1 files changed, 128 insertions, 0 deletions
diff --git a/nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/deps/DEPSAgent.java b/nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/deps/DEPSAgent.java
new file mode 100644
index 000000000..3a08df39f
--- /dev/null
+++ b/nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/deps/DEPSAgent.java
@@ -0,0 +1,128 @@
+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.AbsGTBehavior;
+import net.adaptivebox.deps.behavior.DEGTBehavior;
+import net.adaptivebox.deps.behavior.PSGTBehavior;
+import net.adaptivebox.global.RandomGenerator;
+import net.adaptivebox.goodness.IGoodnessCompareEngine;
+import net.adaptivebox.knowledge.ILibEngine;
+import net.adaptivebox.knowledge.Library;
+import net.adaptivebox.knowledge.SearchPoint;
+import net.adaptivebox.problem.ProblemEncoder;
+import net.adaptivebox.space.BasicPoint;
+
+public class DEPSAgent {
+
+  // Describes the problem to be solved
+  private ProblemEncoder problemEncoder;
+
+  // Forms the goodness landscape
+  private IGoodnessCompareEngine qualityComparator;
+
+  // store the point that generated in current learning cycle
+  private SearchPoint trailPoint;
+
+  // temp variable
+  private AbsGTBehavior selectGTBehavior;
+
+  // the own memory: store the point that generated in old learning cycle
+  private BasicPoint pold_t;
+
+  // the own memory: store the point that generated in last learning cycle
+  private BasicPoint pcurrent_t;
+
+  // the own memory: store the personal best point
+  private SearchPoint pbest_t;
+
+  // Generate-and-test behaviors.
+  private DEGTBehavior deGTBehavior;
+  private PSGTBehavior psGTBehavior;
+
+  private double switchP = 0.5;
+
+  public DEPSAgent(ProblemEncoder encoder, DEGTBehavior deGTBehavior, PSGTBehavior psGTBehavior,
+                   double switchP, IGoodnessCompareEngine comparer, SearchPoint pbest) {
+      this.switchP = switchP;
+
+      problemEncoder = encoder;
+
+      qualityComparator = comparer;
+
+      trailPoint = problemEncoder.getFreshSearchPoint();
+      pold_t = problemEncoder.getFreshSearchPoint();
+      pcurrent_t = problemEncoder.getFreshSearchPoint();
+      pbest_t = pbest;
+
+      this.deGTBehavior = deGTBehavior;
+      this.deGTBehavior.setMemPoints(pbest_t, pcurrent_t, pold_t);
+
+      this.psGTBehavior = psGTBehavior;
+      this.psGTBehavior.setMemPoints(pbest_t, pcurrent_t, pold_t);
+  }
+
+  public void setSpecComparator(IGoodnessCompareEngine comparer) {
+    qualityComparator = comparer;
+  }
+
+  private AbsGTBehavior getGTBehavior() {
+    if (RandomGenerator.doubleZeroOneRandom() < switchP) {
+      return deGTBehavior;
+    } else {
+      return psGTBehavior;
+    }
+  }
+
+  public void setGTBehavior(AbsGTBehavior gtBehavior) {
+    gtBehavior.setMemPoints(pbest_t, pcurrent_t, pold_t);
+  }
+
+  public void generatePoint() {
+    // generates a new point in the search space (S) based on
+    // its memory and the library
+    selectGTBehavior = getGTBehavior();
+    selectGTBehavior.generateBehavior(trailPoint, problemEncoder);
+
+    // evaluate into goodness information
+    problemEncoder.evaluate(trailPoint);
+  }
+
+  public void learn() {
+    selectGTBehavior.testBehavior(trailPoint, qualityComparator);
+  }
+
+  public SearchPoint getMGState() {
+    return trailPoint;
+  }
+}