Merge pull request #1 from lucijabrezocnik/fixes

Fixes

NiaPy/algorithms/basic/abc.py

@@ -62,6 +62,18 @@ class ArtificialBeeColonyAlgorithm(Algorithm):
 	"""
 	Name = ['ArtificialBeeColonyAlgorithm', 'ABC']
 
+	@staticmethod
+	def algorithmInfo():
+		r"""Get algorithms information.
+
+		Returns:
+			str: Algorithm information.
+
+		See Also:
+			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
+		"""
+		return r"""Karaboga, D., and Bahriye B. "A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm." Journal of global optimization 39.3 (2007): 459-471."""
+
 	@staticmethod
 	def typeParameters():
 		r"""Return functions for checking values of parameters.

NiaPy/algorithms/basic/ba.py

@@ -47,8 +47,11 @@ def algorithmInfo():
 
 		Returns:
 			str: Algorithm information.
+
+		See Also:
+			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
 		"""
-		return r'''Yang, Xin-She. "A new metaheuristic bat-inspired algorithm." Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin, Heidelberg, 2010. 65-74.'''
+		return r"""Yang, Xin-She. "A new metaheuristic bat-inspired algorithm." Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin, Heidelberg, 2010. 65-74."""
 
 	@staticmethod
 	def typeParameters():

NiaPy/algorithms/basic/bea.py

@@ -45,12 +45,15 @@ class BeesAlgorithm(Algorithm):
 
 	@staticmethod
 	def algorithmInfo():
-		return r"""
-        Description: A new population-based search algorithm called the Bees Algorithm (BA) is presented. The algorithm mimics the food foraging behaviour of swarms of honey bees.
-        Authors: D.T. Pham, A. Ghanbarzadeh,  E. Koç, S. Otri,  S. Rahim, M. Zaidi
-        Year: 2006
-        Main reference: DT Pham, A Ghanbarzadeh, E Koc, S Otri, S Rahim, and M Zaidi. The bees algorithm-a novel tool for complex optimisation problems. In Proceedings of the 2nd Virtual International Conference on Intelligent Production Machines and Systems (IPROMS 2006), pages 454–459, 2006
-    """
+		r"""Get information about algorithm.
+
+		Returns:
+			str: Algorithm information
+
+		See Also:
+			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
+		"""
+		return r"""DT Pham, A Ghanbarzadeh, E Koc, S Otri, S Rahim, and M Zaidi. The bees algorithm-a novel tool for complex optimisation problems. In Proceedings of the 2nd Virtual International Conference on Intelligent Production Machines and Systems (IPROMS 2006), pages 454–459, 2006"""
 
 	@staticmethod
 	def typeParameters():
@@ -118,7 +121,7 @@ def beeDance(self, x, task, ngh):
 
 		Args:
 			 x (numpy.ndarray): One instance from the population.
-			 task (Task): Optimization task
+			 task (Task): Optimization task.
 			 ngh (float): A small value for patch search.
 
 		Returns:
@@ -191,8 +194,8 @@ def runIteration(self, task, BeesPosition, BeesCost, xb, fxb, ngh, **dparams):
 			 Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, Dict[str, Any]]:
 				  1. New population.
 				  2. New population fitness/function values.
-				  3. New global best solution
-				  4. New global best fitness/objective value
+				  3. New global best solution.
+				  4. New global best fitness/objective value.
 				  5. Additional arguments:
 						* ngh (float): A small value used for patches.
 		"""

NiaPy/algorithms/basic/ca.py

@@ -153,6 +153,9 @@ def algorithmInfo():
 
 		Returns:
 			str: Algorithm information
+
+		See Also:
+			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
 		"""
 		return r'''Ali, Ramzy. (2016). Novel Optimization Algorithm Inspired by Camel Traveling Behavior. Iraq J. Electrical and Electronic Engineering. 12. 167-177.'''
 

NiaPy/algorithms/basic/cro.py

@@ -104,6 +104,18 @@ class CoralReefsOptimization(Algorithm):
 	"""
 	Name = ['CoralReefsOptimization', 'CRO']
 
+	@staticmethod
+	def algorithmInfo():
+		r"""Get algorithms information.
+
+		Returns:
+			str: Algorithm information.
+
+		See Also:
+			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
+		"""
+		return r"""S. Salcedo-Sanz, J. Del Ser, I. Landa-Torres, S. Gil-López, and J. A. Portilla-Figueras, “The Coral Reefs Optimization Algorithm: A Novel Metaheuristic for Efficiently Solving Optimization Problems,” The Scientific World Journal, vol. 2014, Article ID 739768, 15 pages, 2014."""
+
 	@staticmethod
 	def typeParameters():
 		r"""Get dictionary with functions for checking values of parameters.

NiaPy/algorithms/basic/cs.py

@@ -45,6 +45,9 @@ def algorithmInfo():
 
 		Returns:
 			str: Algorithm information.
+
+		See Also:
+			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
 		"""
 		return r"""Yang, Xin-She, and Suash Deb. "Cuckoo search via Lévy flights." Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on. IEEE, 2009."""
 

NiaPy/algorithms/basic/cso.py

@@ -25,6 +25,18 @@ class CatSwarmOptimization(Algorithm):
     """
     Name = ['CatSwarmOptimization', 'CSO']
 
+    @staticmethod
+    def algorithmInfo():
+    	r"""Get algorithm information.
+
+    	Returns:
+    		str: Algorithm information.
+
+    	See Also:
+    		* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
+    	"""
+    	return r"""Chu, Shu-Chuan & Tsai, Pei-Wei & Pan, Jeng-Shyang. (2006). Cat Swarm Optimization. 854-858. 10.1007/11801603_94."""
+
     @staticmethod
     def typeParameters(): return {
         'NP': lambda x: isinstance(x, int) and x > 0,
@@ -41,14 +53,14 @@ def setParameters(self, NP=30, MR=0.1, C1=2.05, SMP=3, SPC=True, CDC=0.85, SRD=0
         r"""Set the algorithm parameters.
 
         Arguments:
-            NP (int): Number of individuals in population
-            MR (float): Mixture ratio
-            C1 (float): Constant in tracing mode
-            SMP (int): Seeking memory pool
-            SPC (bool): Self-position considering
-            CDC (float): Decides how many dimensions will be varied
-            SRD (float): Seeking range of the selected dimension
-            vMax (float): Maximal velocity
+            NP (int): Number of individuals in population.
+            MR (float): Mixture ratio.
+            C1 (float): Constant in tracing mode.
+            SMP (int): Seeking memory pool.
+            SPC (bool): Self-position considering.
+            CDC (float): Decides how many dimensions will be varied.
+            SRD (float): Seeking range of the selected dimension.
+            vMax (float): Maximal velocity.
 
             See Also:
                 * :func:`NiaPy.algorithms.Algorithm.setParameters`

NiaPy/algorithms/basic/es.py

@@ -63,6 +63,18 @@ class EvolutionStrategy1p1(Algorithm):
 	"""
 	Name = ['EvolutionStrategy1p1', 'EvolutionStrategy(1+1)', 'ES(1+1)']
 
+	@staticmethod
+	def algorithmInfo():
+		r"""Get algorithms information.
+
+		Returns:
+			str: Algorithm information.
+
+		See Also:
+			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
+		"""
+		return r"""TODO"""
+
 	@staticmethod
 	def typeParameters():
 		r"""Get dictionary with functions for checking values of parameters.
@@ -198,6 +210,18 @@ class EvolutionStrategyMp1(EvolutionStrategy1p1):
 	"""
 	Name = ['EvolutionStrategyMp1', 'EvolutionStrategy(mu+1)', 'ES(m+1)']
 
+	@staticmethod
+	def algorithmInfo():
+		r"""Get algorithms information.
+
+		Returns:
+			str: Algorithm information.
+
+		See Also:
+			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
+		"""
+		return r"""TODO"""
+
 	def setParameters(self, **kwargs):
 		r"""Set core parameters of EvolutionStrategy(mu+1) algorithm.
 
@@ -238,6 +262,18 @@ class EvolutionStrategyMpL(EvolutionStrategy1p1):
 	"""
 	Name = ['EvolutionStrategyMpL', 'EvolutionStrategy(mu+lambda)', 'ES(m+l)']
 
+	@staticmethod
+	def algorithmInfo():
+		r"""Get algorithms information.
+
+		Returns:
+			str: Algorithm information.
+
+		See Also:
+			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
+		"""
+		return r"""TODO"""
+
 	@staticmethod
 	def typeParameters():
 		r"""TODO.
@@ -383,6 +419,18 @@ class EvolutionStrategyML(EvolutionStrategyMpL):
 	"""
 	Name = ['EvolutionStrategyML', 'EvolutionStrategy(mu,lambda)', 'ES(m,l)']
 
+	@staticmethod
+	def algorithmInfo():
+		r"""Get algorithms information.
+
+		Returns:
+			str: Algorithm information.
+
+		See Also:
+			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
+		"""
+		return r"""TODO"""
+
 	def newPop(self, pop):
 		r"""Return new population.
 
@@ -507,6 +555,18 @@ class CovarianceMatrixAdaptionEvolutionStrategy(Algorithm):
 	Name = ['CovarianceMatrixAdaptionEvolutionStrategy', 'CMA-ES', 'CMAES']
 	epsilon = 1e-20
 
+	@staticmethod
+	def algorithmInfo():
+		r"""Get algorithms information.
+
+		Returns:
+			str: Algorithm information.
+
+		See Also:
+			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
+		"""
+		return r"""TODO"""
+
 	@staticmethod
 	def typeParameters(): return {
 			'epsilon': lambda x: isinstance(x, (float, int)) and 0 < x < 1

NiaPy/algorithms/basic/fa.py

@@ -42,12 +42,15 @@ class FireflyAlgorithm(Algorithm):
 
 	@staticmethod
 	def algorithmInfo():
-		return r"""
-        	Description: Firefly algorithm is inspired by the flashing behavior of fireflies.
-        	Author: Xin-She Yang
-        	Year: 2008
-                Main reference: Yang, Xin-She. Nature-Inspired Metaheuristic Algorithms,  Luniver Press, 2008.
-        """
+		r"""Get algorithms information.
+
+		Returns:
+			str: Algorithm information.
+
+		See Also:
+			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
+		"""
+		return r"""Fister, I., Fister Jr, I., Yang, X. S., & Brest, J. (2013). A comprehensive review of firefly algorithms. Swarm and Evolutionary Computation, 13, 34-46."""
 
 	@staticmethod
 	def typeParameters():
@@ -94,7 +97,7 @@ def alpha_new(self, a, alpha):
 			alpha (float):
 
 		Returns:
-			float: New value of parameter alpha
+			float: New value of parameter alpha.
 		"""
 		delta = 1.0 - pow(pow(10.0, -4.0) / 0.9, 1.0 / float(a))
 		return (1 - delta) * alpha

NiaPy/algorithms/basic/foa.py

@@ -50,13 +50,11 @@ def algorithmInfo():
 
 		Returns:
 			str: Algorithm information.
+
+		See Also:
+			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
 		"""
-		return r"""
-        Description: Forest Optimization Algorithm is inspired by few trees in the forests which can survive for several decades, while other trees could live for a limited period.
-        Authors: Manizheh Ghaemi, Mohammad-Reza Feizi-Derakhshi
-        Year: 2014
-        Main reference: Manizheh Ghaemi, Mohammad-Reza Feizi-Derakhshi, Forest Optimization Algorithm, Expert Systems with Applications, Volume 41, Issue 15, 2014, Pages 6676-6687, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2014.05.009.
-        """
+		return r"""Manizheh Ghaemi, Mohammad-Reza Feizi-Derakhshi, Forest Optimization Algorithm, Expert Systems with Applications, Volume 41, Issue 15, 2014, Pages 6676-6687, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2014.05.009."""
 
 	@staticmethod
 	def typeParameters():

NiaPy/algorithms/basic/fpa.py

@@ -43,6 +43,18 @@ class FlowerPollinationAlgorithm(Algorithm):
 	"""
 	Name = ['FlowerPollinationAlgorithm', 'FPA']
 
+	@staticmethod
+	def algorithmInfo():
+		r"""Get default information of algorithm.
+
+		Returns:
+			str: Basic information.
+
+		See Also:
+			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
+		"""
+		return r"""Yang, Xin-She. "Flower pollination algorithm for global optimization. International conference on unconventional computing and natural computation. Springer, Berlin, Heidelberg, 2012."""
+
 	@staticmethod
 	def typeParameters():
 		r"""TODO.
@@ -122,8 +134,8 @@ def runIteration(self, task, Sol, Sol_f, xb, fxb, S, **dparams):
 			Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, Dict[str, Any]]:
 				1. New population.
 				2. New populations fitness/function values.
-				3. New global best solution
-				4. New global best solution fitness/objective value
+				3. New global best solution.
+				4. New global best solution fitness/objective value.
 				5. Additional arguments.
 		"""
 		for i in range(self.NP):

NiaPy/algorithms/basic/fss.py

@@ -66,6 +66,18 @@ class FishSchoolSearch(Algorithm):
 	"""
 	Name = ['FSS', 'FishSchoolSearch']
 
+	@staticmethod
+	def algorithmInfo():
+		r"""Get default information of algorithm.
+
+		Returns:
+			str: Basic information.
+
+		See Also:
+			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
+		"""
+		return r"""Bastos Filho, Lima Neto, Lins, D. O. Nascimento and P. Lima, “A novel search algorithm based on fish school behavior,” in 2008 IEEE International Conference on Systems, Man and Cybernetics, Oct 2008, pp. 2646–2651."""
+
 	@staticmethod
 	def typeParameters():
 		# TODO

NiaPy/algorithms/basic/fwa.py

@@ -11,7 +11,7 @@
 __all__ = ['FireworksAlgorithm', 'EnhancedFireworksAlgorithm', 'DynamicFireworksAlgorithm', 'DynamicFireworksAlgorithmGauss', 'BareBonesFireworksAlgorithm']
 
 class BareBonesFireworksAlgorithm(Algorithm):
-	r"""Implementation of bare bone fireworks algorithm.
+	r"""Implementation of Bare Bones Fireworks Algorithm.
 
 	Algorithm:
 		Bare Bones Fireworks Algorithm
@@ -134,7 +134,7 @@ class FireworksAlgorithm(Algorithm):
 		https://www.springer.com/gp/book/9783662463529
 
 	Reference paper:
-		Tan, Ying. "Firework Algorithm: A Novel Swarm Intelligence Optimization Method." (2015).
+		Tan, Ying. "Fireworks algorithm." Heidelberg, Germany: Springer 10 (2015): 978-3
 
 	Attributes:
 		Name (List[str]): List of stirngs representing algorithm names.
@@ -151,7 +151,7 @@ def algorithmInfo():
 		See Also:
 			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
 		"""
-		return r"""Tan, Ying. "Firework Algorithm: A Novel Swarm Intelligence Optimization Method." (2015)."""
+		return r"""Tan, Ying. "Fireworks algorithm." Heidelberg, Germany: Springer 10 (2015): 978-3."""
 
 	@staticmethod
 	def typeParameters(): return {

NiaPy/algorithms/basic/ga.py

@@ -167,7 +167,7 @@ def CreepMutation(pop, ic, mr, task, rnd=rand):
 	return asarray(nx)
 
 class GeneticAlgorithm(Algorithm):
-	r"""Implementation of Genetic algorithm.
+	r"""Implementation of Genetic Algorithm.
 
 	Algorithm:
 		Genetic algorithm
@@ -201,6 +201,9 @@ def algorithmInfo():
 
 		Returns:
 			str: Basic information of algorithm.
+
+		See Also:
+			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
 		"""
 		return r"""On info"""