Flatten un-maintained ex. Sun/Oracle nlpsolver extension into the repo

This should make it easier to hack, and also to separate out the tangled in
third party EvolutionarySolver as/when we can.

1 files changed, 117 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 100755
index 000000000000..b4ae0017eb69
--- /dev/null
+++ b/nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/deps/behavior/PSGTBehavior.java
@@ -0,0 +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);

+   }

+

+}

+