1 files changed, 117 insertions, 117 deletions
diff --git a/nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/deps/behavior/PSGTBehavior.java b/nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/deps/behavior/PSGTBehavior.java
index b4ae0017eb69..c1e8db0123ae 100644
--- a/nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/deps/behavior/PSGTBehavior.java
+++ b/nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/deps/behavior/PSGTBehavior.java
@@ -1,117 +1,117 @@
-/**
- * Description: The description of particle swarm (PS) Generate-and-test Behavior.
- *
-  #Supported parameters:
-  NAME    VALUE_type   Range      DefaultV        Description
-  c1       real        [0, 2]     1.494           PSAgent: learning factor for pbest
-  c2       real        [0, 2]     1.494           PSAgent: learning factor for gbest
-  w        real        [0, 1]     0.729           PSAgent: inertia weight
-  CL       real        [0, 0.1]   0               PSAgent: chaos factor
-  //Other choices for c1, c2, w, and CL: (2, 2, 0.4, 0.001)
-
- * @ Author        Create/Modi     Note
- * Xiaofeng Xie    May 11, 2004
- * Xiaofeng Xie    Jul 01, 2008
- *
- * This library is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * This library is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * Please acknowledge the author(s) if you use this code in any way.
- *
- * @version 1.0
- * @Since MAOS1.0
- *
- * @References:
- * [1] Kennedy J, Eberhart R C. Particle swarm optimization. IEEE Int. Conf. on
- * Neural Networks, Perth, Australia, 1995: 1942-1948
- * @ For original particle swarm idea
- * [2] Shi Y H, Eberhart R C. A Modified Particle Swarm Optimizer. IEEE Inter. Conf.
- * on Evolutionary Computation, Anchorage, Alaska, 1998: 69-73
- * @ For the inertia weight: adjust the trade-off between exploitation & exploration
- * [3] Clerc M, Kennedy J. The particle swarm - explosion, stability, and
- * convergence in a multidimensional complex space. IEEE Trans. on Evolutionary
- * Computation. 2002, 6 (1): 58-73
- * @ Constriction factor: ensures the convergence
- * [4] Xie X F, Zhang W J, Yang Z L. A dissipative particle swarm optimization.
- * Congress on Evolutionary Computation, Hawaii, USA, 2002: 1456-1461
- * @ The CL parameter
- * [5] Xie X F, Zhang W J, Bi D C. Optimizing semiconductor devices by self-
- * organizing particle swarm. Congress on Evolutionary Computation, Oregon, USA,
- * 2004: 2017-2022
- * @ Further experimental analysis on the convergence of PSO
- * [6] X F Xie, W J Zhang. SWAF: swarm algorithm framework for numerical
- *     optimization. Genetic and Evolutionary Computation Conference (GECCO),
- *     Seattle, WA, USA, 2004: 238-250
- *     -> a generate-and-test behavior
- *     
- */
-
-package net.adaptivebox.deps.behavior;
-
-import net.adaptivebox.goodness.*;
-import net.adaptivebox.knowledge.*;
-import net.adaptivebox.problem.*;
-import net.adaptivebox.space.*;
-
-public class PSGTBehavior extends AbsGTBehavior {
-  // Two normally choices for (c1, c2, weight), i.e., (2, 2, 0.4), or (1.494, 1.494, 0.729)
-  // The first is used in dissipative PSO (cf. [4]) as CL>0, and the second is achieved by using 
-  // constriction factors (cf. [3])
-  public double c1=2;
-  public double c2=2;
-  public double weight = 0.4; //inertia weight
-  
-  public double CL=0;  //See ref[4], normally be 0.001~0.005
-
-  //the own memory: store the point that generated in old learning cycle
-  protected BasicPoint pold_t;
-  //the own memory: store the point that generated in last learning cycle
-  protected BasicPoint pcurrent_t;
-  //the own memory: store the personal best point
-  protected SearchPoint pbest_t;
-
-  public void setMemPoints(SearchPoint pbest, BasicPoint pcurrent, BasicPoint pold) {
-    pcurrent_t = pcurrent;
-    pbest_t = pbest;
-    pold_t = pold;
-  }
-
-  public void generateBehavior(SearchPoint trailPoint, ProblemEncoder problemEncoder) {
-    SearchPoint gbest_t = socialLib.getGbest();
-    DesignSpace designSpace = problemEncoder.getDesignSpace();
-    int DIMENSION = designSpace.getDimension();
-    double deltaxb, deltaxbm;
-    for (int b=0;b<DIMENSION;b++) {
-      if (Math.random()<CL) {
-        designSpace.mutationAt(trailPoint.getLocation(), b);
-      } else {
-        deltaxb = weight*(pcurrent_t.getLocation()[b]-pold_t.getLocation()[b])
-             + c1*Math.random()*(pbest_t.getLocation()[b]-pcurrent_t.getLocation()[b])
-             + c2*Math.random()*(gbest_t.getLocation()[b]-pcurrent_t.getLocation()[b]);
-        //limitation for delta_x
-        deltaxbm = 0.5*designSpace.getMagnitudeIn(b);
-        if(deltaxb<-deltaxbm) {
-          deltaxb = -deltaxbm;
-        } else if (deltaxb>deltaxbm) {
-          deltaxb = deltaxbm;
-        }
-        trailPoint.getLocation()[b] = pcurrent_t.getLocation()[b]+deltaxb;
-      }
-    }
-  }
-
-  public void testBehavior(SearchPoint trailPoint, IGoodnessCompareEngine qualityComparator) {
-    Library.replace(qualityComparator, trailPoint, pbest_t);
-    pold_t.importLocation(pcurrent_t);
-    pcurrent_t.importLocation(trailPoint);
-   }
-
-}
-
+/**
+ * Description: The description of particle swarm (PS) Generate-and-test Behavior.
+ *
+  #Supported parameters:
+  NAME    VALUE_type   Range      DefaultV        Description
+  c1       real        [0, 2]     1.494           PSAgent: learning factor for pbest
+  c2       real        [0, 2]     1.494           PSAgent: learning factor for gbest
+  w        real        [0, 1]     0.729           PSAgent: inertia weight
+  CL       real        [0, 0.1]   0               PSAgent: chaos factor
+  //Other choices for c1, c2, w, and CL: (2, 2, 0.4, 0.001)
+
+ * @ Author        Create/Modi     Note
+ * Xiaofeng Xie    May 11, 2004
+ * Xiaofeng Xie    Jul 01, 2008
+ *
+ * This library is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU Lesser General Public
+ * License as published by the Free Software Foundation; either
+ * version 2.1 of the License, or (at your option) any later version.
+ *
+ * This library is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+ * Lesser General Public License for more details.
+ *
+ * Please acknowledge the author(s) if you use this code in any way.
+ *
+ * @version 1.0
+ * @Since MAOS1.0
+ *
+ * @References:
+ * [1] Kennedy J, Eberhart R C. Particle swarm optimization. IEEE Int. Conf. on
+ * Neural Networks, Perth, Australia, 1995: 1942-1948
+ * @ For original particle swarm idea
+ * [2] Shi Y H, Eberhart R C. A Modified Particle Swarm Optimizer. IEEE Inter. Conf.
+ * on Evolutionary Computation, Anchorage, Alaska, 1998: 69-73
+ * @ For the inertia weight: adjust the trade-off between exploitation & exploration
+ * [3] Clerc M, Kennedy J. The particle swarm - explosion, stability, and
+ * convergence in a multidimensional complex space. IEEE Trans. on Evolutionary
+ * Computation. 2002, 6 (1): 58-73
+ * @ Constriction factor: ensures the convergence
+ * [4] Xie X F, Zhang W J, Yang Z L. A dissipative particle swarm optimization.
+ * Congress on Evolutionary Computation, Hawaii, USA, 2002: 1456-1461
+ * @ The CL parameter
+ * [5] Xie X F, Zhang W J, Bi D C. Optimizing semiconductor devices by self-
+ * organizing particle swarm. Congress on Evolutionary Computation, Oregon, USA,
+ * 2004: 2017-2022
+ * @ Further experimental analysis on the convergence of PSO
+ * [6] X F Xie, W J Zhang. SWAF: swarm algorithm framework for numerical
+ *     optimization. Genetic and Evolutionary Computation Conference (GECCO),
+ *     Seattle, WA, USA, 2004: 238-250
+ *     -> a generate-and-test behavior
+ *
+ */
+
+package net.adaptivebox.deps.behavior;
+
+import net.adaptivebox.goodness.*;
+import net.adaptivebox.knowledge.*;
+import net.adaptivebox.problem.*;
+import net.adaptivebox.space.*;
+
+public class PSGTBehavior extends AbsGTBehavior {
+  // Two normally choices for (c1, c2, weight), i.e., (2, 2, 0.4), or (1.494, 1.494, 0.729)
+  // The first is used in dissipative PSO (cf. [4]) as CL>0, and the second is achieved by using
+  // constriction factors (cf. [3])
+  public double c1=2;
+  public double c2=2;
+  public double weight = 0.4; //inertia weight
+
+  public double CL=0;  //See ref[4], normally be 0.001~0.005
+
+  //the own memory: store the point that generated in old learning cycle
+  protected BasicPoint pold_t;
+  //the own memory: store the point that generated in last learning cycle
+  protected BasicPoint pcurrent_t;
+  //the own memory: store the personal best point
+  protected SearchPoint pbest_t;
+
+  public void setMemPoints(SearchPoint pbest, BasicPoint pcurrent, BasicPoint pold) {
+    pcurrent_t = pcurrent;
+    pbest_t = pbest;
+    pold_t = pold;
+  }
+
+  public void generateBehavior(SearchPoint trailPoint, ProblemEncoder problemEncoder) {
+    SearchPoint gbest_t = socialLib.getGbest();
+    DesignSpace designSpace = problemEncoder.getDesignSpace();
+    int DIMENSION = designSpace.getDimension();
+    double deltaxb, deltaxbm;
+    for (int b=0;b<DIMENSION;b++) {
+      if (Math.random()<CL) {
+        designSpace.mutationAt(trailPoint.getLocation(), b);
+      } else {
+        deltaxb = weight*(pcurrent_t.getLocation()[b]-pold_t.getLocation()[b])
+             + c1*Math.random()*(pbest_t.getLocation()[b]-pcurrent_t.getLocation()[b])
+             + c2*Math.random()*(gbest_t.getLocation()[b]-pcurrent_t.getLocation()[b]);
+        //limitation for delta_x
+        deltaxbm = 0.5*designSpace.getMagnitudeIn(b);
+        if(deltaxb<-deltaxbm) {
+          deltaxb = -deltaxbm;
+        } else if (deltaxb>deltaxbm) {
+          deltaxb = deltaxbm;
+        }
+        trailPoint.getLocation()[b] = pcurrent_t.getLocation()[b]+deltaxb;
+      }
+    }
+  }
+
+  public void testBehavior(SearchPoint trailPoint, IGoodnessCompareEngine qualityComparator) {
+    Library.replace(qualityComparator, trailPoint, pbest_t);
+    pold_t.importLocation(pcurrent_t);
+    pcurrent_t.importLocation(trailPoint);
+   }
+
+}
+