public override IOperation Apply()
{
var solution = CurrentScope.Variables[SolutionParameter.ActualName].Value as Permutation;
if (solution == null)
{
throw new ArgumentException("Cannot improve solution because it has the wrong type.");
}
if (solution.PermutationType != PermutationTypes.RelativeUndirected)
{
throw new ArgumentException("Cannot improve solution because the permutation type is not supported.");
}
for (int i = 0; i < ImprovementAttempts.Value; i++)
{
var move = StochasticInversionSingleMoveGenerator.Apply(solution, Random);
double moveQualtiy = TSPInversionMovePathEvaluator.EvaluateByDistanceMatrix(solution, move, DistanceMatrix);
if (moveQualtiy < 0)
{
InversionManipulator.Apply(solution, move.Index1, move.Index2);
}
}
CurrentScope.Variables.Add(new Variable("LocalEvaluatedSolutions", ImprovementAttempts));
return(base.Apply());
}
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 void InversionMoveEvaluatorTest()
{
for (int i = 0; i < 500; i++)
{
int index1 = random.Next(ProblemSize);
int index2 = random.Next(ProblemSize);
if (index1 > index2)
{
int h = index1; index1 = index2; index2 = h;
}
// SYMMETRIC MATRICES
double before = QAPEvaluator.Apply(assignment, symmetricWeights, symmetricDistances);
InversionManipulator.Apply(assignment, Math.Min(index1, index2), Math.Max(index1, index2));
double after = QAPEvaluator.Apply(assignment, symmetricWeights, symmetricDistances);
double move = QAPInversionMoveEvaluator.Apply(assignment, new InversionMove(index1, index2, assignment), symmetricWeights, symmetricDistances);
Assert.IsTrue(move.IsAlmost(before - after), "Failed on symmetric matrices");
// ASYMMETRIC MATRICES
before = QAPEvaluator.Apply(assignment, asymmetricWeights, asymmetricDistances);
Permutation clone = (Permutation)assignment.Clone();
InversionManipulator.Apply(assignment, index1, index2);
after = QAPEvaluator.Apply(assignment, asymmetricWeights, asymmetricDistances);
move = QAPInversionMoveEvaluator.Apply(clone, new InversionMove(index1, index2), asymmetricWeights, asymmetricDistances);
Assert.IsTrue(move.IsAlmost(after - before), "Failed on asymmetric matrices");
// NON-ZERO DIAGONAL ASYMMETRIC MATRICES
before = QAPEvaluator.Apply(assignment, nonZeroDiagonalWeights, nonZeroDiagonalDistances);
clone = (Permutation)assignment.Clone();
InversionManipulator.Apply(assignment, index1, index2);
after = QAPEvaluator.Apply(assignment, nonZeroDiagonalWeights, nonZeroDiagonalDistances);
move = QAPInversionMoveEvaluator.Apply(clone, new InversionMove(index1, index2), nonZeroDiagonalWeights, nonZeroDiagonalDistances);
Assert.IsTrue(move.IsAlmost(after - before), "Failed on non-zero diagonal matrices");
}
}
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 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 void InversionMoveEvaluatorTest()
{
var evaluator = new TSPRoundedEuclideanPathEvaluator();
var moveEvaluator = new TSPInversionMoveRoundedEuclideanPathEvaluator();
double beforeMatrix = TSPDistanceMatrixEvaluator.Apply(distances, tour);
double beforeCoordinates = TSPCoordinatesPathEvaluator.Apply(evaluator, coordinates, tour);
Assert.IsTrue(beforeMatrix.IsAlmost(beforeCoordinates), "Evaluation differs between using the coordinates and using the distance matrix.");
for (int i = 0; i < 500; i++)
{
var move = StochasticInversionSingleMoveGenerator.Apply(tour, random);
double moveMatrix = TSPInversionMovePathEvaluator.EvaluateByDistanceMatrix(tour, move, distances);
double moveCoordinates = TSPInversionMovePathEvaluator.EvaluateByCoordinates(tour, move, coordinates, moveEvaluator);
Assert.IsTrue(moveMatrix.IsAlmost(moveCoordinates), "Evaluation differs between using the coordinates and using the distance matrix.");
string failureString = string.Format(@"Inversion move is calculated with quality {0}, but actual difference is {4}.
The move would invert the tour {1} between values {2} and {3}.", moveMatrix.ToString(), tour.ToString(), tour[move.Index1].ToString(), tour[move.Index2].ToString(), "{0}");
InversionManipulator.Apply(tour, move.Index1, move.Index2);
double afterMatrix = TSPDistanceMatrixEvaluator.Apply(distances, tour);
double afterCoordinates = TSPCoordinatesPathEvaluator.Apply(evaluator, coordinates, tour);
Assert.IsTrue(afterMatrix.IsAlmost(afterCoordinates), "Evaluation differs between using the coordinates and using the distance matrix.");
Assert.IsTrue(moveMatrix.IsAlmost(afterMatrix - beforeMatrix), string.Format(failureString, (afterMatrix - beforeMatrix).ToString()));
beforeMatrix = afterMatrix;
beforeCoordinates = afterCoordinates;
}
}
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 static void Apply(Permutation permutation, TwoPointFiveMove move)
{
if (move.IsInvert)
{
InversionManipulator.Apply(permutation, move.Index1, move.Index2);
}
else
{
TranslocationManipulator.Apply(permutation, move.Index1, move.Index1, move.Index2);
}
}
public void InversionManipulatorApplyTest()
{
TestRandom random = new TestRandom();
Permutation parent, expected;
// The following test is based on an example from Eiben, A.E. and Smith, J.E. 2003. Introduction to Evolutionary Computation. Natural Computing Series, Springer-Verlag Berlin Heidelberg, pp. 46-47
random.Reset();
random.IntNumbers = new int[] { 1, 4 };
parent = new Permutation(PermutationTypes.RelativeUndirected, new int[] { 0, 1, 2, 3, 4, 5, 6, 7, 8 });
Assert.IsTrue(parent.Validate());
expected = new Permutation(PermutationTypes.RelativeUndirected, new int[] { 0, 4, 3, 2, 1, 5, 6, 7, 8 });
Assert.IsTrue(expected.Validate());
InversionManipulator.Apply(random, parent);
Assert.IsTrue(parent.Validate());
Assert.IsTrue(Auxiliary.PermutationIsEqualByPosition(expected, parent));
}
public void InversionMoveEvaluatorTest()
{
Func <int, int, double> distance = (a, b) => distances[a, b];
double variance;
var beforeMatrix = EstimatedProbabilisticTravelingSalesmanProblem.Evaluate(tour, distance, realizations, out variance);
for (var i = 0; i < 500; i++)
{
var move = StochasticInversionSingleMoveGenerator.Apply(tour, random);
var moveMatrix = PTSPEstimatedInversionMoveEvaluator.EvaluateMove(tour, move, distance, realizations);
InversionManipulator.Apply(tour, move.Index1, move.Index2);
var afterMatrix = EstimatedProbabilisticTravelingSalesmanProblem.Evaluate(tour, distance, realizations, out variance);
Assert.IsTrue(Math.Abs(moveMatrix).IsAlmost(Math.Abs(afterMatrix - beforeMatrix)),
string.Format(@"Inversion move is calculated with quality {0}, but actual difference is {4}.
The move would invert the tour {1} between values {2} and {3}.",
moveMatrix, tour, tour[move.Index1], tour[move.Index2], Math.Abs(afterMatrix - beforeMatrix)));
beforeMatrix = afterMatrix;
}
}
