1 files changed, 127 insertions, 127 deletions
diff --git a/nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/deps/DEPSAgent.java b/nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/deps/DEPSAgent.java
index 02043f5b89f2..3107fa8deff1 100644
--- a/nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/deps/DEPSAgent.java
+++ b/nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/deps/DEPSAgent.java
@@ -1,127 +1,127 @@
-package net.adaptivebox.deps;
-
-/**
- * Description: The description of agent with hybrid differential evolution and particle swarm.
- *
- * @ Author        Create/Modi     Note
- * Xiaofeng Xie    Jun 10, 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] Zhang W J, Xie X F. DEPSO: hybrid particle swarm with differential
- * evolution operator. IEEE International Conference on Systems, Man & Cybernetics,
- * Washington D C, USA, 2003: 3816-3821
- * [2] 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
- *     -> an agent perspective
- */
-
-import net.adaptivebox.deps.behavior.*;
-import net.adaptivebox.goodness.IGoodnessCompareEngine;
-import net.adaptivebox.knowledge.*;
-import net.adaptivebox.problem.*;
-import net.adaptivebox.space.*;
-
-public class DEPSAgent implements ILibEngine {
-
-  //Describes the problem to be solved
-  protected ProblemEncoder problemEncoder;
-  //Forms the goodness landscape
-  protected IGoodnessCompareEngine qualityComparator;
-
-  //store the point that generated in current learning cycle
-  protected SearchPoint trailPoint;
-
-  //temp variable
-  private AbsGTBehavior selectGTBehavior;
-
-  //The referred library
-  protected Library socialLib;
-  //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;
-
-  //Generate-and-test Behaviors
-  protected DEGTBehavior deGTBehavior;
-  protected PSGTBehavior psGTBehavior;
-  public double switchP = 0.5;
-
-  public void setLibrary(Library lib) {
-    socialLib = lib;
-    deGTBehavior.setLibrary(socialLib);
-    psGTBehavior.setLibrary(socialLib);
-  }
-
-  public void setProblemEncoder(ProblemEncoder encoder) {
-    problemEncoder = encoder;
-    trailPoint = problemEncoder.getFreshSearchPoint();
-    pold_t = problemEncoder.getFreshSearchPoint();
-    pcurrent_t = problemEncoder.getFreshSearchPoint();
-  }
-
-  public void setSpecComparator(IGoodnessCompareEngine comparer) {
-    qualityComparator = comparer;
-  }
-
-  public void setPbest(SearchPoint pbest) {
-    pbest_t = pbest;
-  }
-
-  protected AbsGTBehavior getGTBehavior() {
-    if (Math.random()<switchP) {
-      return deGTBehavior;
-    } else {
-      return psGTBehavior;
-    }
-  }
-
-  public void setGTBehavior(AbsGTBehavior gtBehavior) {
-    if (gtBehavior instanceof DEGTBehavior) {
-      deGTBehavior = ((DEGTBehavior)gtBehavior);
-      deGTBehavior.setPbest(pbest_t);
-      return;
-    }
-    if (gtBehavior instanceof PSGTBehavior) {
-      psGTBehavior = ((PSGTBehavior)gtBehavior);
-      psGTBehavior.setMemPoints(pbest_t, pcurrent_t, pold_t);
-      return;
-    }
-  }
-
-  public void generatePoint() {
-    // generates a new point in the search space (S) based on
-    // its memory and the library
-    selectGTBehavior = this.getGTBehavior();
-    selectGTBehavior.generateBehavior(trailPoint, problemEncoder);
-    //evaluate into goodness information
-    problemEncoder.evaluate(trailPoint);
-  }
-
-  public void learn() {
-    selectGTBehavior.testBehavior(trailPoint, qualityComparator);
-  }
-
-  public SearchPoint getMGState() {
-    return trailPoint;
-  }
-}
-
+package net.adaptivebox.deps;
+
+/**
+ * Description: The description of agent with hybrid differential evolution and particle swarm.
+ *
+ * @ Author        Create/Modi     Note
+ * Xiaofeng Xie    Jun 10, 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] Zhang W J, Xie X F. DEPSO: hybrid particle swarm with differential
+ * evolution operator. IEEE International Conference on Systems, Man & Cybernetics,
+ * Washington D C, USA, 2003: 3816-3821
+ * [2] 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
+ *     -> an agent perspective
+ */
+
+import net.adaptivebox.deps.behavior.*;
+import net.adaptivebox.goodness.IGoodnessCompareEngine;
+import net.adaptivebox.knowledge.*;
+import net.adaptivebox.problem.*;
+import net.adaptivebox.space.*;
+
+public class DEPSAgent implements ILibEngine {
+
+  //Describes the problem to be solved
+  protected ProblemEncoder problemEncoder;
+  //Forms the goodness landscape
+  protected IGoodnessCompareEngine qualityComparator;
+
+  //store the point that generated in current learning cycle
+  protected SearchPoint trailPoint;
+
+  //temp variable
+  private AbsGTBehavior selectGTBehavior;
+
+  //The referred library
+  protected Library socialLib;
+  //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;
+
+  //Generate-and-test Behaviors
+  protected DEGTBehavior deGTBehavior;
+  protected PSGTBehavior psGTBehavior;
+  public double switchP = 0.5;
+
+  public void setLibrary(Library lib) {
+    socialLib = lib;
+    deGTBehavior.setLibrary(socialLib);
+    psGTBehavior.setLibrary(socialLib);
+  }
+
+  public void setProblemEncoder(ProblemEncoder encoder) {
+    problemEncoder = encoder;
+    trailPoint = problemEncoder.getFreshSearchPoint();
+    pold_t = problemEncoder.getFreshSearchPoint();
+    pcurrent_t = problemEncoder.getFreshSearchPoint();
+  }
+
+  public void setSpecComparator(IGoodnessCompareEngine comparer) {
+    qualityComparator = comparer;
+  }
+
+  public void setPbest(SearchPoint pbest) {
+    pbest_t = pbest;
+  }
+
+  protected AbsGTBehavior getGTBehavior() {
+    if (Math.random()<switchP) {
+      return deGTBehavior;
+    } else {
+      return psGTBehavior;
+    }
+  }
+
+  public void setGTBehavior(AbsGTBehavior gtBehavior) {
+    if (gtBehavior instanceof DEGTBehavior) {
+      deGTBehavior = ((DEGTBehavior)gtBehavior);
+      deGTBehavior.setPbest(pbest_t);
+      return;
+    }
+    if (gtBehavior instanceof PSGTBehavior) {
+      psGTBehavior = ((PSGTBehavior)gtBehavior);
+      psGTBehavior.setMemPoints(pbest_t, pcurrent_t, pold_t);
+      return;
+    }
+  }
+
+  public void generatePoint() {
+    // generates a new point in the search space (S) based on
+    // its memory and the library
+    selectGTBehavior = this.getGTBehavior();
+    selectGTBehavior.generateBehavior(trailPoint, problemEncoder);
+    //evaluate into goodness information
+    problemEncoder.evaluate(trailPoint);
+  }
+
+  public void learn() {
+    selectGTBehavior.testBehavior(trailPoint, qualityComparator);
+  }
+
+  public SearchPoint getMGState() {
+    return trailPoint;
+  }
+}
+