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diff --git a/nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/sco/SCAgent.java b/nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/sco/SCAgent.java
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+++ b/nlpsolver/ThirdParty/EvolutionarySolver/src/net/adaptivebox/sco/SCAgent.java
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+package net.adaptivebox.sco;
+
+/**
+ * Description: The description of social cognitive agent.
+ *
+ * @Information source: a) external library (L); b) the own memory: a point that
+ * generated in the last learning cycle
+ *
+ * @Coefficients: TaoB and TaoW
+ *
+ * @ Author        Create/Modi     Note
+ * Xiaofeng Xie    Mar 11, 2003
+ * Xiaofeng Xie    May 11, 2004
+ * Xiaofeng Xie    May 20, 2004
+ *
+ * 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] Xie X F, Zhang W J. Solving engineering design problems by social cognitive
+ * optimization. Genetic and Evolutionary Computation Conference, 2004: 261-262
+ */
+
+import net.adaptivebox.problem.ProblemEncoder;
+import net.adaptivebox.space.DesignSpace;
+import net.adaptivebox.space.ILocationEngine;
+import net.adaptivebox.global.RandomGenerator;
+import net.adaptivebox.goodness.IGoodnessCompareEngine;
+import net.adaptivebox.knowledge.Library;
+import net.adaptivebox.knowledge.SearchPoint;
+
+public class SCAgent {
+
+  // Describes the problem to be solved (encode the point into intermediate information)
+  private ProblemEncoder problemEncoder;
+
+  // Forms the goodness landscape
+  private IGoodnessCompareEngine specComparator;
+
+  // the coefficients of SCAgent
+  private static final int TaoB = 2;
+
+  // The early version set TaoW as the size of external library (NL), but 4 is often enough
+  private static final int TaoW = 4;
+
+  // The referred external library
+  private Library externalLib;
+
+  // store the point that generated in current learning cycle
+  private SearchPoint trailPoint;
+
+  // the own memory: store the point that generated in last learning cycle
+  private SearchPoint pcurrent_t;
+
+  public void setExternalLib(Library lib) {
+    externalLib = lib;
+  }
+
+  public void setProblemEncoder(ProblemEncoder encoder) {
+    problemEncoder = encoder;
+    trailPoint = problemEncoder.getFreshSearchPoint();
+    pcurrent_t = problemEncoder.getEncodedSearchPoint();
+  }
+
+  public void setSpecComparator(IGoodnessCompareEngine comparer) {
+    specComparator = comparer;
+  }
+
+  public SearchPoint generatePoint() {
+    // generate a new point
+    generatePoint(trailPoint);
+
+    // evaluate the generated point
+    problemEncoder.evaluate(trailPoint);
+    return trailPoint;
+  }
+
+  private void generatePoint(ILocationEngine tempPoint) {
+    SearchPoint Xmodel, Xrefer, libBPoint;
+
+    // choose Selects a better point (libBPoint) from externalLib (L) based
+    // on tournament selection
+    int xb = externalLib.tournamentSelection(specComparator, TaoB, true);
+    libBPoint = externalLib.getSelectedPoint(xb);
+
+    // Compares pcurrent_t with libBPoint
+    // The better one becomes model point (Xmodel)
+    // The worse one becomes refer point (Xrefer)
+    if (specComparator.compare(pcurrent_t.getEncodeInfo(),
+        libBPoint.getEncodeInfo()) == IGoodnessCompareEngine.LARGER_THAN) {
+      Xmodel = libBPoint;
+      Xrefer = pcurrent_t;
+    } else {
+      Xmodel = pcurrent_t;
+      Xrefer = libBPoint;
+    }
+
+    // observational learning: generates a new point near the model point, which
+    // the variation range is decided by the difference of Xmodel and Xrefer
+    inferPoint(tempPoint, Xmodel, Xrefer, problemEncoder.getDesignSpace());
+  }
+
+  // 1. Update the current point into the external library
+  // 2. Replace the current point by the generated point
+  public void updateInfo() {
+    // Selects a bad point kw from TaoW points in Library
+    int xw = externalLib.tournamentSelection(specComparator, TaoW, false);
+
+    // Replaces kw with pcurrent_t
+    externalLib.getSelectedPoint(xw).importPoint(pcurrent_t);
+
+    // Replaces pcurrent_t (x(t)) with trailPoint (x(t+1))
+    pcurrent_t.importPoint(trailPoint);
+  }
+
+  // 1---model point, 2---refer point
+  private boolean inferPoint(ILocationEngine newPoint, ILocationEngine point1, ILocationEngine point2,
+      DesignSpace space) {
+    double[] newLoc = newPoint.getLocation();
+    double[] real1 = point1.getLocation();
+    double[] real2 = point2.getLocation();
+
+    for (int i = 0; i < newLoc.length; i++) {
+      newLoc[i] = real1[i] * 2 - real2[i];
+      // boundary handling
+      newLoc[i] = space.boundAdjustAt(newLoc[i], i);
+      newLoc[i] = RandomGenerator.doubleRangeRandom(newLoc[i], real2[i]);
+    }
+    return true;
+  }
+}