public static double EvaluateMove(Permutation tour, InversionMove move, Func <int, int, double> distance, DoubleArray probabilities)
{
var afterMove = (Permutation)tour.Clone();
InversionManipulator.Apply(afterMove, move.Index1, move.Index2);
return(AnalyticalProbabilisticTravelingSalesmanProblem.Evaluate(afterMove, distance, probabilities));
}
public static void Improve(Permutation assignment, DoubleMatrix distances, DoubleValue quality, IntValue localIterations, IntValue evaluatedSolutions, bool maximization, int maxIterations, DoubleArray probabilities, CancellationToken cancellation)
{
var distanceM = (DistanceMatrix)distances;
Func <int, int, double> distance = (a, b) => distanceM[a, b];
for (var i = localIterations.Value; i < maxIterations; i++)
{
InversionMove bestMove = null;
var bestQuality = quality.Value; // we have to make an improvement, so current quality is the baseline
var evaluations = 0.0;
foreach (var move in ExhaustiveInversionMoveGenerator.Generate(assignment))
{
var moveQuality = PTSPAnalyticalInversionMoveEvaluator.EvaluateMove(assignment, move, distance, probabilities);
evaluations++;
if (maximization && moveQuality > bestQuality ||
!maximization && moveQuality < bestQuality)
{
bestQuality = moveQuality;
bestMove = move;
}
}
evaluatedSolutions.Value += (int)Math.Ceiling(evaluations);
if (bestMove == null)
{
break;
}
InversionManipulator.Apply(assignment, bestMove.Index1, bestMove.Index2);
quality.Value = bestQuality;
localIterations.Value++;
cancellation.ThrowIfCancellationRequested();
}
}
public static double Apply(Permutation assignment, InversionMove move, DoubleMatrix weights, DoubleMatrix distances)
{
if (move.Index1 == move.Index2)
{
return(0);
}
double moveQuality = 0;
int min = Math.Min(move.Index1, move.Index2);
int max = Math.Max(move.Index1, move.Index2);
for (int i = min; i <= max; i++)
{
int locI = assignment[i];
int newlocI = assignment[max - i + min];
for (int j = 0; j < assignment.Length; j++)
{
int locJ = assignment[j];
if (j >= min && j <= max)
{
int newlocJ = assignment[max - j + min];
moveQuality += weights[i, j] * (distances[newlocI, newlocJ] - distances[locI, locJ]);
}
else
{
moveQuality += weights[i, j] * (distances[newlocI, locJ] - distances[locI, locJ]);
moveQuality += weights[j, i] * (distances[locJ, newlocI] - distances[locJ, locI]);
}
}
}
return(moveQuality);
}
public static double EvaluateByCoordinates(Permutation permutation, InversionMove move, DoubleMatrix coordinates, TSPInversionMovePathEvaluator evaluator)
{
int edge1source = permutation.GetCircular(move.Index1 - 1);
int edge1target = permutation[move.Index1];
int edge2source = permutation[move.Index2];
int edge2target = permutation.GetCircular(move.Index2 + 1);
if (move.Index2 - move.Index1 >= permutation.Length - 2)
{
return(0);
}
double moveQuality = 0;
// remove two edges
moveQuality -= evaluator.CalculateDistance(coordinates[edge1source, 0], coordinates[edge1source, 1],
coordinates[edge1target, 0], coordinates[edge1target, 1]);
moveQuality -= evaluator.CalculateDistance(coordinates[edge2source, 0], coordinates[edge2source, 1],
coordinates[edge2target, 0], coordinates[edge2target, 1]);
// add two edges
moveQuality += evaluator.CalculateDistance(coordinates[edge1source, 0], coordinates[edge1source, 1],
coordinates[edge2source, 0], coordinates[edge2source, 1]);
moveQuality += evaluator.CalculateDistance(coordinates[edge1target, 0], coordinates[edge1target, 1],
coordinates[edge2target, 0], coordinates[edge2target, 1]);
return(moveQuality);
}
public static void Improve(Permutation assignment, DoubleMatrix weights, DoubleMatrix distances, DoubleValue quality, IntValue localIterations, IntValue evaluatedSolutions, bool maximization, int maxIterations, CancellationToken cancellation)
{
for (int i = localIterations.Value; i < maxIterations; i++)
{
InversionMove bestMove = null;
double bestQuality = 0; // we have to make an improvement, so 0 is the baseline
double evaluations = 0.0;
foreach (var move in ExhaustiveInversionMoveGenerator.Generate(assignment))
{
double moveQuality = QAPInversionMoveEvaluator.Apply(assignment, move, weights, distances);
evaluations += 2 * (move.Index2 - move.Index1 + 1) / (double)assignment.Length;
if (maximization && moveQuality > bestQuality ||
!maximization && moveQuality < bestQuality)
{
bestQuality = moveQuality;
bestMove = move;
}
}
evaluatedSolutions.Value += (int)Math.Ceiling(evaluations);
if (bestMove == null)
{
break;
}
InversionManipulator.Apply(assignment, bestMove.Index1, bestMove.Index2);
quality.Value += bestQuality;
localIterations.Value++;
cancellation.ThrowIfCancellationRequested();
}
}
public static void Improve(Permutation assignment, DoubleMatrix distances, DoubleValue quality, IntValue localIterations, IntValue evaluatedSolutions, bool maximization, int maxIterations, ItemList <BoolArray> realizations, CancellationToken cancellation)
{
var distanceM = (DistanceMatrix)distances;
Func <int, int, double> distance = (a, b) => distanceM[a, b];
for (var i = localIterations.Value; i < maxIterations; i++)
{
InversionMove bestMove = null;
double bestQuality = 0; // we have to make an improvement, so 0 is the baseline
double evaluations = 0.0;
foreach (var move in ExhaustiveInversionMoveGenerator.Generate(assignment))
{
double moveQuality = PTSPEstimatedInversionMoveEvaluator.EvaluateMove(assignment, move, distance, realizations);
evaluations += realizations.Count * 4.0 / (assignment.Length * assignment.Length);
if (maximization && moveQuality > bestQuality ||
!maximization && moveQuality < bestQuality)
{
bestQuality = moveQuality;
bestMove = move;
}
}
evaluatedSolutions.Value += (int)Math.Ceiling(evaluations);
if (bestMove == null)
{
break;
}
InversionManipulator.Apply(assignment, bestMove.Index1, bestMove.Index2);
quality.Value += bestQuality;
localIterations.Value++;
cancellation.ThrowIfCancellationRequested();
}
}
public override IOperation Apply()
{
InversionMove move = InversionMoveParameter.ActualValue;
if (move == null)
{
throw new InvalidOperationException("Inversion 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 static double EvaluateByDistanceMatrix(Permutation permutation, InversionMove move, DistanceMatrix distanceMatrix)
{
int edge1source = permutation.GetCircular(move.Index1 - 1);
int edge1target = permutation[move.Index1];
int edge2source = permutation[move.Index2];
int edge2target = permutation.GetCircular(move.Index2 + 1);
if (move.Index2 - move.Index1 >= permutation.Length - 2)
{
return(0);
}
double moveQuality = 0;
// remove two edges
moveQuality -= distanceMatrix[edge1source, edge1target];
moveQuality -= distanceMatrix[edge2source, edge2target];
// add two edges
moveQuality += distanceMatrix[edge1source, edge2source];
moveQuality += distanceMatrix[edge1target, edge2target];
return(moveQuality);
}
public static double EvaluateMove(Permutation tour, InversionMove move, Func <int, int, double> distance, ItemList <BoolArray> realizations)
{
double moveQuality = 0;
var edges = new int[4];
var indices = new int[4];
edges[0] = tour.GetCircular(move.Index1 - 1);
indices[0] = move.Index1 - 1;
if (indices[0] == -1)
{
indices[0] = tour.Length - 1;
}
edges[1] = tour[move.Index1];
indices[1] = move.Index1;
edges[2] = tour[move.Index2];
indices[2] = move.Index2;
edges[3] = tour.GetCircular(move.Index2 + 1);
indices[3] = move.Index2 + 1;
if (indices[3] == tour.Length + 1)
{
indices[3] = 0;
}
var aPosteriori = new int[4];
foreach (var realization in realizations)
{
for (var i = 0; i < edges.Length; i++)
{
if (realization[edges[i]])
{
aPosteriori[i] = edges[i];
}
else
{
var j = 1;
if (i % 2 == 0)
{
// find nearest predecessor in realization if source edge
while (!realization[tour.GetCircular(indices[i] - j)])
{
j++;
}
aPosteriori[i] = tour.GetCircular(indices[i] - j);
}
else
{
// find nearest successor in realization if target edge
while (!realization[tour.GetCircular(indices[i] + j)])
{
j++;
}
aPosteriori[i] = tour.GetCircular(indices[i] + j);
}
}
}
// compute cost difference between the two a posteriori solutions
if (!(aPosteriori[0] == aPosteriori[2] && aPosteriori[1] == aPosteriori[3]))
{
moveQuality = moveQuality + distance(aPosteriori[0], aPosteriori[2]) + distance(aPosteriori[1], aPosteriori[3])
- distance(aPosteriori[0], aPosteriori[1]) - distance(aPosteriori[2], aPosteriori[3]);
}
Array.Clear(aPosteriori, 0, aPosteriori.Length);
}
// return average of cost differences
return(moveQuality / realizations.Count);
}
