NiaPy.algorithms.other.aso.AnarchicSocietyOptimization.algorithmInfo() - Code Metrics - Inspection of "Merge pull request #262 from lucijabrezocnik/maste..." - NiaOrg/NiaPy - Measure and Improve Code Quality continuously with Scrutinizer

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AnarchicSocietyOptimization.algorithmInfo() A

↳ Parent: NiaPy.algorithms.other.aso

Complexity

Conditions

Size

Total Lines	11
Code Lines	3

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	3
nop	0
dl	0
loc	11
rs	10
c	0
b	0
f	0

# encoding=utf8
import logging
from scipy.spatial.distance import euclidean
from numpy import apply_along_axis, argmin, full, inf, where, asarray, random as rand, sort, exp
from NiaPy.algorithms.algorithm import Algorithm
from NiaPy.util import fullArray

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

__all__ = ['AnarchicSocietyOptimization', 'Elitism', 'Sequential', 'Crossover']

def Elitism(x, xpb, xb, xr, MP_c, MP_s, MP_p, F, CR, task, rnd=rand):
	r"""Select the best of all three strategies.

	Args:
		x (numpy.ndarray): individual position.
		xpb (numpy.ndarray): individuals best position.
		xb (numpy.ndarray): current best position.
		xr (numpy.ndarray): random individual.
		MP_c (float): Fickleness index value.
		MP_s (float): External irregularity index value.
		MP_p (float): Internal irregularity index value.
		F (float): scale factor.
		CR (float): crossover factor.
		task (Task): optimization task.
		rnd (mtrand.randomstate): random number generator.

	Returns:
		Tuple[numpy.ndarray, float]:
			1. New position of individual
			2. New positions fitness/function value
	"""
	xn = [task.repair(MP_C(x, F, CR, MP_c, rnd), rnd=rnd), task.repair(MP_S(x, xr, xb, CR, MP_s, rnd), rnd=rnd), task.repair(MP_P(x, xpb, CR, MP_p, rnd), rnd=rnd)]
	xn_f = apply_along_axis(task.eval, 1, xn)
	ib = argmin(xn_f)
	return xn[ib], xn_f[ib]

def Sequential(x, xpb, xb, xr, MP_c, MP_s, MP_p, F, CR, task, rnd=rand):
	r"""Sequentialy combines all three strategies.

	Args:
		x (numpy.ndarray): individual position.
		xpb (numpy.ndarray): individuals best position.
		xb (numpy.ndarray): current best position.
		xr (numpy.ndarray): random individual.
		MP_c (float): Fickleness index value.
		MP_s (float): External irregularity index value.
		MP_p (float): Internal irregularity index value.
		F (float): scale factor.
		CR (float): crossover factor.
		task (Task): optimization task.
		rnd (mtrand.randomstate): random number generator.

	Returns:
		tuple[numpy.ndarray, float]:
			1. new position
			2. new positions function/fitness value
	"""
	xn = task.repair(MP_S(MP_P(MP_C(x, F, CR, MP_c, rnd), xpb, CR, MP_p, rnd), xr, xb, CR, MP_s, rnd), rnd=rnd)
	return xn, task.eval(xn)

def Crossover(x, xpb, xb, xr, MP_c, MP_s, MP_p, F, CR, task, rnd=rand):
	r"""Create a crossover over all three strategies.

	Args:
		x (numpy.ndarray): individual position.
		xpb (numpy.ndarray): individuals best position.
		xb (numpy.ndarray): current best position.
		xr (numpy.ndarray): random individual.
		MP_c (float): Fickleness index value.
		MP_s (float): External irregularity index value.
		MP_p (float): Internal irregularity index value.
		F (float): scale factor.
		CR (float): crossover factor.
		task (Task): optimization task.
		rnd (mtrand.randomstate): random number generator.

	Returns:
		Tuple[numpy.ndarray, float]:
			1. new position
			2. new positions function/fitness value
	"""
	xns = [task.repair(MP_C(x, F, CR, MP_c, rnd), rnd=rnd), task.repair(MP_S(x, xr, xb, CR, MP_s, rnd), rnd=rnd), task.repair(MP_P(x, xpb, CR, MP_p, rnd), rnd=rnd)]
	x = asarray([xns[rnd.randint(len(xns))][i] if rnd.rand() < CR else x[i] for i in range(len(x))])
	return x, task.eval(x)

def MP_C(x, F, CR, MP, rnd=rand):
	r"""Get bew position based on fickleness.

	Args:
		x (numpy.ndarray): Current individuals position.
		F (float): Scale factor.
		CR (float): Crossover probability.
		MP (float): Fickleness index value
		rnd (mtrand.RandomState): Random number generator

	Returns:
		numpy.ndarray: New position
	"""
	if MP < 0.5:
		b = sort(rnd.choice(len(x), 2, replace=False))
		x[b[0]:b[1]] = x[b[0]:b[1]] + F * rnd.normal(0, 1, b[1] - b[0])
		return x
	return asarray([x[i] + F * rnd.normal(0, 1) if rnd.rand() < CR else x[i] for i in range(len(x))])

def MP_S(x, xr, xb, CR, MP, rnd=rand):
	r"""Get new position based on external irregularity.

	Args:
		x (numpy.ndarray): Current individuals position.
		xr (numpy.ndarray): Random individuals position.
		xb (numpy.ndarray): Global best individuals position.
		CR (float): Crossover probability.
		MP (float): External irregularity index.
		rnd (mtrand.RandomState): Random number generator.

	Returns:
		numpy.ndarray: New position.
	"""
	if MP < 0.25:
		b = sort(rnd.choice(len(x), 2, replace=False))
		x[b[0]:b[1]] = xb[b[0]:b[1]]
		return x
	elif MP < 0.5: return asarray([xb[i] if rnd.rand() < CR else x[i] for i in range(len(x))])
	elif MP < 0.75:
		b = sort(rnd.choice(len(x), 2, replace=False))
		x[b[0]:b[1]] = xr[b[0]:b[1]]
		return x
	return asarray([xr[i] if rnd.rand() < CR else x[i] for i in range(len(x))])

def MP_P(x, xpb, CR, MP, rnd=rand):
	r"""Get new position based on internal irregularity.

	Args:
		x (numpy.ndarray): Current individuals position.
		xpb (numpy.ndarray): Current individuals personal best position.
		CR (float): Crossover probability.
		MP (float): Internal irregularity index value.
		rnd (mtrand.RandomState): Random number generator.

	Returns:
		numpy.ndarray: Current individuals new position.
	"""
	if MP < 0.5:
		b = sort(rnd.choice(len(x), 2, replace=False))
		x[b[0]:b[1]] = xpb[b[0]:b[1]]
		return x
	return asarray([xpb[i] if rnd.rand() < CR else x[i] for i in range(len(x))])

class AnarchicSocietyOptimization(Algorithm):
	r"""Implementation of Anarchic Society Optimization algorithm.

	Algorithm:
		Anarchic Society Optimization algorithm

	Date:
		2018

	Authors:
		Klemen Berkovič

	License:
		MIT

	Reference paper:
		Ahmadi-Javid, Amir. "Anarchic Society Optimization: A human-inspired method." Evolutionary Computation (CEC), 2011 IEEE Congress on. IEEE, 2011.

	Attributes:
		Name (list of str): List of stings representing name of algorithm.
		alpha (List[float]): Factor for fickleness index function :math:`\in [0, 1]`.
		gamma (List[float]): Factor for external irregularity index function :math:`\in [0, \infty)`.
		theta (List[float]): Factor for internal irregularity index function :math:`\in [0, \infty)`.
		d (Callable[[float, float], float]): function that takes two arguments that are function values and calcs the distance between them.
		dn (Callable[[numpy.ndarray, numpy.ndarray], float]): function that takes two arguments that are points in function landscape and calcs the distance between them.
		nl (float): Normalized range for neighborhood search :math:`\in (0, 1]`.
		F (float): Mutation parameter.
		CR (float): Crossover parameter :math:`\in [0, 1]`.
		Combination (Callable[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, float, float, float, float, float, float, Task, mtrand.RandomState]): Function for combining individuals to get new position/individual.

	See Also:
		* :class:`NiaPy.algorithms.Algorithm`
	"""
	Name = ['AnarchicSocietyOptimization', 'ASO']

	@staticmethod
	def algorithmInfo():
		r"""Get basic information about the algorithm.

		Returns:
			str: Basic information.

		See Also:
			:func:`NiaPy.algorithms.algorithm.Algorithm.algorithmInfo`
		"""
		return r"""Ahmadi-Javid, Amir. "Anarchic Society Optimization: A human-inspired method." Evolutionary Computation (CEC), 2011 IEEE Congress on. IEEE, 2011."""

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

		Returns:
			Dict[str, Callable]:
				* alpha (Callable): TODO
				* gamma (Callable): TODO
				* theta (Callable): TODO
				* nl (Callable): TODO
				* F (Callable[[Union[float, int]], bool]): TODO
				* CR (Callable[[Union[float, int]], bool]): TODO

		See Also:
			* :func:`NiaPy.algorithms.Algorithm.typeParameters`
		"""
		d = Algorithm.typeParameters()
		d.update({
			'alpha': lambda x: True,
			'gamma': lambda x: True,
			'theta': lambda x: True,
			'nl': lambda x: True,
			'F': lambda x: isinstance(x, (int, float)) and x > 0,
			'CR': lambda x: isinstance(x, float) and 0 <= x <= 1
		})
		return d

	def setParameters(self, NP=43, alpha=(1, 0.83), gamma=(1.17, 0.56), theta=(0.932, 0.832), d=euclidean, dn=euclidean, nl=1, F=1.2, CR=0.25, Combination=Elitism, **ukwargs):
		r"""Set the parameters for the algorith.

		Arguments:
			alpha (Optional[List[float]]): Factor for fickleness index function :math:`\in [0, 1]`.
			gamma (Optional[List[float]]): Factor for external irregularity index function :math:`\in [0, \infty)`.
			theta (Optional[List[float]]): Factor for internal irregularity index function :math:`\in [0, \infty)`.
			d (Optional[Callable[[float, float], float]]): function that takes two arguments that are function values and calcs the distance between them.
			dn (Optional[Callable[[numpy.ndarray, numpy.ndarray], float]]): function that takes two arguments that are points in function landscape and calcs the distance between them.
			nl (Optional[float]): Normalized range for neighborhood search :math:`\in (0, 1]`.
			F (Optional[float]): Mutation parameter.
			CR (Optional[float]): Crossover parameter :math:`\in [0, 1]`.
			Combination (Optional[Callable[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, float, float, float, float, float, float, Task, mtrand.RandomState]]): Function for combining individuals to get new position/individual.

		See Also:
			* :func:`NiaPy.algorithms.Algorithm.setParameters`
			* Combination methods:
				* :func:`NiaPy.algorithms.other.Elitism`
				* :func:`NiaPy.algorithms.other.Crossover`
				* :func:`NiaPy.algorithms.other.Sequential`
		"""
		Algorithm.setParameters(self, NP=NP, **ukwargs)
		self.alpha, self.gamma, self.theta, self.d, self.dn, self.nl, self.F, self.CR, self.Combination = alpha, gamma, theta, d, dn, nl, F, CR, Combination

	def init(self, task):
		r"""Initialize dynamic parameters of algorithm.

		Args:
			task (Task): Optimization task.

		Returns:
			Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]
				1. Array of `self.alpha` propagated values
				2. Array of `self.gamma` propagated values
				3. Array of `self.theta` propagated values
		"""
		return fullArray(self.alpha, self.NP), fullArray(self.gamma, self.NP), fullArray(self.theta, self.NP)

	def FI(self, x_f, xpb_f, xb_f, alpha):
		r"""Get fickleness index.

		Args:
			x_f (float): Individuals fitness/function value.
			xpb_f (float): Individuals personal best fitness/function value.
			xb_f (float): Current best found individuals fitness/function value.
			alpha (float): TODO.

		Returns:
			float: Fickleness index.
		"""
		return 1 - alpha * xb_f / x_f - (1 - alpha) * xpb_f / x_f

	def EI(self, x_f, xnb_f, gamma):
		r"""Get external irregularity index.

		Args:
			x_f (float): Individuals fitness/function value.
			xnb_f (float): Individuals new fitness/function value.
			gamma (float): TODO.

		Returns:
			float: External irregularity index.
		"""
		return 1 - exp(-gamma * self.d(x_f, xnb_f))

	def II(self, x_f, xpb_f, theta):
		r"""Get internal irregularity index.

		Args:
			x_f (float): Individuals fitness/function value.
			xpb_f (float): Individuals personal best fitness/function value.
			theta (float): TODO.

		Returns:
			float: Internal irregularity index
		"""
		return 1 - exp(-theta * self.d(x_f, xpb_f))

	def getBestNeighbors(self, i, X, X_f, rs):
		r"""Get neighbors of individual.

		Mesurment of distance for neighborhud is defined with `self.nl`.
		Function for calculating distances is define with `self.dn`.

		Args:
			i (int): Index of individual for hum we are looking for neighbours.
			X (numpy.ndarray): Current population.
			X_f (numpy.ndarray[float]): Current population fitness/function values.
			rs (numpy.ndarray[float]): Distance between individuals.

		Returns:
			numpy.ndarray[int]: Indexes that represent individuals closest to `i`-th individual.
		"""
		nn = asarray([self.dn(X[i], X[j]) / rs for j in range(len(X))])
		return argmin(X_f[where(nn <= self.nl)])

	def uBestAndPBest(self, X, X_f, Xpb, Xpb_f):
		r"""Update personal best solution of all individuals in population.

		Args:
			X (numpy.ndarray): Current population.
			X_f (numpy.ndarray[float]): Current population fitness/function values.
			Xpb (numpy.ndarray): Current population best positions.
			Xpb_f (numpy.ndarray[float]): Current populations best positions fitness/function values.

		Returns:
			Tuple[numpy.ndarray, numpy.ndarray[float], numpy.ndarray, float]:
				1. New personal best positions for current population.
				2. New personal best positions function/fitness values for current population.
				3. New best individual.
				4. New best individual fitness/function value.
		"""
		ix_pb = where(X_f < Xpb_f)
		Xpb[ix_pb], Xpb_f[ix_pb] = X[ix_pb], X_f[ix_pb]
		return Xpb, Xpb_f

	def initPopulation(self, task):
		r"""Initialize first population and additional arguments.

		Args:
			task (Task): Optimization task

		Returns:
			Tuple[numpy.ndarray, numpy.ndarray, dict]:
				1. Initialized population
				2. Initialized population fitness/function values
				3. Dict[str, Any]:
					* Xpb (numpy.ndarray): Initialized populations best positions.
					* Xpb_f (numpy.ndarray): Initialized populations best positions function/fitness values.
					* alpha (numpy.ndarray):
					* gamma (numpy.ndarray):
					* theta (numpy.ndarray):
					* rs (float): Distance of search space.

		See Also:
			* :func:`NiaPy.algorithms.algorithm.Algorithm.initPopulation`
			* :func:`NiaPy.algorithms.other.aso.AnarchicSocietyOptimization.init`
		"""
		X, X_f, d = Algorithm.initPopulation(self, task)
		alpha, gamma, theta = self.init(task)
		Xpb, Xpb_f = self.uBestAndPBest(X, X_f, full([self.NP, task.D], 0.0), full(self.NP, task.optType.value * inf))
		d.update({'Xpb': Xpb, 'Xpb_f': Xpb_f, 'alpha': alpha, 'gamma': gamma, 'theta': theta, 'rs': self.d(task.Upper, task.Lower)})
		return X, X_f, d

	def runIteration(self, task, X, X_f, xb, fxb, Xpb, Xpb_f, alpha, gamma, theta, rs, **dparams):
		r"""Core function of AnarchicSocietyOptimization algorithm.

		Args:
			task (Task): Optimization task.
			X (numpy.ndarray): Current populations positions.
			X_f (numpy.ndarray): Current populations function/fitness values.
			xb (numpy.ndarray): Current global best individuals position.
			fxb (float): Current global best individual function/fitness value.
			Xpb (numpy.ndarray): Current populations best positions.
			Xpb_f (numpy.ndarray): Current population best positions function/fitness values.
			alpha (numpy.ndarray): TODO.
			gamma (numpy.ndarray):
			theta (numpy.ndarray):
			**dparams: Additional arguments.

		Returns:
			Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, dict]:
				1. Initialized population
				2. Initialized population fitness/function values
				3. New global best solution
				4. New global best solutions fitness/objective value
				5. Dict[str, Union[float, int, numpy.ndarray]:
					* Xpb (numpy.ndarray): Initialized populations best positions.
					* Xpb_f (numpy.ndarray): Initialized populations best positions function/fitness values.
					* alpha (numpy.ndarray):
					* gamma (numpy.ndarray):
					* theta (numpy.ndarray):
					* rs (float): Distance of search space.
		"""
		Xin = [self.getBestNeighbors(i, X, X_f, rs) for i in range(len(X))]
		MP_c, MP_s, MP_p = asarray([self.FI(X_f[i], Xpb_f[i], fxb, alpha[i]) for i in range(len(X))]), asarray([self.EI(X_f[i], X_f[Xin[i]], gamma[i]) for i in range(len(X))]), asarray([self.II(X_f[i], Xpb_f[i], theta[i]) for i in range(len(X))])
		Xtmp = asarray([self.Combination(X[i], Xpb[i], xb, X[self.randint(len(X), skip=[i])], MP_c[i], MP_s[i], MP_p[i], self.F, self.CR, task, self.Rand) for i in range(len(X))])
		X, X_f = asarray([Xtmp[i][0] for i in range(len(X))]), asarray([Xtmp[i][1] for i in range(len(X))])
		Xpb, Xpb_f = self.uBestAndPBest(X, X_f, Xpb, Xpb_f)
		xb, fxb = self.getBest(X, X_f, xb, fxb)
		return X, X_f, xb, fxb, {'Xpb': Xpb, 'Xpb_f': Xpb_f, 'alpha': alpha, 'gamma': gamma, 'theta': theta, 'rs': rs}

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


1			# encoding=utf8
2			import logging
3			from scipy.spatial.distance import euclidean
4			from numpy import apply_along_axis, argmin, full, inf, where, asarray, random as rand, sort, exp
5			from NiaPy.algorithms.algorithm import Algorithm
6			from NiaPy.util import fullArray
7
8			logging.basicConfig()
9			logger = logging.getLogger('NiaPy.algorithms.other')
10			logger.setLevel('INFO')
11
12			__all__ = ['AnarchicSocietyOptimization', 'Elitism', 'Sequential', 'Crossover']
13
14			def Elitism(x, xpb, xb, xr, MP_c, MP_s, MP_p, F, CR, task, rnd=rand):
15			r"""Select the best of all three strategies.
16
17			Args:
18			x (numpy.ndarray): individual position.
19			xpb (numpy.ndarray): individuals best position.
20			xb (numpy.ndarray): current best position.
21			xr (numpy.ndarray): random individual.
22			MP_c (float): Fickleness index value.
23			MP_s (float): External irregularity index value.
24			MP_p (float): Internal irregularity index value.
25			F (float): scale factor.
26			CR (float): crossover factor.
27			task (Task): optimization task.
28			rnd (mtrand.randomstate): random number generator.
29
30			Returns:
31			Tuple[numpy.ndarray, float]:
32			1. New position of individual
33			2. New positions fitness/function value
34			"""
35			xn = [task.repair(MP_C(x, F, CR, MP_c, rnd), rnd=rnd), task.repair(MP_S(x, xr, xb, CR, MP_s, rnd), rnd=rnd), task.repair(MP_P(x, xpb, CR, MP_p, rnd), rnd=rnd)]
36			xn_f = apply_along_axis(task.eval, 1, xn)
37			ib = argmin(xn_f)
38			return xn[ib], xn_f[ib]
39
40			def Sequential(x, xpb, xb, xr, MP_c, MP_s, MP_p, F, CR, task, rnd=rand):
41			r"""Sequentialy combines all three strategies.
42
43			Args:
44			x (numpy.ndarray): individual position.
45			xpb (numpy.ndarray): individuals best position.
46			xb (numpy.ndarray): current best position.
47			xr (numpy.ndarray): random individual.
48			MP_c (float): Fickleness index value.
49			MP_s (float): External irregularity index value.
50			MP_p (float): Internal irregularity index value.
51			F (float): scale factor.
52			CR (float): crossover factor.
53			task (Task): optimization task.
54			rnd (mtrand.randomstate): random number generator.
55
56			Returns:
57			tuple[numpy.ndarray, float]:
58			1. new position
59			2. new positions function/fitness value
60			"""
61			xn = task.repair(MP_S(MP_P(MP_C(x, F, CR, MP_c, rnd), xpb, CR, MP_p, rnd), xr, xb, CR, MP_s, rnd), rnd=rnd)
62			return xn, task.eval(xn)
63
64			def Crossover(x, xpb, xb, xr, MP_c, MP_s, MP_p, F, CR, task, rnd=rand):
65			r"""Create a crossover over all three strategies.
66
67			Args:
68			x (numpy.ndarray): individual position.
69			xpb (numpy.ndarray): individuals best position.
70			xb (numpy.ndarray): current best position.
71			xr (numpy.ndarray): random individual.
72			MP_c (float): Fickleness index value.
73			MP_s (float): External irregularity index value.
74			MP_p (float): Internal irregularity index value.
75			F (float): scale factor.
76			CR (float): crossover factor.
77			task (Task): optimization task.
78			rnd (mtrand.randomstate): random number generator.
79
80			Returns:
81			Tuple[numpy.ndarray, float]:
82			1. new position
83			2. new positions function/fitness value
84			"""
85			xns = [task.repair(MP_C(x, F, CR, MP_c, rnd), rnd=rnd), task.repair(MP_S(x, xr, xb, CR, MP_s, rnd), rnd=rnd), task.repair(MP_P(x, xpb, CR, MP_p, rnd), rnd=rnd)]
86			x = asarray([xns[rnd.randint(len(xns))][i] if rnd.rand() < CR else x[i] for i in range(len(x))])
87			return x, task.eval(x)
88
89			def MP_C(x, F, CR, MP, rnd=rand):
90			r"""Get bew position based on fickleness.
91
92			Args:
93			x (numpy.ndarray): Current individuals position.
94			F (float): Scale factor.
95			CR (float): Crossover probability.
96			MP (float): Fickleness index value
97			rnd (mtrand.RandomState): Random number generator
98
99			Returns:
100			numpy.ndarray: New position
101			"""
102			if MP < 0.5:
103			b = sort(rnd.choice(len(x), 2, replace=False))
104			x[b[0]:b[1]] = x[b[0]:b[1]] + F * rnd.normal(0, 1, b[1] - b[0])
105			return x
106			return asarray([x[i] + F * rnd.normal(0, 1) if rnd.rand() < CR else x[i] for i in range(len(x))])
107
108			def MP_S(x, xr, xb, CR, MP, rnd=rand):
109			r"""Get new position based on external irregularity.
110
111			Args:
112			x (numpy.ndarray): Current individuals position.
113			xr (numpy.ndarray): Random individuals position.
114			xb (numpy.ndarray): Global best individuals position.
115			CR (float): Crossover probability.
116			MP (float): External irregularity index.
117			rnd (mtrand.RandomState): Random number generator.
118
119			Returns:
120			numpy.ndarray: New position.
121			"""
122			if MP < 0.25:
123			b = sort(rnd.choice(len(x), 2, replace=False))
124			x[b[0]:b[1]] = xb[b[0]:b[1]]
125			return x
126			elif MP < 0.5: return asarray([xb[i] if rnd.rand() < CR else x[i] for i in range(len(x))])
127			elif MP < 0.75:
128			b = sort(rnd.choice(len(x), 2, replace=False))
129			x[b[0]:b[1]] = xr[b[0]:b[1]]
130			return x
131			return asarray([xr[i] if rnd.rand() < CR else x[i] for i in range(len(x))])
132
133			def MP_P(x, xpb, CR, MP, rnd=rand):
134			r"""Get new position based on internal irregularity.
135
136			Args:
137			x (numpy.ndarray): Current individuals position.
138			xpb (numpy.ndarray): Current individuals personal best position.
139			CR (float): Crossover probability.
140			MP (float): Internal irregularity index value.
141			rnd (mtrand.RandomState): Random number generator.
142
143			Returns:
144			numpy.ndarray: Current individuals new position.
145			"""
146			if MP < 0.5:
147			b = sort(rnd.choice(len(x), 2, replace=False))
148			x[b[0]:b[1]] = xpb[b[0]:b[1]]
149			return x
150			return asarray([xpb[i] if rnd.rand() < CR else x[i] for i in range(len(x))])
151
152			class AnarchicSocietyOptimization(Algorithm):
153			r"""Implementation of Anarchic Society Optimization algorithm.
154
155			Algorithm:
156			Anarchic Society Optimization algorithm
157
158			Date:
159			2018
160
161			Authors:
162			Klemen Berkovič
163
164			License:
165			MIT
166
167			Reference paper:
168			Ahmadi-Javid, Amir. "Anarchic Society Optimization: A human-inspired method." Evolutionary Computation (CEC), 2011 IEEE Congress on. IEEE, 2011.
169
170			Attributes:
171			Name (list of str): List of stings representing name of algorithm.
172			alpha (List[float]): Factor for fickleness index function :math:`\in [0, 1]`.
173			gamma (List[float]): Factor for external irregularity index function :math:`\in [0, \infty)`.
174			theta (List[float]): Factor for internal irregularity index function :math:`\in [0, \infty)`.
175			d (Callable[[float, float], float]): function that takes two arguments that are function values and calcs the distance between them.
176			dn (Callable[[numpy.ndarray, numpy.ndarray], float]): function that takes two arguments that are points in function landscape and calcs the distance between them.
177			nl (float): Normalized range for neighborhood search :math:`\in (0, 1]`.
178			F (float): Mutation parameter.
179			CR (float): Crossover parameter :math:`\in [0, 1]`.
180			Combination (Callable[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, float, float, float, float, float, float, Task, mtrand.RandomState]): Function for combining individuals to get new position/individual.
181
182			See Also:
183			* :class:`NiaPy.algorithms.Algorithm`
184			"""
185			Name = ['AnarchicSocietyOptimization', 'ASO']
186
187			@staticmethod
188			def algorithmInfo():
189			r"""Get basic information about the algorithm.
190
191			Returns:
192			str: Basic information.
193
194			See Also:
195			:func:`NiaPy.algorithms.algorithm.Algorithm.algorithmInfo`
196			"""
197			return r"""Ahmadi-Javid, Amir. "Anarchic Society Optimization: A human-inspired method." Evolutionary Computation (CEC), 2011 IEEE Congress on. IEEE, 2011."""
198
199			@staticmethod
200			def typeParameters():
201			r"""Get dictionary with functions for checking values of parameters.
202
203			Returns:
204			Dict[str, Callable]:
205			* alpha (Callable): TODO
206			* gamma (Callable): TODO
207			* theta (Callable): TODO
208			* nl (Callable): TODO
209			* F (Callable[[Union[float, int]], bool]): TODO
210			* CR (Callable[[Union[float, int]], bool]): TODO
211
212			See Also:
213			* :func:`NiaPy.algorithms.Algorithm.typeParameters`
214			"""
215			d = Algorithm.typeParameters()
216			d.update({
217			'alpha': lambda x: True,
218			'gamma': lambda x: True,
219			'theta': lambda x: True,
220			'nl': lambda x: True,
221			'F': lambda x: isinstance(x, (int, float)) and x > 0,
222			'CR': lambda x: isinstance(x, float) and 0 <= x <= 1
223			})
224			return d
225
226			def setParameters(self, NP=43, alpha=(1, 0.83), gamma=(1.17, 0.56), theta=(0.932, 0.832), d=euclidean, dn=euclidean, nl=1, F=1.2, CR=0.25, Combination=Elitism, **ukwargs):
227			r"""Set the parameters for the algorith.
228
229			Arguments:
230			alpha (Optional[List[float]]): Factor for fickleness index function :math:`\in [0, 1]`.
231			gamma (Optional[List[float]]): Factor for external irregularity index function :math:`\in [0, \infty)`.
232			theta (Optional[List[float]]): Factor for internal irregularity index function :math:`\in [0, \infty)`.
233			d (Optional[Callable[[float, float], float]]): function that takes two arguments that are function values and calcs the distance between them.
234			dn (Optional[Callable[[numpy.ndarray, numpy.ndarray], float]]): function that takes two arguments that are points in function landscape and calcs the distance between them.
235			nl (Optional[float]): Normalized range for neighborhood search :math:`\in (0, 1]`.
236			F (Optional[float]): Mutation parameter.
237			CR (Optional[float]): Crossover parameter :math:`\in [0, 1]`.
238			Combination (Optional[Callable[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, float, float, float, float, float, float, Task, mtrand.RandomState]]): Function for combining individuals to get new position/individual.
239
240			See Also:
241			* :func:`NiaPy.algorithms.Algorithm.setParameters`
242			* Combination methods:
243			* :func:`NiaPy.algorithms.other.Elitism`
244			* :func:`NiaPy.algorithms.other.Crossover`
245			* :func:`NiaPy.algorithms.other.Sequential`
246			"""
247			Algorithm.setParameters(self, NP=NP, **ukwargs)
248			self.alpha, self.gamma, self.theta, self.d, self.dn, self.nl, self.F, self.CR, self.Combination = alpha, gamma, theta, d, dn, nl, F, CR, Combination
249
250			def init(self, task):
251			r"""Initialize dynamic parameters of algorithm.
252
253			Args:
254			task (Task): Optimization task.
255
256			Returns:
257			Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]
258			1. Array of `self.alpha` propagated values
259			2. Array of `self.gamma` propagated values
260			3. Array of `self.theta` propagated values
261			"""
262			return fullArray(self.alpha, self.NP), fullArray(self.gamma, self.NP), fullArray(self.theta, self.NP)
263
264			def FI(self, x_f, xpb_f, xb_f, alpha):
265			r"""Get fickleness index.
266
267			Args:
268			x_f (float): Individuals fitness/function value.
269			xpb_f (float): Individuals personal best fitness/function value.
270			xb_f (float): Current best found individuals fitness/function value.
271			alpha (float): TODO.
272
273			Returns:
274			float: Fickleness index.
275			"""
276			return 1 - alpha * xb_f / x_f - (1 - alpha) * xpb_f / x_f
277
278			def EI(self, x_f, xnb_f, gamma):
279			r"""Get external irregularity index.
280
281			Args:
282			x_f (float): Individuals fitness/function value.
283			xnb_f (float): Individuals new fitness/function value.
284			gamma (float): TODO.
285
286			Returns:
287			float: External irregularity index.
288			"""
289			return 1 - exp(-gamma * self.d(x_f, xnb_f))
290
291			def II(self, x_f, xpb_f, theta):
292			r"""Get internal irregularity index.
293
294			Args:
295			x_f (float): Individuals fitness/function value.
296			xpb_f (float): Individuals personal best fitness/function value.
297			theta (float): TODO.
298
299			Returns:
300			float: Internal irregularity index
301			"""
302			return 1 - exp(-theta * self.d(x_f, xpb_f))
303
304			def getBestNeighbors(self, i, X, X_f, rs):
305			r"""Get neighbors of individual.
306
307			Mesurment of distance for neighborhud is defined with `self.nl`.
308			Function for calculating distances is define with `self.dn`.
309
310			Args:
311			i (int): Index of individual for hum we are looking for neighbours.
312			X (numpy.ndarray): Current population.
313			X_f (numpy.ndarray[float]): Current population fitness/function values.
314			rs (numpy.ndarray[float]): Distance between individuals.
315
316			Returns:
317			numpy.ndarray[int]: Indexes that represent individuals closest to `i`-th individual.
318			"""
319			nn = asarray([self.dn(X[i], X[j]) / rs for j in range(len(X))])
320			return argmin(X_f[where(nn <= self.nl)])
321
322			def uBestAndPBest(self, X, X_f, Xpb, Xpb_f):
323			r"""Update personal best solution of all individuals in population.
324
325			Args:
326			X (numpy.ndarray): Current population.
327			X_f (numpy.ndarray[float]): Current population fitness/function values.
328			Xpb (numpy.ndarray): Current population best positions.
329			Xpb_f (numpy.ndarray[float]): Current populations best positions fitness/function values.
330
331			Returns:
332			Tuple[numpy.ndarray, numpy.ndarray[float], numpy.ndarray, float]:
333			1. New personal best positions for current population.
334			2. New personal best positions function/fitness values for current population.
335			3. New best individual.
336			4. New best individual fitness/function value.
337			"""
338			ix_pb = where(X_f < Xpb_f)
339			Xpb[ix_pb], Xpb_f[ix_pb] = X[ix_pb], X_f[ix_pb]
340			return Xpb, Xpb_f
341
342			def initPopulation(self, task):
343			r"""Initialize first population and additional arguments.
344
345			Args:
346			task (Task): Optimization task
347
348			Returns:
349			Tuple[numpy.ndarray, numpy.ndarray, dict]:
350			1. Initialized population
351			2. Initialized population fitness/function values
352			3. Dict[str, Any]:
353			* Xpb (numpy.ndarray): Initialized populations best positions.
354			* Xpb_f (numpy.ndarray): Initialized populations best positions function/fitness values.
355			* alpha (numpy.ndarray):
356			* gamma (numpy.ndarray):
357			* theta (numpy.ndarray):
358			* rs (float): Distance of search space.
359
360			See Also:
361			* :func:`NiaPy.algorithms.algorithm.Algorithm.initPopulation`
362			* :func:`NiaPy.algorithms.other.aso.AnarchicSocietyOptimization.init`
363			"""
364			X, X_f, d = Algorithm.initPopulation(self, task)
365			alpha, gamma, theta = self.init(task)
366			Xpb, Xpb_f = self.uBestAndPBest(X, X_f, full([self.NP, task.D], 0.0), full(self.NP, task.optType.value * inf))
367			d.update({'Xpb': Xpb, 'Xpb_f': Xpb_f, 'alpha': alpha, 'gamma': gamma, 'theta': theta, 'rs': self.d(task.Upper, task.Lower)})
368			return X, X_f, d
369
370			def runIteration(self, task, X, X_f, xb, fxb, Xpb, Xpb_f, alpha, gamma, theta, rs, **dparams):
371			r"""Core function of AnarchicSocietyOptimization algorithm.
372
373			Args:
374			task (Task): Optimization task.
375			X (numpy.ndarray): Current populations positions.
376			X_f (numpy.ndarray): Current populations function/fitness values.
377			xb (numpy.ndarray): Current global best individuals position.
378			fxb (float): Current global best individual function/fitness value.
379			Xpb (numpy.ndarray): Current populations best positions.
380			Xpb_f (numpy.ndarray): Current population best positions function/fitness values.
381			alpha (numpy.ndarray): TODO.
382			gamma (numpy.ndarray):
383			theta (numpy.ndarray):
384			**dparams: Additional arguments.
385
386			Returns:
387			Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, dict]:
388			1. Initialized population
389			2. Initialized population fitness/function values
390			3. New global best solution
391			4. New global best solutions fitness/objective value
392			5. Dict[str, Union[float, int, numpy.ndarray]:
393			* Xpb (numpy.ndarray): Initialized populations best positions.
394			* Xpb_f (numpy.ndarray): Initialized populations best positions function/fitness values.
395			* alpha (numpy.ndarray):
396			* gamma (numpy.ndarray):
397			* theta (numpy.ndarray):
398			* rs (float): Distance of search space.
399			"""
400			Xin = [self.getBestNeighbors(i, X, X_f, rs) for i in range(len(X))]
401			MP_c, MP_s, MP_p = asarray([self.FI(X_f[i], Xpb_f[i], fxb, alpha[i]) for i in range(len(X))]), asarray([self.EI(X_f[i], X_f[Xin[i]], gamma[i]) for i in range(len(X))]), asarray([self.II(X_f[i], Xpb_f[i], theta[i]) for i in range(len(X))])
402			Xtmp = asarray([self.Combination(X[i], Xpb[i], xb, X[self.randint(len(X), skip=[i])], MP_c[i], MP_s[i], MP_p[i], self.F, self.CR, task, self.Rand) for i in range(len(X))])
403			X, X_f = asarray([Xtmp[i][0] for i in range(len(X))]), asarray([Xtmp[i][1] for i in range(len(X))])
404			Xpb, Xpb_f = self.uBestAndPBest(X, X_f, Xpb, Xpb_f)
405			xb, fxb = self.getBest(X, X_f, xb, fxb)
406			return X, X_f, xb, fxb, {'Xpb': Xpb, 'Xpb_f': Xpb_f, 'alpha': alpha, 'gamma': gamma, 'theta': theta, 'rs': rs}
407
408			# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
409

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