS, GS_f, {'L': L, 'R': R, 'rs': rs}

class GlowwormSwarmOptimizationV1(GlowwormSwarmOptimization):
	r"""Implementation of glowwarm swarm optimization.

	Algorithm:
		Glowwarm Swarm Optimization Algorithm

	Date:
		2018

	Authors:
		Klemen Berkovič

	License:
		MIT

	Reference URL:
		https://www.springer.com/gp/book/9783319515946

	Reference paper:
		Kaipa, Krishnanand N., and Debasish Ghose. Glowworm swarm optimization: theory, algorithms, and applications. Vol. 698. Springer, 2017.

	Attributes:
		Name (list of str): TODO
	"""
	Name = ['GlowwormSwarmOptimizationV1', 'GSOv1']

	def setParameters(self, **kwargs):
		self.__setParams(**kwargs)
		GlowwormSwarmOptimization.setParameters(self, **kwargs)

	def __setParams(self, alpha=0.2, **ukwargs):
		r"""Set the arguments of an algorithm.

		Arguments:
			alpha (float): --
		"""
		self.alpha = alpha
		if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))

	def calcLuciferin(self, L, GS_f):
		r"""TODO.

		Args:
			L:
			GS_f:

		Returns:

		"""
		return fmax(0, (1 - self.rho) * L + self.gamma * GS_f)

	def rangeUpdate(self, R, N, rs):
		r"""TODO.

		Args:
			R:
			N:
			rs:

		Returns:

		"""
		return rs / (1 + self.beta * (sum(N) / (pi * rs ** 2)))

class GlowwormSwarmOptimizationV2(GlowwormSwarmOptimization):
	r"""Implementation of glowwarm swarm optimization.

	Algorithm:
		Glowwarm Swarm Optimization Algorithm

	Date:
		2018

	Authors:
		Klemen Berkovič

	License:
		MIT

	Reference URL:
		https://www.springer.com/gp/book/9783319515946

	Reference paper:
		Kaipa, Krishnanand N., and Debasish Ghose. Glowworm swarm optimization: theory, algorithms, and applications. Vol. 698. Springer, 2017.

	Attributes:
		Name (list or str): TODO
	"""
	Name = ['GlowwormSwarmOptimizationV2', 'GSOv2']

	def setParameters(self, **kwargs):
		self.__setParams(alpha=kwargs.pop('alpha', 0.2), **kwargs)
		GlowwormSwarmOptimization.setParameters(self, **kwargs)

	def __setParams(self, alpha=0.2, **ukwargs):
		r"""Set the arguments of an algorithm.

		**Arguments:**

		beta1 {real} --

		s {real} --
		"""
		self.alpha = alpha
		if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))

	def rangeUpdate(self, P, N, rs):
		r"""TODO.

		Args:
			P:
			N:
			rs:

		Returns:

		"""
		return self.alpha + (rs - self.alpha) / (1 + self.beta * sum(N))

class GlowwormSwarmOptimizationV3(GlowwormSwarmOptimization):
	r"""Implementation of glowwarm swarm optimization.

	Algorithm:
		Glowwarm Swarm Optimization Algorithm

	Date:
		2018

	Authors:
		Klemen Berkovič

	License:
		MIT

	Reference URL:
		https://www.springer.com/gp/book/9783319515946

	Reference paper:
		Kaipa, Krishnanand N., and Debasish Ghose. Glowworm swarm optimization: theory, algorithms, and applications. Vol. 698. Springer, 2017.

	Attributes:
		Name (list of str): TODO
	"""
	Name = ['GlowwormSwarmOptimizationV3', 'GSOv3']

	def setParameters(self, **kwargs):
		self.__setParams(beta1=kwargs.pop('beta1', 0.2), **kwargs)
		GlowwormSwarmOptimization.setParameters(self, **kwargs)

	def __setParams(self, beta1=0.2, **ukwargs):
		r"""Set the arguments of an algorithm.

		**Arguments:**

		beta1 {real} --

		s {real} --
		"""
		self.beta1 = beta1
		if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))

	def rangeUpdate(self, R, N, rs):
		r"""TODO.

		Args:
			R:
			N:
			rs:

		Returns:

		"""
		return R + (self.beta * sum(N)) if sum(N) < self.nt else (-self.beta1 * sum(N))

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


1			# encoding=utf8
2			# pylint: disable=mixed-indentation, trailing-whitespace, line-too-long, multiple-statements, attribute-defined-outside-init, logging-not-lazy, no-self-use, redefined-builtin, singleton-comparison, unused-argument, arguments-differ, no-else-return, bad-continuation
3			import logging
4
5			from scipy.spatial.distance import euclidean
6			from numpy import full, apply_along_axis, copy, sum, fmax, pi, where
7
8			from NiaPy.algorithms.algorithm import Algorithm
9
10			logging.basicConfig()
11			logger = logging.getLogger('NiaPy.algorithms.basic')
12			logger.setLevel('INFO')
13
14			__all__ = ['GlowwormSwarmOptimization', 'GlowwormSwarmOptimizationV1', 'GlowwormSwarmOptimizationV2', 'GlowwormSwarmOptimizationV3']
15
16			class GlowwormSwarmOptimization(Algorithm):
17			r"""Implementation of glowwarm swarm optimization.
18
19			Algorithm:
20			Glowwarm Swarm Optimization Algorithm
21
22			Date:
23			2018
24
25			Authors:
26			Klemen Berkovič
27
28			License:
29			MIT
30
31			Reference URL:
32			https://www.springer.com/gp/book/9783319515946
33
34			Reference paper:
35			Kaipa, Krishnanand N., and Debasish Ghose. Glowworm swarm optimization: theory, algorithms, and applications. Vol. 698. Springer, 2017.
36
37			Attributes:
38			Name (List[str]): List of strings represeinting algorithm name.
39			n (int): Number of glowworms in population.
40			l0 (float): Initial luciferin quantity for each glowworm.
41			nt (float): --
42			rs (float): Maximum sensing range.
43			rho (float): Luciferin decay constant.
44			gamma (float): Luciferin enhancement constant.
45			beta (float): --
46			s (float): --
47			Distance (Callable[[numpy.ndarray, numpy.ndarray], float]]): Measure distance between two individuals.
48
49			See Also:
50			* :class:`NiaPy.algorithms.algorithm.Algorithm`
51			"""
52			Name = ['GlowwormSwarmOptimization', 'GSO']
53
54			@staticmethod
55			def typeParameters():
56			r"""Get dictionary with functions for checking values of parameters.
57
58			Returns:
59			Dict[str, Callable]:
60			* n (Callable[[int], bool]): TODO
61			* l0 (Callable[[Union[float, int]], bool]): TODO
62			* nt (Callable[[Union[float, int]], bool]): TODO
63			* rho (Callable[[Union[float, int]], bool]): TODO
64			* gamma (Callable[[float], bool]): TODO
65			* beta (Callable[[float], bool]): TODO
66			* s (Callable[[float], bool]): TODO
67			"""
68			return {
69			'n': lambda x: isinstance(x, int) and x > 0,
70			'l0': lambda x: isinstance(x, (float, int)) and x > 0,
71			'nt': lambda x: isinstance(x, (float, int)) and x > 0,
72			'rho': lambda x: isinstance(x, float) and 0 < x < 1,
73			'gamma': lambda x: isinstance(x, float) and 0 < x < 1,
74			'beta': lambda x: isinstance(x, float) and x > 0,
75			's': lambda x: isinstance(x, float) and x > 0
76			}
77
78			def setParameters(self, n=25, l0=5, nt=5, rho=0.4, gamma=0.6, beta=0.08, s=0.03, Distance=euclidean, **ukwargs):
79			r"""Set the arguments of an algorithm.
80
81			Arguments:
82			n (Optional[int]): Number of glowworms in population.
83			l0 (Optional[float]): Initial luciferin quantity for each glowworm.
84			nt (Optional[float]): --
85			rs (Optional]float]): Maximum sensing range.
86			rho (Optional[float]): Luciferin decay constant.
87			gamma (Optional[float]): Luciferin enhancement constant.
88			beta (Optional[float]): --
89			s (Optional[float]): --
90			Distance (Optional[Callable[[numpy.ndarray, numpy.ndarray], float]]]): Measure distance between two individuals.
91			"""
92			self.n, self.l0, self.nt, self.rho, self.gamma, self.beta, self.s, self.Distance = n, l0, nt, rho, gamma, beta, s, Distance
93			if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))
94
95			def randMove(self, i):
96			r"""Move a glowworm to another glowworm.
97
98			Args:
99			i (int): Index of glowworm that is making a move.
100
101			Returns:
102			int: Index of glowworm to move to.
103			"""
104			j = i
105			while i == j: j = self.randint(self.n)
106			return j
107
108			def getNeighbors(self, i, r, GS, L):
109			r"""Get neighbours of glowworm.
110
111			Args:
112			i (int): Index of glowworm.
113			r (float): Neighborhood distance.
114			GS (numpy.ndarray):
115			L (numpy.ndarray[float]): Luciferin value of glowworm.
116
117			Returns:
118			numpy.ndarray[int]: Indexes of neighborhood glowworms.
119			"""
120			N = full(self.n, 0)
121			for j, gw in enumerate(GS): N[j] = 1 if i != j and self.Distance(GS[i], gw) <= r and L[i] >= L[j] else 0
122			return N
123
124			def probabilityes(self, i, N, L):
125			r"""TODO.
126
127			Args:
128			i:
129			N:
130			L:
131
132			Returns:
133
134			"""
135			d, P = sum(L[where(N == 1)] - L[i]), full(self.n, .0)
136			for j in range(self.n): P[i] = ((L[j] - L[i]) / d) if N[j] == 1 else 0
137			return P
138
139			def moveSelect(self, pb, i):
140			r"""TODO.
141
142			Args:
143			pb:
144			i:
145
146			Returns:
147
148			"""
149			r, b_l, b_u = self.rand(), 0, 0
150			for j in range(self.n):
151			b_l, b_u = b_u, b_u + pb[i]
152			if b_l < r < b_u: return j
153			return self.randint(self.n)
154
155			def calcLuciferin(self, L, GS_f):
156			r"""TODO.
157
158			Args:
159			L:
160			GS_f:
161
162			Returns:
163
164			"""
165			return (1 - self.rho) * L + self.gamma * GS_f
166
167			def rangeUpdate(self, R, N, rs):
168			r"""TODO.
169
170			Args:
171			R:
172			N:
173			rs:
174
175			Returns:
176
177			"""
178			return R + self.beta * (self.nt - sum(N))
179
180			def initPopulation(self, task):
181			r"""Initialize population.
182
183			Args:
184			task (Task): Optimization task.
185
186			Returns:
187			Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]:
188			1. Initialized population of glowwarms.
189			2. Initialized populations function/fitness values.
190			3. Additional arguments:
191			* L (numpy.ndarray): TODO.
192			* R (numpy.ndarray): TODO.
193			* rs (numpy.ndarray): TODO.
194			"""
195			rs = euclidean(full(task.D, 0), task.bRange)
196			GS, L, R = self.uniform(task.Lower, task.Upper, [self.n, task.D]), full(self.n, self.l0), full(self.n, rs)
197			GS_f = apply_along_axis(task.eval, 1, GS)
198			return GS, GS_f, {'L': L, 'R': R, 'rs': rs}
199
200			def runIteration(self, task, GS, GS_f, xb, fxb, L, R, rs, **dparams):
201			r"""Core function of GlowwormSwarmOptimization algorithm.
202
203			Args:
204			task (Task): Optimization taks.
205			GS (numpy.ndarray): Current population.
206			GS_f (numpy.ndarray[float]): Current populations fitness/function values.
207			xb (numpy.ndarray): Global best individual.
208			fxb (float): Global best individuals function/fitness value.
209			L (numpy.ndarray):
210			R (numpy.ndarray):
211			rs (numpy.ndarray):
212			**dparams Dict[str, Any]: Additional arguments.
213
214			Returns:
215			Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]:
216			1. Initialized population of glowwarms.
217			2. Initialized populations function/fitness values.
218			3. Additional arguments:
219			* L (numpy.ndarray): TODO.
220			* R (numpy.ndarray): TODO.
221			* rs (numpy.ndarray): TODO.
222			"""
223			GSo, Ro = copy(GS), copy(R)
224			L = self.calcLuciferin(L, GS_f)
225			N = [self.getNeighbors(i, Ro[i], GSo, L) for i in range(self.n)]
226			P = [self.probabilityes(i, N[i], L) for i in range(self.n)]
227			j = [self.moveSelect(P[i], i) for i in range(self.n)]
228			for i in range(self.n): GS[i] = task.repair(GSo[i] + self.s * ((GSo[j[i]] - GSo[i]) / (self.Distance(GSo[j[i]], GSo[i]) + 1e-31)), rnd=self.Rand)
229			for i in range(self.n): R[i] = max(0, min(rs, self.rangeUpdate(Ro[i], N[i], rs)))
230			GS_f = apply_along_axis(task.eval, 1, GS)
231			return GS, GS_f, {'L': L, 'R': R, 'rs': rs}
232
233			class GlowwormSwarmOptimizationV1(GlowwormSwarmOptimization):
234			r"""Implementation of glowwarm swarm optimization.
235
236			Algorithm:
237			Glowwarm Swarm Optimization Algorithm
238
239			Date:
240			2018
241
242			Authors:
243			Klemen Berkovič
244
245			License:
246			MIT
247
248			Reference URL:
249			https://www.springer.com/gp/book/9783319515946
250
251			Reference paper:
252			Kaipa, Krishnanand N., and Debasish Ghose. Glowworm swarm optimization: theory, algorithms, and applications. Vol. 698. Springer, 2017.
253
254			Attributes:
255			Name (list of str): TODO
256			"""
257			Name = ['GlowwormSwarmOptimizationV1', 'GSOv1']
258
259			def setParameters(self, **kwargs):
260			self.__setParams(**kwargs)
261			GlowwormSwarmOptimization.setParameters(self, **kwargs)
262
263			def __setParams(self, alpha=0.2, **ukwargs):
264			r"""Set the arguments of an algorithm.
265
266			Arguments:
267			alpha (float): --
268			"""
269			self.alpha = alpha
270			if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))
271
272			def calcLuciferin(self, L, GS_f):
273			r"""TODO.
274
275			Args:
276			L:
277			GS_f:
278
279			Returns:
280
281			"""
282			return fmax(0, (1 - self.rho) * L + self.gamma * GS_f)
283
284			def rangeUpdate(self, R, N, rs):
285			r"""TODO.
286
287			Args:
288			R:
289			N:
290			rs:
291
292			Returns:
293
294			"""
295			return rs / (1 + self.beta * (sum(N) / (pi * rs ** 2)))
296
297			class GlowwormSwarmOptimizationV2(GlowwormSwarmOptimization):
298			r"""Implementation of glowwarm swarm optimization.
299
300			Algorithm:
301			Glowwarm Swarm Optimization Algorithm
302
303			Date:
304			2018
305
306			Authors:
307			Klemen Berkovič
308
309			License:
310			MIT
311
312			Reference URL:
313			https://www.springer.com/gp/book/9783319515946
314
315			Reference paper:
316			Kaipa, Krishnanand N., and Debasish Ghose. Glowworm swarm optimization: theory, algorithms, and applications. Vol. 698. Springer, 2017.
317
318			Attributes:
319			Name (list or str): TODO
320			"""
321			Name = ['GlowwormSwarmOptimizationV2', 'GSOv2']
322
323			def setParameters(self, **kwargs):
324			self.__setParams(alpha=kwargs.pop('alpha', 0.2), **kwargs)
325			GlowwormSwarmOptimization.setParameters(self, **kwargs)
326
327			def __setParams(self, alpha=0.2, **ukwargs):
328			r"""Set the arguments of an algorithm.
329
330			Arguments:
331
332			beta1 {real} --
333
334			s {real} --
335			"""
336			self.alpha = alpha
337			if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))
338
339			def rangeUpdate(self, P, N, rs):
340			r"""TODO.
341
342			Args:
343			P:
344			N:
345			rs:
346
347			Returns:
348
349			"""
350			return self.alpha + (rs - self.alpha) / (1 + self.beta * sum(N))
351
352			class GlowwormSwarmOptimizationV3(GlowwormSwarmOptimization):
353			r"""Implementation of glowwarm swarm optimization.
354
355			Algorithm:
356			Glowwarm Swarm Optimization Algorithm
357
358			Date:
359			2018
360
361			Authors:
362			Klemen Berkovič
363
364			License:
365			MIT
366
367			Reference URL:
368			https://www.springer.com/gp/book/9783319515946
369
370			Reference paper:
371			Kaipa, Krishnanand N., and Debasish Ghose. Glowworm swarm optimization: theory, algorithms, and applications. Vol. 698. Springer, 2017.
372
373			Attributes:
374			Name (list of str): TODO
375			"""
376			Name = ['GlowwormSwarmOptimizationV3', 'GSOv3']
377
378			def setParameters(self, **kwargs):
379			self.__setParams(beta1=kwargs.pop('beta1', 0.2), **kwargs)
380			GlowwormSwarmOptimization.setParameters(self, **kwargs)
381
382			def __setParams(self, beta1=0.2, **ukwargs):
383			r"""Set the arguments of an algorithm.
384
385			Arguments:
386
387			beta1 {real} --
388
389			s {real} --
390			"""
391			self.beta1 = beta1
392			if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))
393
394			def rangeUpdate(self, R, N, rs):
395			r"""TODO.
396
397			Args:
398			R:
399			N:
400			rs:
401
402			Returns:
403
404			"""
405			return R + (self.beta * sum(N)) if sum(N) < self.nt else (-self.beta1 * sum(N))
406
407			# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
408

NiaOrg / NiaPy

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GlowwormSwarmOptimization.setParameters() A

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