Fix GWO maximization

NiaPy/algorithms/basic/gwo.py

@@ -1,123 +1,113 @@
 # encoding=utf8
-import logging
 
-from numpy import fabs, inf
+import numpy as np
 
 from NiaPy.algorithms.algorithm import Algorithm
 
-logging.basicConfig()
-logger = logging.getLogger('NiaPy.algorithms.basic')
-logger.setLevel('INFO')
-
 __all__ = ['GreyWolfOptimizer']
 
 class GreyWolfOptimizer(Algorithm):
-	r"""Implementation of Grey wolf optimizer.
-
-	Algorithm:
-		Grey wolf optimizer
-
-	Date:
-		2018
-
-	Author:
-		Iztok Fister Jr. and Klemen Berkovič
-
-	License:
-		MIT
-
-	Reference paper:
-		* Mirjalili, Seyedali, Seyed Mohammad Mirjalili, and Andrew Lewis. "Grey wolf optimizer." Advances in engineering software 69 (2014): 46-61.
-		* Grey Wold Optimizer (GWO) source code version 1.0 (MATLAB) from MathWorks
-
-	Attributes:
-		Name (List[str]): List of strings representing algorithm names.
-
-	See Also:
-		* :class:`NiaPy.algorithms.Algorithm`
-	"""
-	Name = ['GreyWolfOptimizer', 'GWO']
-
-	@staticmethod
-	def typeParameters(): return {
-			'NP': lambda x: isinstance(x, int) and x > 0
-	}
-
-	def setParameters(self, NP=25, **ukwargs):
-		r"""Set the algorithm parameters.
-
-		Arguments:
-			NP (int): Number of individuals in population
-
-		See Also:
-			* :func:`NiaPy.algorithms.Algorithm.setParameters`
-		"""
-		Algorithm.setParameters(self, NP=NP, **ukwargs)
-
-	def initPopulation(self, task):
-		r"""Initialize population.
-
-		Args:
-			task (Task): Optimization task.
-
-		Returns:
-			Tuple[numpy.ndarray, numpy.ndarray, Dict[str, Any]]:
-				1. Initialized population.
-				2. Initialized populations fitness/function values.
-				3. Additional arguments:
-					* A (): TODO
-
-		See Also:
-			* :func:`NiaPy.algorithms.Algorithm.initPopulation`
-		"""
-		pop, fpop, d = Algorithm.initPopulation(self, task)
-		A, A_f, B, B_f, D, D_f = None, task.optType.value * inf, None, task.optType.value * inf, None, task.optType.value * inf
-		for i, f in enumerate(fpop):
-			if f < A_f: A, A_f = pop[i], f
-			elif A_f < f < B_f: B, B_f = pop[i], f
-			elif B_f < f < D_f: D, D_f = pop[i], f
-		d.update({'A': A, 'A_f': A_f, 'B': B, 'B_f': B_f, 'D': D, 'D_f': D_f})
-		return pop, fpop, d
-
-	def runIteration(self, task, pop, fpop, xb, fxb, A, A_f, B, B_f, D, D_f, **dparams):
-		r"""Core funciton of GreyWolfOptimizer algorithm.
-
-		Args:
-			task (Task): Optimization task.
-			pop (numpy.ndarray): Current population.
-			fpop (numpy.ndarray): Current populations function/fitness values.
-			xb (numpy.ndarray):
-			fxb (float):
-			A (numpy.ndarray):
-			A_f (float):
-			B (numpy.ndarray):
-			B_f (float):
-			D (numpy.ndarray):
-			D_f (float):
-			**dparams (Dict[str, Any]): Additional arguments.
-
-		Returns:
-			Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, Dict[str, Any]]:
-				1. New population
-				2. New population fitness/function values
-				3. Additional arguments:
-					* A (): TODO
-		"""
-		a = 2 - task.Evals * (2 / task.nFES)
-		for i, w in enumerate(pop):
-			A1, C1 = 2 * a * self.rand(task.D) - a, 2 * self.rand(task.D)
-			X1 = A - A1 * fabs(C1 * A - w)
-			A2, C2 = 2 * a * self.rand(task.D) - a, 2 * self.rand(task.D)
-			X2 = B - A2 * fabs(C2 * B - w)
-			A3, C3 = 2 * a * self.rand(task.D) - a, 2 * self.rand(task.D)
-			X3 = D - A3 * fabs(C3 * D - w)
-			pop[i] = task.repair((X1 + X2 + X3) / 3, self.Rand)
-			fpop[i] = task.eval(pop[i])
-		for i, f in enumerate(fpop):
-			if f < A_f: A, A_f = pop[i].copy(), f
-			elif A_f < f < B_f: B, B_f = pop[i].copy(), f
-			elif B_f < f < D_f: D, D_f = pop[i].copy(), f
-		xb, fxb = self.getBest(A, A_f, xb, fxb)
-		return pop, fpop, xb, fxb, {'A': A, 'A_f': A_f, 'B': B, 'B_f': B_f, 'D': D, 'D_f': D_f}
-
-# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
+    r"""Implementation of Grey wolf optimizer.
+
+    Algorithm:
+        Grey wolf optimizer
+
+    Date:
+        2018
+
+    Author:
+        Iztok Fister Jr. and Klemen Berkovič
+
+    License:
+        MIT
+
+    Reference paper:
+        * Mirjalili, Seyedali, Seyed Mohammad Mirjalili, and Andrew Lewis. "Grey wolf optimizer." Advances in engineering software 69 (2014): 46-61.
+        * Grey Wold Optimizer (GWO) source code version 1.0 (MATLAB) from MathWorks
+
+    Attributes:
+        Name (List[str]): List of strings representing algorithm names.
+
+    See Also:
+        * :class:`NiaPy.algorithms.Algorithm`
+    """
+    Name = ['GreyWolfOptimizer', 'GWO']
+
+    @staticmethod
+    def typeParameters(): return {
+        'NP': lambda x: isinstance(x, int) and x > 0
+    }
+
+    def setParameters(self, NP=25, **ukwargs):
+        r"""Set the algorithm parameters.
+
+        Arguments:
+            NP (int): Number of individuals in population
+
+        See Also:
+            * :func:`NiaPy.algorithms.Algorithm.setParameters`
+        """
+        Algorithm.setParameters(self, NP=NP, **ukwargs)
+
+    def initPopulation(self, task):
+        r"""Initialize population.
+
+        Args:
+            task (Task): Optimization task.
+
+        Returns:
+            Tuple[numpy.ndarray, numpy.ndarray, Dict[str, Any]]:
+                1. Initialized population.
+                2. Initialized populations fitness/function values.
+                3. Additional arguments:
+                    * A (): TODO
+
+        See Also:
+            * :func:`NiaPy.algorithms.Algorithm.initPopulation`
+        """
+        pop, fpop, d = Algorithm.initPopulation(self, task)
+        si = np.argsort(fpop)
+        A, A_f, B, B_f, D, D_f = np.copy(pop[si[0]]), fpop[si[0]], np.copy(pop[si[1]]), fpop[si[1]], np.copy(pop[si[2]]), fpop[si[2]]
+        d.update({'A': A, 'A_f': A_f, 'B': B, 'B_f': B_f, 'D': D, 'D_f': D_f})
+        return pop, fpop, d
+
+    def runIteration(self, task, pop, fpop, xb, fxb, A, A_f, B, B_f, D, D_f, **dparams):
+        r"""Core funciton of GreyWolfOptimizer algorithm.
+
+        Args:
+            task (Task): Optimization task.
+            pop (numpy.ndarray): Current population.
+            fpop (numpy.ndarray): Current populations function/fitness values.
+            xb (numpy.ndarray):
+            fxb (float):
+            A (numpy.ndarray):
+            A_f (float):
+            B (numpy.ndarray):
+            B_f (float):
+            D (numpy.ndarray):
+            D_f (float):
+            **dparams (Dict[str, Any]): Additional arguments.
+
+        Returns:
+            Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, Dict[str, Any]]:
+                1. New population
+                2. New population fitness/function values
+                3. Additional arguments:
+                    * A (): TODO
+        """
+        a = 2 - task.Evals * (2 / task.nFES)
+        for i, w in enumerate(pop):
+            A1, C1 = 2 * a * self.rand(task.D) - a, 2 * self.rand(task.D)
+            X1 = A - A1 * np.fabs(C1 * A - w)
+            A2, C2 = 2 * a * self.rand(task.D) - a, 2 * self.rand(task.D)
+            X2 = B - A2 * np.fabs(C2 * B - w)
+            A3, C3 = 2 * a * self.rand(task.D) - a, 2 * self.rand(task.D)
+            X3 = D - A3 * np.fabs(C3 * D - w)
+            pop[i] = task.repair((X1 + X2 + X3) / 3, self.Rand)
+            fpop[i] = task.eval(pop[i])
+        for i, f in enumerate(fpop):
+            if f < A_f: A, A_f = pop[i].copy(), f
+            elif A_f < f < B_f: B, B_f = pop[i].copy(), f
+            elif B_f < f < D_f: D, D_f = pop[i].copy(), f
+        xb, fxb = self.getBest(A, A_f, xb, fxb)
+        return pop, fpop, xb, fxb, {'A': A, 'A_f': A_f, 'B': B, 'B_f': B_f, 'D': D, 'D_f': D_f}