NiaPy.algorithms.basic.gso.GlowwormSwarmOptimization.setParameters() - 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 2019-11-10 17:51 UTC

GlowwormSwarmOptimization.setParameters() A

↳ Parent: NiaPy.algorithms.basic.gso

Complexity

Conditions

Size

Total Lines	17
Code Lines	4

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	4
nop	10
dl	0
loc	17
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, no-member
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 algorithmInfo():
		r"""Get basic information of algorithm.

		Returns:
			str: Basic information.
		"""
		return r"""Kaipa, Krishnanand N., and Debasish Ghose. Glowworm swarm optimization: theory, algorithms, and applications. Vol. 698. Springer, 2017."""

	@staticmethod

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

		Returns:
			Dict[str, Callable]:
				* n (Callable[[int], bool])
				* l0 (Callable[[Union[float, int]], bool])
				* nt (Callable[[Union[float, int]], bool])
				* rho (Callable[[Union[float, int]], bool])
				* gamma (Callable[[float], bool])
				* beta (Callable[[float], bool])
				* s (Callable[[float], bool])
		"""
		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

	def getParameters(self):
		r"""Get algorithms parameters values.

		Returns:
			Dict[str, Any]: TODO.
		"""
		d = Algorithm.getParameters(self)
		d.pop('NP', None)
		d.update({
			'n': self.NP,
			'l0': self.l0,
			'nt': self.nt,
			'rho': self.rho,
			'gamma': self.gamma,
			'beta': self.beta,
			's': self.s,
			'Distance': self.Distance
		})
		return d

	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"""Calculate probabilities for glowworm to movement.

		Args:
			i (int): Index of glowworm to search for probable movement.
			N (numpy.ndarray[float]):
			L (numpy.ndarray[float]):

		Returns:
			numpy.ndarray[float]: Probabilities for each glowworm in swarm.
		"""
		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): 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, numpy.ndarray, float, Dict[str, Any]]:
				1. Initialized population of glowwarms.
				2. Initialized populations function/fitness values.
				3. New global best solution
				4. New global best sloutions fitness/objective value.
				5. 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)
		xb, fxb = self.getBest(GS, GS_f, xb, fxb)
		return GS, GS_f, xb, fxb, {'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):
		r"""Set default parameters of the algorithm.

		Args:
			**kwargs (dict): Additional arguments.
		"""
		GlowwormSwarmOptimization.setParameters(self, **kwargs)

	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

	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

	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, no-member
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 algorithmInfo():
55		r"""Get basic information of algorithm.
56
57		Returns:
58		str: Basic information.
59		"""
60		return r"""Kaipa, Krishnanand N., and Debasish Ghose. Glowworm swarm optimization: theory, algorithms, and applications. Vol. 698. Springer, 2017."""
61
62	View Code Duplication	@staticmethod
		0 ignored issues – show Duplication introduced 2019-06-07 14:55 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
63		def typeParameters():
64		r"""Get dictionary with functions for checking values of parameters.
65
66		Returns:
67		Dict[str, Callable]:
68		* n (Callable[[int], bool])
69		* l0 (Callable[[Union[float, int]], bool])
70		* nt (Callable[[Union[float, int]], bool])
71		* rho (Callable[[Union[float, int]], bool])
72		* gamma (Callable[[float], bool])
73		* beta (Callable[[float], bool])
74		* s (Callable[[float], bool])
75		"""
76		return {
77		'n': lambda x: isinstance(x, int) and x > 0,
78		'l0': lambda x: isinstance(x, (float, int)) and x > 0,
79		'nt': lambda x: isinstance(x, (float, int)) and x > 0,
80		'rho': lambda x: isinstance(x, float) and 0 < x < 1,
81		'gamma': lambda x: isinstance(x, float) and 0 < x < 1,
82		'beta': lambda x: isinstance(x, float) and x > 0,
83		's': lambda x: isinstance(x, float) and x > 0
84		}
85
86		def setParameters(self, n=25, l0=5, nt=5, rho=0.4, gamma=0.6, beta=0.08, s=0.03, Distance=euclidean, **ukwargs):
87		r"""Set the arguments of an algorithm.
88
89		Arguments:
90		n (Optional[int]): Number of glowworms in population.
91		l0 (Optional[float]): Initial luciferin quantity for each glowworm.
92		nt (Optional[float]): --
93		rs (Optional]float]): Maximum sensing range.
94		rho (Optional[float]): Luciferin decay constant.
95		gamma (Optional[float]): Luciferin enhancement constant.
96		beta (Optional[float]): --
97		s (Optional[float]): --
98		Distance (Optional[Callable[[numpy.ndarray, numpy.ndarray], float]]]): Measure distance between two individuals.
99		"""
100		ukwargs.pop('NP', None)
101		Algorithm.setParameters(self, NP=n, **ukwargs)
102		self.l0, self.nt, self.rho, self.gamma, self.beta, self.s, self.Distance = l0, nt, rho, gamma, beta, s, Distance
103
104		def getParameters(self):
105		r"""Get algorithms parameters values.
106
107		Returns:
108		Dict[str, Any]: TODO.
109		"""
110		d = Algorithm.getParameters(self)
111		d.pop('NP', None)
112		d.update({
113		'n': self.NP,
114		'l0': self.l0,
115		'nt': self.nt,
116		'rho': self.rho,
117		'gamma': self.gamma,
118		'beta': self.beta,
119		's': self.s,
120		'Distance': self.Distance
121		})
122		return d
123
124		def getNeighbors(self, i, r, GS, L):
125		r"""Get neighbours of glowworm.
126
127		Args:
128		i (int): Index of glowworm.
129		r (float): Neighborhood distance.
130		GS (numpy.ndarray):
131		L (numpy.ndarray[float]): Luciferin value of glowworm.
132
133		Returns:
134		numpy.ndarray[int]: Indexes of neighborhood glowworms.
135		"""
136		N = full(self.NP, 0)
137		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
138		return N
139
140		def probabilityes(self, i, N, L):
141		r"""Calculate probabilities for glowworm to movement.
142
143		Args:
144		i (int): Index of glowworm to search for probable movement.
145		N (numpy.ndarray[float]):
146		L (numpy.ndarray[float]):
147
148		Returns:
149		numpy.ndarray[float]: Probabilities for each glowworm in swarm.
150		"""
151		d, P = sum(L[where(N == 1)] - L[i]), full(self.NP, .0)
152		for j in range(self.NP): P[i] = ((L[j] - L[i]) / d) if N[j] == 1 else 0
153		return P
154
155		def moveSelect(self, pb, i):
156		r"""TODO.
157
158		Args:
159		pb:
160		i:
161
162		Returns:
163
164		"""
165		r, b_l, b_u = self.rand(), 0, 0
166		for j in range(self.NP):
167		b_l, b_u = b_u, b_u + pb[i]
168		if b_l < r < b_u: return j
169		return self.randint(self.NP)
170
171		def calcLuciferin(self, L, GS_f):
172		r"""TODO.
173
174		Args:
175		L:
176		GS_f:
177
178		Returns:
179
180		"""
181		return (1 - self.rho) * L + self.gamma * GS_f
182
183		def rangeUpdate(self, R, N, rs):
184		r"""TODO.
185
186		Args:
187		R:
188		N:
189		rs:
190
191		Returns:
192
193		"""
194		return R + self.beta * (self.nt - sum(N))
195
196		def initPopulation(self, task):
197		r"""Initialize population.
198
199		Args:
200		task (Task): Optimization task.
201
202		Returns:
203		Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]:
204		1. Initialized population of glowwarms.
205		2. Initialized populations function/fitness values.
206		3. Additional arguments:
207		* L (numpy.ndarray): TODO.
208		* R (numpy.ndarray): TODO.
209		* rs (numpy.ndarray): TODO.
210		"""
211		GS, GS_f, d = Algorithm.initPopulation(self, task)
212		rs = euclidean(full(task.D, 0), task.bRange)
213		L, R = full(self.NP, self.l0), full(self.NP, rs)
214		d.update({'L': L, 'R': R, 'rs': rs})
215		return GS, GS_f, d
216
217		def runIteration(self, task, GS, GS_f, xb, fxb, L, R, rs, **dparams):
218		r"""Core function of GlowwormSwarmOptimization algorithm.
219
220		Args:
221		task (Task): Optimization taks.
222		GS (numpy.ndarray): Current population.
223		GS_f (numpy.ndarray): Current populations fitness/function values.
224		xb (numpy.ndarray): Global best individual.
225		fxb (float): Global best individuals function/fitness value.
226		L (numpy.ndarray):
227		R (numpy.ndarray):
228		rs (numpy.ndarray):
229		**dparams Dict[str, Any]: Additional arguments.
230
231		Returns:
232		Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, Dict[str, Any]]:
233		1. Initialized population of glowwarms.
234		2. Initialized populations function/fitness values.
235		3. New global best solution
236		4. New global best sloutions fitness/objective value.
237		5. Additional arguments:
238		* L (numpy.ndarray): TODO.
239		* R (numpy.ndarray): TODO.
240		* rs (numpy.ndarray): TODO.
241		"""
242		GSo, Ro = copy(GS), copy(R)
243		L = self.calcLuciferin(L, GS_f)
244		N = [self.getNeighbors(i, Ro[i], GSo, L) for i in range(self.NP)]
245		P = [self.probabilityes(i, N[i], L) for i in range(self.NP)]
246		j = [self.moveSelect(P[i], i) for i in range(self.NP)]
247		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)
248		for i in range(self.NP): R[i] = max(0, min(rs, self.rangeUpdate(Ro[i], N[i], rs)))
249		GS_f = apply_along_axis(task.eval, 1, GS)
250		xb, fxb = self.getBest(GS, GS_f, xb, fxb)
251		return GS, GS_f, xb, fxb, {'L': L, 'R': R, 'rs': rs}
252
253		class GlowwormSwarmOptimizationV1(GlowwormSwarmOptimization):
254		r"""Implementation of glowwarm swarm optimization.
255
256		Algorithm:
257		Glowwarm Swarm Optimization Algorithm
258
259		Date:
260		2018
261
262		Authors:
263		Klemen Berkovič
264
265		License:
266		MIT
267
268		Reference URL:
269		https://www.springer.com/gp/book/9783319515946
270
271		Reference paper:
272		Kaipa, Krishnanand N., and Debasish Ghose. Glowworm swarm optimization: theory, algorithms, and applications. Vol. 698. Springer, 2017.
273
274		Attributes:
275		Name (List[str]): List of strings representing algorithm names.
276		alpha (float): --
277
278		See Also:
279		* :class:`NiaPy.algorithms.basic.GlowwormSwarmOptimization`
280		"""
281		Name = ['GlowwormSwarmOptimizationV1', 'GSOv1']
282
283		def setParameters(self, **kwargs):
284		r"""Set default parameters of the algorithm.
285
286		Args:
287		**kwargs (dict): Additional arguments.
288		"""
289		GlowwormSwarmOptimization.setParameters(self, **kwargs)
290
291		def calcLuciferin(self, L, GS_f):
292		r"""TODO.
293
294		Args:
295		L:
296		GS_f:
297
298		Returns:
299
300		"""
301		return fmax(0, (1 - self.rho) * L + self.gamma * GS_f)
302
303		def rangeUpdate(self, R, N, rs):
304		r"""TODO.
305
306		Args:
307		R:
308		N:
309		rs:
310
311		Returns:
312
313		"""
314		return rs / (1 + self.beta * (sum(N) / (pi * rs ** 2)))
315
316		class GlowwormSwarmOptimizationV2(GlowwormSwarmOptimization):
317		r"""Implementation of glowwarm swarm optimization.
318
319		Algorithm:
320		Glowwarm Swarm Optimization Algorithm
321
322		Date:
323		2018
324
325		Authors:
326		Klemen Berkovič
327
328		License:
329		MIT
330
331		Reference URL:
332		https://www.springer.com/gp/book/9783319515946
333
334		Reference paper:
335		Kaipa, Krishnanand N., and Debasish Ghose. Glowworm swarm optimization: theory, algorithms, and applications. Vol. 698. Springer, 2017.
336
337		Attributes:
338		Name (List[str]): List of strings representing algorithm names.
339		alpha (float): --
340
341		See Also:
342		* :class:`NiaPy.algorithms.basic.GlowwormSwarmOptimization`
343		"""
344		Name = ['GlowwormSwarmOptimizationV2', 'GSOv2']
345
346		def setParameters(self, alpha=0.2, **kwargs):
347		r"""Set core parameters for GlowwormSwarmOptimizationV2 algorithm.
348
349		Args:
350		alpha (Optional[float]): --
351		**kwargs (Dict[str, Any]): Additional arguments.
352
353		See Also:
354		* :func:`NiaPy.algorithms.basic.GlowwormSwarmOptimization.setParameters`
355		"""
356		GlowwormSwarmOptimization.setParameters(self, **kwargs)
357		self.alpha = alpha
358
359		def rangeUpdate(self, P, N, rs):
360		r"""TODO.
361
362		Args:
363		P:
364		N:
365		rs:
366
367		Returns:
368		float: TODO
369		"""
370		return self.alpha + (rs - self.alpha) / (1 + self.beta * sum(N))
371
372		class GlowwormSwarmOptimizationV3(GlowwormSwarmOptimization):
373		r"""Implementation of glowwarm swarm optimization.
374
375		Algorithm:
376		Glowwarm Swarm Optimization Algorithm
377
378		Date:
379		2018
380
381		Authors:
382		Klemen Berkovič
383
384		License:
385		MIT
386
387		Reference URL:
388		https://www.springer.com/gp/book/9783319515946
389
390		Reference paper:
391		Kaipa, Krishnanand N., and Debasish Ghose. Glowworm swarm optimization: theory, algorithms, and applications. Vol. 698. Springer, 2017.
392
393		Attributes:
394		Name (List[str]): List of strings representing algorithm names.
395		beta1 (float): --
396
397		See Also:
398		* :class:`NiaPy.algorithms.basic.GlowwormSwarmOptimization`
399		"""
400		Name = ['GlowwormSwarmOptimizationV3', 'GSOv3']
401
402		def setParameters(self, beta1=0.2, **kwargs):
403		r"""Set core parameters for GlowwormSwarmOptimizationV3 algorithm.
404
405		Args:
406		beta1 (Optional[float]): --
407		**kwargs (Dict[str, Any]): Additional arguments.
408
409		See Also:
410		* :func:`NiaPy.algorithms.basic.GlowwormSwarmOptimization.setParameters`
411		"""
412		GlowwormSwarmOptimization.setParameters(self, **kwargs)
413		self.beta1 = beta1
414
415		def rangeUpdate(self, R, N, rs):
416		r"""TODO.
417
418		Args:
419		R:
420		N:
421		rs:
422
423		Returns:
424
425		"""
426		return R + (self.beta * sum(N)) if sum(N) < self.nt else (-self.beta1 * sum(N))
427
428		# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
429

NiaOrg / NiaPy

Pull Request — master (#233)

GlowwormSwarmOptimization.setParameters() A

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