From 940b4d1848e8c70ab7642901a68594e8016caffc Mon Sep 17 00:00:00 2001
From: Daniel Baumann <daniel.baumann@progress-linux.org>
Date: Sat, 27 Apr 2024 18:51:28 +0200
Subject: Adding upstream version 1:7.0.4.

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
---
 .../src/net/adaptivebox/deps/DEPSAgent.java        | 131 +++++++++++++++++++++
 1 file changed, 131 insertions(+)
 create mode 100644 nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/deps/DEPSAgent.java

(limited to 'nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/deps/DEPSAgent.java')

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..b9784c8a2
--- /dev/null
+++ b/nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/deps/DEPSAgent.java
@@ -0,0 +1,131 @@
+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.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 implements ILibEngine {
+
+  // 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;
+  public double switchP = 0.5;
+
+  public void setLibrary(Library lib) {
+    deGTBehavior.setLibrary(lib);
+    psGTBehavior.setLibrary(lib);
+  }
+
+  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;
+  }
+
+  private 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;
+  }
+}
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