Changeset 6593 for branches/QAPAlgorithms/HeuristicLab.Problems.QuadraticAssignment.Algorithms/3.3/RobustTabooSeachOperator.cs

Timestamp:

07/25/11 18:28:10 (13 years ago)

Author:

abeham

Message:

#1541

• Changed aspiration criterion after contacting Taillard, added option to choose between old or new adaption scheme

File:

• branches/QAPAlgorithms/HeuristicLab.Problems.QuadraticAssignment.Algorithms/3.3/RobustTabooSeachOperator.cs (modified) (6 diffs)

branches/QAPAlgorithms/HeuristicLab.Problems.QuadraticAssignment.Algorithms/3.3/RobustTabooSeachOperator.cs

@@ -37 +37 @@
       get { return (ILookupParameter<IntValue>)Parameters["Iterations"]; }
     }
+    public ILookupParameter<IRandom> RandomParameter {
+      get { return (ILookupParameter<IRandom>)Parameters["Random"]; }
+    }
     public ILookupParameter<Permutation> PermutationParameter {
       get { return (ILookupParameter<Permutation>)Parameters["Permutation"]; }
@@ -61 +64 @@
       get { return (ILookupParameter<Swap2Move>)Parameters["LastMove"]; }
     }
+    public ILookupParameter<BoolValue> UseNewTabuTenureAdaptionSchemeParameter {
+      get { return (ILookupParameter<BoolValue>)Parameters["UseNewTabuTenureAdaptionScheme"]; }
+    }
     public ILookupParameter<ResultCollection> ResultsParameter {
       get { return (ILookupParameter<ResultCollection>)Parameters["Results"]; }
     }
 
-    public ILookupParameter<IRandom> RandomParameter {
-      get { return (ILookupParameter<IRandom>)Parameters["Random"]; }
-    }
     public IValueLookupParameter<IntValue> MaximumIterationsParameter {
       get { return (IValueLookupParameter<IntValue>)Parameters["MaximumIterations"]; }
@@ -101 +104 @@
       Parameters.Add(new LookupParameter<DoubleValue>("BestQuality", "The best quality value."));
       Parameters.Add(new LookupParameter<Swap2Move>("LastMove", "The last move that was applied."));
+      Parameters.Add(new LookupParameter<BoolValue>("UseNewTabuTenureAdaptionScheme", "True if the new tabu tenure adaption should be used or false otherwise."));
       Parameters.Add(new LookupParameter<ResultCollection>("Results", "Collection that houses the results of the algorithm."));
       Parameters.Add(new ValueLookupParameter<IntValue>("MaximumIterations", "The number of iterations that the algorithm should run."));
@@ -151 +155 @@
                       || shortTermMemory[move.Index2, solution[move.Index1]] < iteration;
 
-        bool aspired = shortTermMemory[move.Index1, solution[move.Index2]] < iteration - alternativeAspirationTenure
-                    || shortTermMemory[move.Index2, solution[move.Index1]] < iteration - alternativeAspirationTenure
-                    || quality.Value + moveQuality < bestQuality.Value;
+        bool aspired = (shortTermMemory[move.Index1, solution[move.Index2]] < iteration - alternativeAspirationTenure
+                     && shortTermMemory[move.Index2, solution[move.Index1]] < iteration - alternativeAspirationTenure)
+                  || quality.Value + moveQuality < bestQuality.Value;
 
         if ((aspired && !already_aspired) // the first alternative move is aspired
@@ -169 +173 @@
         if (!results.ContainsKey("AspiredMoves")) {
           aspiredMoves = new IntValue(already_aspired ? 1 : 0);
-          results.Add(new Result("AspiredMoves", aspiredMoves));
+          results.Add(new Result("AspiredMoves", "Counts the number of moves that were selected because of the aspiration criteria.", aspiredMoves));
         } else if (already_aspired) {
           aspiredMoves = (IntValue)results["AspiredMoves"].Value;
@@ -180 +184 @@
       if (bestMove == null) return base.Apply();
 
-      shortTermMemory[bestMove.Index1, solution[bestMove.Index1]] = iteration + random.Next(minTenure, maxTenure);
-      shortTermMemory[bestMove.Index2, solution[bestMove.Index2]] = iteration + random.Next(minTenure, maxTenure);
+      bool useNewAdaptionScheme = UseNewTabuTenureAdaptionSchemeParameter.ActualValue.Value;
+      if (useNewAdaptionScheme) {
+        double r = random.NextDouble();
+        shortTermMemory[bestMove.Index1, solution[bestMove.Index1]] = (int)(iteration + r * r * r * maxTenure);
+        r = random.NextDouble();
+        shortTermMemory[bestMove.Index2, solution[bestMove.Index2]] = (int)(iteration + r * r * r * maxTenure);
+      } else {
+        shortTermMemory[bestMove.Index1, solution[bestMove.Index1]] = iteration + random.Next(minTenure, maxTenure);
+        shortTermMemory[bestMove.Index2, solution[bestMove.Index2]] = iteration + random.Next(minTenure, maxTenure);
+      }
       Swap2Manipulator.Apply(solution, bestMove.Index1, bestMove.Index2);
       quality.Value += bestMoveQuality;