/// <summary>
/// Calculates the quality of the move <paramref name="move"/> by evaluating the changes.
/// The runtime complexity of this method is O(N) with N being the size of the permutation.
/// </summary>
/// <param name="assignment">The current permutation.</param>
/// <param name="move">The move that is to be evaluated if it was applied to the current permutation.</param>
/// <param name="weights">The weights matrix.</param>
/// <param name="distances">The distances matrix.</param>
/// <returns>The relative change in quality if <paramref name="move"/> was applied to <paramref name="assignment"/>.</returns>
public static double Apply(Permutation assignment, Swap2Move move, DoubleMatrix weights, DoubleMatrix distances)
{
if (move.Index1 == move.Index2)
{
return(0);
}
double moveQuality = 0;
int fac1 = move.Index1, fac2 = move.Index2;
int loc1 = assignment[fac1], loc2 = assignment[fac2];
for (int j = 0; j < assignment.Length; j++)
{
if (j == fac1)
{
moveQuality += weights[fac1, fac1] * (distances[loc2, loc2] - distances[loc1, loc1]);
moveQuality += weights[fac1, fac2] * (distances[loc2, loc1] - distances[loc1, loc2]);
}
else if (j == fac2)
{
moveQuality += weights[fac2, fac2] * (distances[loc1, loc1] - distances[loc2, loc2]);
moveQuality += weights[fac2, fac1] * (distances[loc1, loc2] - distances[loc2, loc1]);
}
else
{
int locJ = assignment[j];
moveQuality += weights[fac1, j] * (distances[loc2, locJ] - distances[loc1, locJ]);
moveQuality += weights[j, fac1] * (distances[locJ, loc2] - distances[locJ, loc1]);
moveQuality += weights[fac2, j] * (distances[loc1, locJ] - distances[loc2, locJ]);
moveQuality += weights[j, fac2] * (distances[locJ, loc1] - distances[locJ, loc2]);
}
}
return(moveQuality);
}
public static void Improve(Permutation assignment, DoubleMatrix weights, DoubleMatrix distances, DoubleValue quality, IntValue localIterations, IntValue evaluatedSolutions, bool maximization, int maxIterations, CancellationToken cancellation)
{
double evalSolPerMove = 4.0 / assignment.Length;
for (int i = localIterations.Value; i < maxIterations; i++)
{
Swap2Move bestMove = null;
double bestQuality = 0; // we have to make an improvement, so 0 is the baseline
double evaluations = 0.0;
foreach (Swap2Move move in ExhaustiveSwap2MoveGenerator.Generate(assignment))
{
double moveQuality = QAPSwap2MoveEvaluator.Apply(assignment, move, weights, distances);
evaluations += evalSolPerMove;
if (maximization && moveQuality > bestQuality ||
!maximization && moveQuality < bestQuality)
{
bestQuality = moveQuality;
bestMove = move;
}
}
evaluatedSolutions.Value += (int)Math.Ceiling(evaluations);
if (bestMove == null)
{
break;
}
Swap2Manipulator.Apply(assignment, bestMove.Index1, bestMove.Index2);
quality.Value += bestQuality;
localIterations.Value++;
cancellation.ThrowIfCancellationRequested();
}
}
public static void ImproveFast(Permutation assignment, DoubleMatrix weights, DoubleMatrix distances, DoubleValue quality, IntValue localIterations, IntValue evaluatedSolutions, bool maximization, int maxIterations, CancellationToken cancellation)
{
Swap2Move bestMove = null;
double evaluations = 0.0;
var lastQuality = new double[assignment.Length, assignment.Length];
for (int i = localIterations.Value; i < maxIterations; i++)
{
double bestQuality = 0; // we have to make an improvement, so 0 is the baseline
var lastMove = bestMove;
bestMove = null;
foreach (Swap2Move move in ExhaustiveSwap2MoveGenerator.Generate(assignment))
{
double moveQuality;
if (lastMove == null)
{
moveQuality = QAPSwap2MoveEvaluator.Apply(assignment, move, weights, distances);
evaluations += 4.0 / assignment.Length;
}
else
{
moveQuality = QAPSwap2MoveEvaluator.Apply(assignment, move, lastQuality[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 / assignment.Length;
}
else
{
evaluations += 2.0 / (assignment.Length * assignment.Length);
}
}
lastQuality[move.Index1, move.Index2] = moveQuality;
if (maximization && moveQuality > bestQuality ||
!maximization && moveQuality < bestQuality)
{
bestQuality = moveQuality;
bestMove = move;
}
}
if (bestMove == null)
{
break;
}
Swap2Manipulator.Apply(assignment, bestMove.Index1, bestMove.Index2);
quality.Value += bestQuality;
localIterations.Value++;
if (cancellation.IsCancellationRequested)
{
evaluatedSolutions.Value += (int)Math.Round(evaluations);
throw new OperationCanceledException();
}
}
evaluatedSolutions.Value += (int)Math.Round(evaluations);
}
public override IOperation Apply()
{
Swap2Move move = Swap2MoveParameter.ActualValue;
if (move == null)
{
throw new InvalidOperationException("Swap-2 move is not found.");
}
Permutation assignment = PermutationParameter.ActualValue;
DoubleMatrix distances = DistancesParameter.ActualValue;
DoubleMatrix weights = WeightsParameter.ActualValue;
double moveQuality = QualityParameter.ActualValue.Value;
moveQuality += Apply(assignment, move, weights, distances);
MoveQualityParameter.ActualValue = new DoubleValue(moveQuality);
return(base.Apply());
}
public void Swap2MoveEvaluatorFastEvaluationTest()
{
for (int i = 0; i < 500; i++)
{
Swap2Move lastMove = new Swap2Move(random.Next(ProblemSize), random.Next(ProblemSize));
Permutation prevAssignment = (Permutation)assignment.Clone();
Swap2Manipulator.Apply(assignment, lastMove.Index1, lastMove.Index2);
Permutation nextAssignment = (Permutation)assignment.Clone();
Swap2Move currentMove = new Swap2Move(random.Next(ProblemSize), random.Next(ProblemSize));
Swap2Manipulator.Apply(nextAssignment, currentMove.Index1, currentMove.Index2);
double moveBefore = QAPSwap2MoveEvaluator.Apply(prevAssignment, currentMove, symmetricWeights, symmetricDistances);
double moveAfter = QAPSwap2MoveEvaluator.Apply(assignment, currentMove,
moveBefore, symmetricWeights, symmetricDistances, lastMove);
double before = QAPEvaluator.Apply(assignment, symmetricWeights, symmetricDistances);
double after = QAPEvaluator.Apply(nextAssignment, symmetricWeights, symmetricDistances);
Assert.IsTrue(moveAfter.IsAlmost(after - before), "Failed on symmetric matrices: " + Environment.NewLine
+ "Quality changed from " + before + " to " + after + " (" + (after - before).ToString() + "), but move quality change was " + moveAfter + ".");
moveBefore = QAPSwap2MoveEvaluator.Apply(prevAssignment, currentMove, asymmetricWeights, asymmetricDistances);
moveAfter = QAPSwap2MoveEvaluator.Apply(assignment, currentMove,
moveBefore, asymmetricWeights, asymmetricDistances, lastMove);
before = QAPEvaluator.Apply(assignment, asymmetricWeights, asymmetricDistances);
after = QAPEvaluator.Apply(nextAssignment, asymmetricWeights, asymmetricDistances);
Assert.IsTrue(moveAfter.IsAlmost(after - before), "Failed on asymmetric matrices: " + Environment.NewLine
+ "Quality changed from " + before + " to " + after + " (" + (after - before).ToString() + "), but move quality change was " + moveAfter + ".");
moveBefore = QAPSwap2MoveEvaluator.Apply(prevAssignment, currentMove, nonZeroDiagonalWeights, nonZeroDiagonalDistances);
moveAfter = QAPSwap2MoveEvaluator.Apply(assignment, currentMove,
moveBefore, nonZeroDiagonalWeights, nonZeroDiagonalDistances, lastMove);
before = QAPEvaluator.Apply(assignment, nonZeroDiagonalWeights, nonZeroDiagonalDistances);
after = QAPEvaluator.Apply(nextAssignment, nonZeroDiagonalWeights, nonZeroDiagonalDistances);
Assert.IsTrue(moveAfter.IsAlmost(after - before), "Failed on non-zero diagonal matrices: " + Environment.NewLine
+ "Quality changed from " + before + " to " + after + " (" + (after - before).ToString() + "), but move quality change was " + moveAfter + ".");
}
}
/// <summary>
/// Is able to compute the move qualities faster O(1) in some cases if it knows the quality of
/// performing the move <paramref name="move"/> previously. In other cases it performs a
/// standard move quality calculation with runtime complexity O(N).
/// </summary>
/// <remarks>
/// The number of cases that the calculation can be performed faster grows with N^2
/// while the number of cases which require a larger recalculation grows linearly with N.
/// Larger problem instances thus benefit from this faster method to a larger degree.
/// </remarks>
/// <param name="assignment">The current permutation.</param>
/// <param name="move">The current move that is to be evaluated.</param>
/// <param name="previousQuality">The quality of that move as evaluated for the previous permutation.</param>
/// <param name="weights">The weigths matrix.</param>
/// <param name="distances">The distances matrix.</param>
/// <param name="lastMove">The move that was applied to transform the permutation from the previous to the current one.</param>
/// <returns>The relative change in quality if <paramref name="move"/> was applied to <paramref name="assignment"/>.</returns>
public static double Apply(Permutation assignment, Swap2Move move, double previousQuality,
DoubleMatrix weights, DoubleMatrix distances, Swap2Move lastMove)
{
bool overlapsLastMove = move.Index1 == lastMove.Index1 ||
move.Index2 == lastMove.Index1 ||
move.Index1 == lastMove.Index2 ||
move.Index2 == lastMove.Index2;
if (!overlapsLastMove)
{
int r = lastMove.Index1, u = move.Index1, s = lastMove.Index2, v = move.Index2;
int pR = assignment[lastMove.Index1], pU = assignment[move.Index1], pS = assignment[lastMove.Index2], pV = assignment[move.Index2];
return(previousQuality
+ (weights[r, u] - weights[r, v] + weights[s, v] - weights[s, u])
* (distances[pS, pU] - distances[pS, pV] + distances[pR, pV] - distances[pR, pU])
+ (weights[u, r] - weights[v, r] + weights[v, s] - weights[u, s])
* (distances[pU, pS] - distances[pV, pS] + distances[pV, pR] - distances[pU, pR]));
}
else
{
return(Apply(assignment, move, weights, distances));
}
}
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());
}
