NiaPy.algorithms.basic.gso.GlowwormSwarmOptimization.runIteration() - Code Metrics - Inspection of "BA and HBA" - NiaOrg/NiaPy - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — master (#202)

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created 2019-04-16 21:05 UTC

GlowwormSwarmOptimization.runIteration() A

↳ Parent: NiaPy.algorithms.basic.gso

Complexity

Conditions

Size

Total Lines	32
Code Lines	10

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	3
eloc	10
nop	10
dl	0
loc	32
rs	9.9
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.
		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.
		"""
		ukwargs.pop('NP', None)
		Algorithm.setParameters(self, NP=n, **ukwargs)
		self.l0, self.nt, self.rho, self.gamma, self.beta, self.s, self.Distance = 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.NP, 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.NP, .0)
		for j in range(self.NP): 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.NP):
			b_l, b_u = b_u, b_u + pb[i]
			if b_l < r < b_u: return j
		return self.randint(self.NP)

	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.
		"""
		GS, GS_f, d = Algorithm.initPopulation(self, task)
		rs = euclidean(full(task.D, 0), task.bRange)
		L, R = full(self.NP, self.l0), full(self.NP, rs)
		d.update({'L': L, 'R': R, 'rs': rs})
		return GS, GS_f, d

	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.NP)]
		P = [self.probabilityes(i, N[i], L) for i in range(self.NP)]
		j = [self.moveSelect(P[i], i) for i in range(self.NP)]
		for i in range(self.NP): 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.NP): 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[str]): List of strings representing algorithm names.
		alpha (float): --

	See Also:
		* :class:`NiaPy.algorithms.basic.GlowwormSwarmOptimization`
	"""
	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[str]): List of strings representing algorithm names.
		alpha (float): --

	See Also:
		* :class:`NiaPy.algorithms.basic.GlowwormSwarmOptimization`
	"""
	Name = ['GlowwormSwarmOptimizationV2', 'GSOv2']

	def setParameters(self, alpha=0.2, **kwargs):
		r"""Set core parameters for GlowwormSwarmOptimizationV2 algorithm.

		Args:
			alpha (Optional[float]): --
			**kwargs (Dict[str, Any]): Additional arguments.

		See Also:
			* :func:`NiaPy.algorithms.basic.GlowwormSwarmOptimization.setParameters`
		"""
		GlowwormSwarmOptimization.setParameters(self, **kwargs)
		self.alpha = alpha
		if kwargs: logger.info('Unused arguments: %s' % (kwargs))

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

		Args:
			P:
			N:
			rs:

		Returns:
			float: TODO
		"""
		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[str]): List of strings representing algorithm names.
		beta1 (float): --

	See Also:
		* :class:`NiaPy.algorithms.basic.GlowwormSwarmOptimization`
	"""
	Name = ['GlowwormSwarmOptimizationV3', 'GSOv3']

	def setParameters(self, beta1=0.2, **kwargs):
		r"""Set core parameters for GlowwormSwarmOptimizationV3 algorithm.

		Args:
			beta1 (Optional[float]): --
			**kwargs (Dict[str, Any]): Additional arguments.

		See Also:
			* :func:`NiaPy.algorithms.basic.GlowwormSwarmOptimization.setParameters`
		"""
		GlowwormSwarmOptimization.setParameters(self, **kwargs)
		self.beta1 = beta1
		if kwargs: logger.info('Unused arguments: %s' % (kwargs))

	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			l0 (float): Initial luciferin quantity for each glowworm.
40			nt (float): --
41			rs (float): Maximum sensing range.
42			rho (float): Luciferin decay constant.
43			gamma (float): Luciferin enhancement constant.
44			beta (float): --
45			s (float): --
46			Distance (Callable[[numpy.ndarray, numpy.ndarray], float]]): Measure distance between two individuals.
47
48			See Also:
49			* :class:`NiaPy.algorithms.algorithm.Algorithm`
50			"""
51			Name = ['GlowwormSwarmOptimization', 'GSO']
52
53			@staticmethod
54			def typeParameters():
55			r"""Get dictionary with functions for checking values of parameters.
56
57			Returns:
58			Dict[str, Callable]:
59			* n (Callable[[int], bool]): TODO
60			* l0 (Callable[[Union[float, int]], bool]): TODO
61			* nt (Callable[[Union[float, int]], bool]): TODO
62			* rho (Callable[[Union[float, int]], bool]): TODO
63			* gamma (Callable[[float], bool]): TODO
64			* beta (Callable[[float], bool]): TODO
65			* s (Callable[[float], bool]): TODO
66			"""
67			return {
68			'n': lambda x: isinstance(x, int) and x > 0,
69			'l0': lambda x: isinstance(x, (float, int)) and x > 0,
70			'nt': lambda x: isinstance(x, (float, int)) and x > 0,
71			'rho': lambda x: isinstance(x, float) and 0 < x < 1,
72			'gamma': lambda x: isinstance(x, float) and 0 < x < 1,
73			'beta': lambda x: isinstance(x, float) and x > 0,
74			's': lambda x: isinstance(x, float) and x > 0
75			}
76
77			def setParameters(self, n=25, l0=5, nt=5, rho=0.4, gamma=0.6, beta=0.08, s=0.03, Distance=euclidean, **ukwargs):
78			r"""Set the arguments of an algorithm.
79
80			Arguments:
81			n (Optional[int]): Number of glowworms in population.
82			l0 (Optional[float]): Initial luciferin quantity for each glowworm.
83			nt (Optional[float]): --
84			rs (Optional]float]): Maximum sensing range.
85			rho (Optional[float]): Luciferin decay constant.
86			gamma (Optional[float]): Luciferin enhancement constant.
87			beta (Optional[float]): --
88			s (Optional[float]): --
89			Distance (Optional[Callable[[numpy.ndarray, numpy.ndarray], float]]]): Measure distance between two individuals.
90			"""
91			ukwargs.pop('NP', None)
92			Algorithm.setParameters(self, NP=n, **ukwargs)
93			self.l0, self.nt, self.rho, self.gamma, self.beta, self.s, self.Distance = l0, nt, rho, gamma, beta, s, Distance
94			if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))
95
96			def randMove(self, i):
97			r"""Move a glowworm to another glowworm.
98
99			Args:
100			i (int): Index of glowworm that is making a move.
101
102			Returns:
103			int: Index of glowworm to move to.
104			"""
105			j = i
106			while i == j: j = self.randint(self.n)
107			return j
108
109			def getNeighbors(self, i, r, GS, L):
110			r"""Get neighbours of glowworm.
111
112			Args:
113			i (int): Index of glowworm.
114			r (float): Neighborhood distance.
115			GS (numpy.ndarray):
116			L (numpy.ndarray[float]): Luciferin value of glowworm.
117
118			Returns:
119			numpy.ndarray[int]: Indexes of neighborhood glowworms.
120			"""
121			N = full(self.NP, 0)
122			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
123			return N
124
125			def probabilityes(self, i, N, L):
126			r"""TODO.
127
128			Args:
129			i:
130			N:
131			L:
132
133			Returns:
134
135			"""
136			d, P = sum(L[where(N == 1)] - L[i]), full(self.NP, .0)
137			for j in range(self.NP): P[i] = ((L[j] - L[i]) / d) if N[j] == 1 else 0
138			return P
139
140			def moveSelect(self, pb, i):
141			r"""TODO.
142
143			Args:
144			pb:
145			i:
146
147			Returns:
148
149			"""
150			r, b_l, b_u = self.rand(), 0, 0
151			for j in range(self.NP):
152			b_l, b_u = b_u, b_u + pb[i]
153			if b_l < r < b_u: return j
154			return self.randint(self.NP)
155
156			def calcLuciferin(self, L, GS_f):
157			r"""TODO.
158
159			Args:
160			L:
161			GS_f:
162
163			Returns:
164
165			"""
166			return (1 - self.rho) * L + self.gamma * GS_f
167
168			def rangeUpdate(self, R, N, rs):
169			r"""TODO.
170
171			Args:
172			R:
173			N:
174			rs:
175
176			Returns:
177
178			"""
179			return R + self.beta * (self.nt - sum(N))
180
181			def initPopulation(self, task):
182			r"""Initialize population.
183
184			Args:
185			task (Task): Optimization task.
186
187			Returns:
188			Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]:
189			1. Initialized population of glowwarms.
190			2. Initialized populations function/fitness values.
191			3. Additional arguments:
192			* L (numpy.ndarray): TODO.
193			* R (numpy.ndarray): TODO.
194			* rs (numpy.ndarray): TODO.
195			"""
196			GS, GS_f, d = Algorithm.initPopulation(self, task)
197			rs = euclidean(full(task.D, 0), task.bRange)
198			L, R = full(self.NP, self.l0), full(self.NP, rs)
199			d.update({'L': L, 'R': R, 'rs': rs})
200			return GS, GS_f, d
201
202			def runIteration(self, task, GS, GS_f, xb, fxb, L, R, rs, **dparams):
203			r"""Core function of GlowwormSwarmOptimization algorithm.
204
205			Args:
206			task (Task): Optimization taks.
207			GS (numpy.ndarray): Current population.
208			GS_f (numpy.ndarray[float]): Current populations fitness/function values.
209			xb (numpy.ndarray): Global best individual.
210			fxb (float): Global best individuals function/fitness value.
211			L (numpy.ndarray):
212			R (numpy.ndarray):
213			rs (numpy.ndarray):
214			**dparams Dict[str, Any]: Additional arguments.
215
216			Returns:
217			Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]:
218			1. Initialized population of glowwarms.
219			2. Initialized populations function/fitness values.
220			3. Additional arguments:
221			* L (numpy.ndarray): TODO.
222			* R (numpy.ndarray): TODO.
223			* rs (numpy.ndarray): TODO.
224			"""
225			GSo, Ro = copy(GS), copy(R)
226			L = self.calcLuciferin(L, GS_f)
227			N = [self.getNeighbors(i, Ro[i], GSo, L) for i in range(self.NP)]
228			P = [self.probabilityes(i, N[i], L) for i in range(self.NP)]
229			j = [self.moveSelect(P[i], i) for i in range(self.NP)]
230			for i in range(self.NP): 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)
231			for i in range(self.NP): R[i] = max(0, min(rs, self.rangeUpdate(Ro[i], N[i], rs)))
232			GS_f = apply_along_axis(task.eval, 1, GS)
233			return GS, GS_f, {'L': L, 'R': R, 'rs': rs}
234
235			class GlowwormSwarmOptimizationV1(GlowwormSwarmOptimization):
236			r"""Implementation of glowwarm swarm optimization.
237
238			Algorithm:
239			Glowwarm Swarm Optimization Algorithm
240
241			Date:
242			2018
243
244			Authors:
245			Klemen Berkovič
246
247			License:
248			MIT
249
250			Reference URL:
251			https://www.springer.com/gp/book/9783319515946
252
253			Reference paper:
254			Kaipa, Krishnanand N., and Debasish Ghose. Glowworm swarm optimization: theory, algorithms, and applications. Vol. 698. Springer, 2017.
255
256			Attributes:
257			Name (List[str]): List of strings representing algorithm names.
258			alpha (float): --
259
260			See Also:
261			* :class:`NiaPy.algorithms.basic.GlowwormSwarmOptimization`
262			"""
263			Name = ['GlowwormSwarmOptimizationV1', 'GSOv1']
264
265			def setParameters(self, **kwargs):
266			self.__setParams(**kwargs)
267			GlowwormSwarmOptimization.setParameters(self, **kwargs)
268
269			def __setParams(self, alpha=0.2, **ukwargs):
270			r"""Set the arguments of an algorithm.
271
272			Arguments:
273			alpha (float): --
274			"""
275			self.alpha = alpha
276			if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))
277
278			def calcLuciferin(self, L, GS_f):
279			r"""TODO.
280
281			Args:
282			L:
283			GS_f:
284
285			Returns:
286
287			"""
288			return fmax(0, (1 - self.rho) * L + self.gamma * GS_f)
289
290			def rangeUpdate(self, R, N, rs):
291			r"""TODO.
292
293			Args:
294			R:
295			N:
296			rs:
297
298			Returns:
299
300			"""
301			return rs / (1 + self.beta * (sum(N) / (pi * rs ** 2)))
302
303			class GlowwormSwarmOptimizationV2(GlowwormSwarmOptimization):
304			r"""Implementation of glowwarm swarm optimization.
305
306			Algorithm:
307			Glowwarm Swarm Optimization Algorithm
308
309			Date:
310			2018
311
312			Authors:
313			Klemen Berkovič
314
315			License:
316			MIT
317
318			Reference URL:
319			https://www.springer.com/gp/book/9783319515946
320
321			Reference paper:
322			Kaipa, Krishnanand N., and Debasish Ghose. Glowworm swarm optimization: theory, algorithms, and applications. Vol. 698. Springer, 2017.
323
324			Attributes:
325			Name (List[str]): List of strings representing algorithm names.
326			alpha (float): --
327
328			See Also:
329			* :class:`NiaPy.algorithms.basic.GlowwormSwarmOptimization`
330			"""
331			Name = ['GlowwormSwarmOptimizationV2', 'GSOv2']
332
333			def setParameters(self, alpha=0.2, **kwargs):
334			r"""Set core parameters for GlowwormSwarmOptimizationV2 algorithm.
335
336			Args:
337			alpha (Optional[float]): --
338			**kwargs (Dict[str, Any]): Additional arguments.
339
340			See Also:
341			* :func:`NiaPy.algorithms.basic.GlowwormSwarmOptimization.setParameters`
342			"""
343			GlowwormSwarmOptimization.setParameters(self, **kwargs)
344			self.alpha = alpha
345			if kwargs: logger.info('Unused arguments: %s' % (kwargs))
346
347			def rangeUpdate(self, P, N, rs):
348			r"""TODO.
349
350			Args:
351			P:
352			N:
353			rs:
354
355			Returns:
356			float: TODO
357			"""
358			return self.alpha + (rs - self.alpha) / (1 + self.beta * sum(N))
359
360			class GlowwormSwarmOptimizationV3(GlowwormSwarmOptimization):
361			r"""Implementation of glowwarm swarm optimization.
362
363			Algorithm:
364			Glowwarm Swarm Optimization Algorithm
365
366			Date:
367			2018
368
369			Authors:
370			Klemen Berkovič
371
372			License:
373			MIT
374
375			Reference URL:
376			https://www.springer.com/gp/book/9783319515946
377
378			Reference paper:
379			Kaipa, Krishnanand N., and Debasish Ghose. Glowworm swarm optimization: theory, algorithms, and applications. Vol. 698. Springer, 2017.
380
381			Attributes:
382			Name (List[str]): List of strings representing algorithm names.
383			beta1 (float): --
384
385			See Also:
386			* :class:`NiaPy.algorithms.basic.GlowwormSwarmOptimization`
387			"""
388			Name = ['GlowwormSwarmOptimizationV3', 'GSOv3']
389
390			def setParameters(self, beta1=0.2, **kwargs):
391			r"""Set core parameters for GlowwormSwarmOptimizationV3 algorithm.
392
393			Args:
394			beta1 (Optional[float]): --
395			**kwargs (Dict[str, Any]): Additional arguments.
396
397			See Also:
398			* :func:`NiaPy.algorithms.basic.GlowwormSwarmOptimization.setParameters`
399			"""
400			GlowwormSwarmOptimization.setParameters(self, **kwargs)
401			self.beta1 = beta1
402			if kwargs: logger.info('Unused arguments: %s' % (kwargs))
403
404			def rangeUpdate(self, R, N, rs):
405			r"""TODO.
406
407			Args:
408			R:
409			N:
410			rs:
411
412			Returns:
413
414			"""
415			return R + (self.beta * sum(N)) if sum(N) < self.nt else (-self.beta1 * sum(N))
416
417			# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
418

NiaOrg / NiaPy

Pull Request — master (#202)

GlowwormSwarmOptimization.runIteration() A

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