public override IOperation Apply()
{
IRandom random = RandomParameter.ActualValue;
int iteration = IterationsParameter.ActualValue.Value;
IntMatrix shortTermMemory = ShortTermMemoryParameter.ActualValue;
DoubleMatrix weights = WeightsParameter.ActualValue;
DoubleMatrix distances = DistancesParameter.ActualValue;
DoubleMatrix moveQualityMatrix = MoveQualityMatrixParameter.ActualValue;
DoubleValue quality = QualityParameter.ActualValue;
DoubleValue bestQuality = BestQualityParameter.ActualValue;
if (bestQuality == null)
{
BestQualityParameter.ActualValue = (DoubleValue)quality.Clone();
bestQuality = BestQualityParameter.ActualValue;
}
bool allMovesTabu = false;
if (AllMovesTabuParameter.ActualValue == null)
{
AllMovesTabuParameter.ActualValue = new BoolValue(false);
}
else
{
allMovesTabu = AllMovesTabuParameter.ActualValue.Value;
}
int minTenure = MinimumTabuTenureParameter.ActualValue.Value;
int maxTenure = MaximumTabuTenureParameter.ActualValue.Value;
int alternativeAspirationTenure = AlternativeAspirationTenureParameter.ActualValue.Value;
bool useAlternativeAspiration = UseAlternativeAspirationParameter.ActualValue.Value;
Permutation solution = PermutationParameter.ActualValue;
Swap2Move lastMove = LastMoveParameter.ActualValue;
double bestMoveQuality = double.MaxValue;
Swap2Move bestMove = null;
bool already_aspired = false;
double evaluations = EvaluatedSolutionEquivalentsParameter.ActualValue.Value;
foreach (Swap2Move move in ExhaustiveSwap2MoveGenerator.Generate(solution))
{
double moveQuality;
if (lastMove == null)
{
moveQuality = QAPSwap2MoveEvaluator.Apply(solution, move, weights, distances);
evaluations += 4.0 / solution.Length;
}
else if (allMovesTabu)
{
moveQuality = moveQualityMatrix[move.Index1, move.Index2];
}
else
{
moveQuality = QAPSwap2MoveEvaluator.Apply(solution, move, moveQualityMatrix[move.Index1, move.Index2], weights, distances, lastMove);
if (move.Index1 == lastMove.Index1 || move.Index2 == lastMove.Index1 || move.Index1 == lastMove.Index2 || move.Index2 == lastMove.Index2)
{
evaluations += 4.0 / solution.Length;
}
else
{
evaluations += 2.0 / (solution.Length * solution.Length);
}
}
moveQualityMatrix[move.Index1, move.Index2] = moveQuality;
moveQualityMatrix[move.Index2, move.Index1] = moveQuality;
bool autorized = shortTermMemory[move.Index1, solution[move.Index2]] < iteration ||
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;
if ((aspired && !already_aspired) || // the first alternative move is aspired
(aspired && already_aspired && moveQuality < bestMoveQuality) || // an alternative move was already aspired, but this is better
(autorized && !aspired && !already_aspired && moveQuality < bestMoveQuality)) // a regular better move is found
{
bestMove = move;
bestMoveQuality = moveQuality;
if (aspired)
{
already_aspired = true;
}
}
}
ResultCollection results = ResultsParameter.ActualValue;
if (results != null)
{
IntValue aspiredMoves = null;
if (!results.ContainsKey("AspiredMoves"))
{
aspiredMoves = new IntValue(already_aspired ? 1 : 0);
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;
aspiredMoves.Value++;
}
}
EvaluatedSolutionEquivalentsParameter.ActualValue.Value = evaluations;
EvaluatedSolutionsParameter.ActualValue.Value = (int)Math.Ceiling(evaluations);
allMovesTabu = bestMove == null;
if (!allMovesTabu)
{
LastMoveParameter.ActualValue = bestMove;
}
AllMovesTabuParameter.ActualValue.Value = allMovesTabu;
if (allMovesTabu)
{
return(base.Apply());
}
bool useNewAdaptionScheme = UseNewTabuTenureAdaptionSchemeParameter.ActualValue.Value;
if (useNewAdaptionScheme)
{
double r = random.NextDouble();
if (r == 0)
{
r = 1; // transform to (0;1]
}
shortTermMemory[bestMove.Index1, solution[bestMove.Index1]] = (int)(iteration + r * r * r * maxTenure);
r = random.NextDouble();
if (r == 0)
{
r = 1; // transform to (0;1]
}
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;
if (quality.Value < bestQuality.Value)
{
bestQuality.Value = quality.Value;
}
return(base.Apply());
}