1 files changed, 132 insertions, 0 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
new file mode 100644
index 000000000..68bf5a10e
--- /dev/null
+++ b/nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/deps/behavior/PSGTBehavior.java
@@ -0,0 +1,132 @@
+/**
+ * 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.global.RandomGenerator;
+import net.adaptivebox.goodness.IGoodnessCompareEngine;
+import net.adaptivebox.knowledge.Library;
+import net.adaptivebox.knowledge.SearchPoint;
+import net.adaptivebox.problem.ProblemEncoder;
+import net.adaptivebox.space.BasicPoint;
+import net.adaptivebox.space.DesignSpace;
+
+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;
+
+  //inertia weight
+  public double weight = 0.4;
+
+  //See ref[4], normally be 0.001~0.005
+  public double CL = 0;
+
+  // 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;
+
+  @Override
+  public void setMemPoints(SearchPoint pbest, BasicPoint pcurrent, BasicPoint pold) {
+    pcurrent_t = pcurrent;
+    pbest_t = pbest;
+    pold_t = pold;
+  }
+
+  @Override
+  public void generateBehavior(SearchPoint trailPoint, ProblemEncoder problemEncoder) {
+    DesignSpace designSpace = problemEncoder.getDesignSpace();
+
+    double[] pold_t_location = pold_t.getLocation();
+    double[] pbest_t_location = pbest_t.getLocation();
+    double[] pcurrent_t_location = pcurrent_t.getLocation();
+    double[] gbest_t_location = socialLib.getGbest().getLocation();
+    double[] trailPointLocation = trailPoint.getLocation();
+
+    int DIMENSION = designSpace.getDimension();
+    for (int b = 0; b < DIMENSION; b++) {
+      if (RandomGenerator.doubleZeroOneRandom() < CL) {
+        designSpace.mutationAt(trailPointLocation, b);
+        continue;
+      }
+
+      double deltaxb = weight * (pcurrent_t_location[b] - pold_t_location[b])
+            + c1 * RandomGenerator.doubleZeroOneRandom() * (pbest_t_location[b] - pcurrent_t_location[b])
+            + c2 * RandomGenerator.doubleZeroOneRandom() * (gbest_t_location[b] - pcurrent_t_location[b]);
+
+      // limitation for delta_x
+      double deltaxbm = 0.5 * designSpace.getMagnitudeIn(b);
+
+      if (deltaxb < -deltaxbm) {
+        deltaxb = -deltaxbm;
+      } else if (deltaxb > deltaxbm) {
+        deltaxb = deltaxbm;
+      }
+
+      trailPointLocation[b] = pcurrent_t_location[b] + deltaxb;
+    }
+  }
+
+  @Override
+  public void testBehavior(SearchPoint trailPoint, IGoodnessCompareEngine qualityComparator) {
+    Library.replace(qualityComparator, trailPoint, pbest_t);
+    pold_t.importLocation(pcurrent_t);
+    pcurrent_t.importLocation(trailPoint);
+  }
+}