private PathTSPTour(PathTSPTour original, Cloner cloner)
: base(original, cloner) {
this.coordinates = cloner.Clone(original.coordinates);
this.permutation = cloner.Clone(original.permutation);
this.quality = cloner.Clone(original.quality);
Initialize();
}
public void SolveTest() {
double[,] costs = new double[,] {
{ 5, 9, 3, 6 },
{ 8, 7, 8, 2 },
{ 6, 10, 12, 7 },
{ 3, 10, 8, 6 }};
double quality;
Permutation p;
p = new Permutation(PermutationTypes.Absolute, LinearAssignmentProblemSolver.Solve(new DoubleMatrix(costs), out quality));
Assert.AreEqual(18, quality);
Assert.IsTrue(p.Validate());
costs = new double[,] {
{ 11, 7, 10, 17, 10 },
{ 13, 21, 7, 11, 13 },
{ 13, 13, 15, 13, 14 },
{ 18, 10, 13, 16, 14 },
{ 12, 8, 16, 19, 10 }};
p = new Permutation(PermutationTypes.Absolute, LinearAssignmentProblemSolver.Solve(new DoubleMatrix(costs), out quality));
Assert.AreEqual(51, quality);
Assert.IsTrue(p.Validate());
costs = new double[,] {
{ 3, 1, 1, 4 },
{ 4, 2, 2, 5 },
{ 5, 3, 4, 8 },
{ 4, 2, 5, 9 }};
p = new Permutation(PermutationTypes.Absolute, LinearAssignmentProblemSolver.Solve(new DoubleMatrix(costs), out quality));
Assert.AreEqual(13, quality);
Assert.IsTrue(p.Validate());
}
public ThreeIndexMove(int index1, int index2, int index3, Permutation permutation)
: base() {
Index1 = index1;
Index2 = index2;
Index3 = index3;
Permutation = permutation;
}
private QAPAssignment(QAPAssignment original, Cloner cloner)
: base(original, cloner) {
distances = cloner.Clone(original.distances);
weights = cloner.Clone(original.weights);
assignment = cloner.Clone(original.assignment);
quality = cloner.Clone(original.quality);
}
private static double CalculateAbsolute(Permutation left, Permutation right) {
double similarity = 0.0;
for (int i = 0; i < left.Length; i++)
if (left[i] == right[i]) similarity++;
return similarity / left.Length;
}
public static void MyClassInitialize(TestContext testContext) {
random = new MersenneTwister();
symmetricDistances = new DoubleMatrix(ProblemSize, ProblemSize);
symmetricWeights = new DoubleMatrix(ProblemSize, ProblemSize);
asymmetricDistances = new DoubleMatrix(ProblemSize, ProblemSize);
asymmetricWeights = new DoubleMatrix(ProblemSize, ProblemSize);
nonZeroDiagonalDistances = new DoubleMatrix(ProblemSize, ProblemSize);
nonZeroDiagonalWeights = new DoubleMatrix(ProblemSize, ProblemSize);
for (int i = 0; i < ProblemSize - 1; i++) {
for (int j = i + 1; j < ProblemSize; j++) {
symmetricDistances[i, j] = random.Next(ProblemSize * 100);
symmetricDistances[j, i] = symmetricDistances[i, j];
symmetricWeights[i, j] = random.Next(ProblemSize * 100);
symmetricWeights[j, i] = symmetricWeights[i, j];
asymmetricDistances[i, j] = random.Next(ProblemSize * 100);
asymmetricDistances[j, i] = random.Next(ProblemSize * 100);
asymmetricWeights[i, j] = random.Next(ProblemSize * 100);
asymmetricWeights[j, i] = random.Next(ProblemSize * 100);
nonZeroDiagonalDistances[i, j] = random.Next(ProblemSize * 100);
nonZeroDiagonalDistances[j, i] = random.Next(ProblemSize * 100);
nonZeroDiagonalWeights[i, j] = random.Next(ProblemSize * 100);
nonZeroDiagonalWeights[j, i] = random.Next(ProblemSize * 100);
}
nonZeroDiagonalDistances[i, i] = random.Next(ProblemSize * 100);
nonZeroDiagonalWeights[i, i] = random.Next(ProblemSize * 100);
}
int index = random.Next(ProblemSize);
if (nonZeroDiagonalDistances[index, index] == 0)
nonZeroDiagonalDistances[index, index] = random.Next(1, ProblemSize * 100);
index = random.Next(ProblemSize);
if (nonZeroDiagonalWeights[index, index] == 0)
nonZeroDiagonalWeights[index, index] = random.Next(1, ProblemSize * 100);
assignment = new Permutation(PermutationTypes.Absolute, ProblemSize, random);
}
/// <summary>
/// Moves an randomly chosen element in the specified <paramref name="permutation"/> array
/// to another randomly generated position.
/// </summary>
/// <param name="random">The random number generator.</param>
/// <param name="permutation">The permutation to manipulate.</param>
public static void Apply(IRandom random, Permutation permutation) {
Permutation original = (Permutation)permutation.Clone();
int cutIndex, insertIndex, number;
cutIndex = random.Next(original.Length);
insertIndex = random.Next(original.Length);
number = original[cutIndex];
int i = 0; // index in new permutation
int j = 0; // index in old permutation
while (i < original.Length) {
if (j == cutIndex) {
j++;
}
if (i == insertIndex) {
permutation[i] = number;
i++;
}
if ((i < original.Length) && (j < original.Length)) {
permutation[i] = original[j];
i++;
j++;
}
}
}
/// <summary>
/// Performs a cross over permutation of <paramref name="parent1"/> and <paramref name="parent2"/>
/// based on randomly chosen positions to define which position to take from where.
/// </summary>
/// <exception cref="ArgumentException">Thrown when <paramref name="parent1"/> and <paramref name="parent2"/> are not of equal length.</exception>
/// <param name="random">The random number generator.</param>
/// <param name="parent1">First parent</param>
/// <param name="parent2">Second Parent</param>
/// <returns>Child</returns>
public static Permutation Apply(IRandom random, Permutation parent1, Permutation parent2) {
if (parent1.Length != parent2.Length) throw new ArgumentException("PositionBasedCrossover: The parent permutations are of unequal length.");
int length = parent1.Length;
int[] result = new int[length];
bool[] randomPosition = new bool[length];
bool[] numberCopied = new bool[length];
int randomPosNumber = random.Next(length);
for (int i = 0; i < randomPosNumber; i++) { // generate random bit mask
randomPosition[random.Next(length)] = true;
}
for (int i = 0; i < length; i++) { // copy numbers masked as true from second permutation
if (randomPosition[i]) {
result[i] = parent2[i];
numberCopied[parent2[i]] = true;
}
}
int index = 0;
for (int i = 0; i < length; i++) { // copy numbers masked as false from first permutation
if (!numberCopied[parent1[i]]) {
if (randomPosition[index]) {
while (randomPosition[index]) {
index++;
}
}
result[index] = parent1[i];
index++;
}
}
return new Permutation(parent1.PermutationType, result);
}
public static double EvaluateByDistanceMatrix(Permutation permutation, TranslocationMove move, DistanceMatrix distanceMatrix) {
if (move.Index1 == move.Index3
|| move.Index2 == permutation.Length - 1 && move.Index3 == 0
|| move.Index1 == 0 && move.Index3 == permutation.Length - 1 - move.Index2) return 0;
int edge1source = permutation.GetCircular(move.Index1 - 1);
int edge1target = permutation[move.Index1];
int edge2source = permutation[move.Index2];
int edge2target = permutation.GetCircular(move.Index2 + 1);
int edge3source, edge3target;
if (move.Index3 > move.Index1) {
edge3source = permutation.GetCircular(move.Index3 + move.Index2 - move.Index1);
edge3target = permutation.GetCircular(move.Index3 + move.Index2 - move.Index1 + 1);
} else {
edge3source = permutation.GetCircular(move.Index3 - 1);
edge3target = permutation[move.Index3];
}
double moveQuality = 0;
// remove three edges
moveQuality -= distanceMatrix[edge1source, edge1target];
moveQuality -= distanceMatrix[edge2source, edge2target];
moveQuality -= distanceMatrix[edge3source, edge3target];
// add three edges
moveQuality += distanceMatrix[edge3source, edge1target];
moveQuality += distanceMatrix[edge2source, edge3target];
moveQuality += distanceMatrix[edge1source, edge2target];
return moveQuality;
}
public static void Apply(Permutation permutation, int breakPoint1, int breakPoint2) {
for (int i = 0; i <= (breakPoint2 - breakPoint1) / 2; i++) { // invert permutation between breakpoints
int temp = permutation[breakPoint1 + i];
permutation[breakPoint1 + i] = permutation[breakPoint2 - i];
permutation[breakPoint2 - i] = temp;
}
}
public static ScrambleMove GenerateRandomMove(Permutation permutation, IRandom random) {
int breakPoint1, breakPoint2;
int[] scrambledIndices;
breakPoint1 = random.Next(permutation.Length);
do {
breakPoint2 = random.Next(permutation.Length);
} while (Math.Abs(breakPoint2 - breakPoint1) <= 1);
if (breakPoint2 < breakPoint1) { int h = breakPoint1; breakPoint1 = breakPoint2; breakPoint2 = h; }
scrambledIndices = new int[breakPoint2 - breakPoint1 + 1];
for (int i = 0; i < scrambledIndices.Length; i++)
scrambledIndices[i] = i;
bool[] moved = new bool[scrambledIndices.Length];
bool changed = false;
do {
for (int i = scrambledIndices.Length - 1; i > 0; i--) {
int j = random.Next(i + 1);
int t = scrambledIndices[j];
scrambledIndices[j] = scrambledIndices[i];
scrambledIndices[i] = t;
if (scrambledIndices[j] == j) moved[j] = false;
else moved[j] = true;
if (scrambledIndices[i] == i) moved[i] = false;
else moved[i] = true;
}
changed = moved.Any(x => x);
} while (!changed);
return new ScrambleMove(breakPoint1, scrambledIndices);
}
public static double EvaluateByCoordinates(Permutation permutation, TranslocationMove move, DoubleMatrix coordinates, TSPTranslocationMovePathEvaluator evaluator) {
if (move.Index1 == move.Index3
|| move.Index2 == permutation.Length - 1 && move.Index3 == 0
|| move.Index1 == 0 && move.Index3 == permutation.Length - 1 - move.Index2) return 0;
int edge1source = permutation.GetCircular(move.Index1 - 1);
int edge1target = permutation[move.Index1];
int edge2source = permutation[move.Index2];
int edge2target = permutation.GetCircular(move.Index2 + 1);
int edge3source, edge3target;
if (move.Index3 > move.Index1) {
edge3source = permutation.GetCircular(move.Index3 + move.Index2 - move.Index1);
edge3target = permutation.GetCircular(move.Index3 + move.Index2 - move.Index1 + 1);
} else {
edge3source = permutation.GetCircular(move.Index3 - 1);
edge3target = permutation[move.Index3];
}
double moveQuality = 0;
// remove three 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]);
moveQuality -= evaluator.CalculateDistance(coordinates[edge3source, 0], coordinates[edge3source, 1],
coordinates[edge3target, 0], coordinates[edge3target, 1]);
// add three edges
moveQuality += evaluator.CalculateDistance(coordinates[edge3source, 0], coordinates[edge3source, 1],
coordinates[edge1target, 0], coordinates[edge1target, 1]);
moveQuality += evaluator.CalculateDistance(coordinates[edge2source, 0], coordinates[edge2source, 1],
coordinates[edge3target, 0], coordinates[edge3target, 1]);
moveQuality += evaluator.CalculateDistance(coordinates[edge1source, 0], coordinates[edge1source, 1],
coordinates[edge2target, 0], coordinates[edge2target, 1]);
return moveQuality;
}
/// <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, ScrambleMove move, DoubleMatrix weights, DoubleMatrix distances) {
double moveQuality = 0;
int min = move.StartIndex;
int max = min + move.ScrambledIndices.Length - 1;
for (int i = min; i <= max; i++) {
int locI = assignment[i];
int newlocI = assignment[min + move.ScrambledIndices[i - min]];
if (locI == newlocI) continue;
for (int j = 0; j < assignment.Length; j++) {
int locJ = assignment[j];
if (j >= min && j <= max) {
int newlocJ = assignment[min + move.ScrambledIndices[j - min]];
moveQuality += weights[i, j] * (distances[newlocI, newlocJ] - distances[locI, locJ]);
if (locJ == newlocJ)
moveQuality += weights[j, i] * (distances[newlocJ, newlocI] - distances[locJ, locI]);
} else {
moveQuality += weights[i, j] * (distances[newlocI, locJ] - distances[locI, locJ]);
moveQuality += weights[j, i] * (distances[locJ, newlocI] - distances[locJ, locI]);
}
}
}
return moveQuality;
}
/// <summary>
/// Performs the order crossover of two permutations.
/// </summary>
/// <exception cref="ArgumentException">Thrown when <paramref name="parent1"/> and <paramref name="parent2"/> are not of equal length.</exception>
/// <exception cref="InvalidOperationException">Thrown if the numbers in the permutation elements are not in the range [0,N) with N = length of the permutation.</exception>
/// <remarks>
/// Crosses two permutations by copying a randomly chosen interval from the first permutation, preserving
/// the positions. Then, starting from the end of the copied interval, copies the missing values from the second permutation
/// in the order they occur.
/// </remarks>
/// <param name="random">A random number generator.</param>
/// <param name="parent1">The first parent permutation to cross.</param>
/// <param name="parent2">The second parent permutation to cross.</param>
/// <returns>The new permutation resulting from the crossover.</returns>
public static Permutation Apply(IRandom random, Permutation parent1, Permutation parent2) {
if (parent1.Length != parent2.Length) throw new ArgumentException("OrderCrossover: The parent permutations are of unequal length.");
int length = parent1.Length;
int[] result = new int[length];
bool[] copied = new bool[length];
int breakPoint1 = random.Next(length - 1);
int breakPoint2 = random.Next(breakPoint1 + 1, length);
try {
for (int j = breakPoint1; j <= breakPoint2; j++) { // copy part of first permutation
result[j] = parent1[j];
copied[parent1[j]] = true;
}
int index = ((breakPoint2 + 1 >= length) ? (0) : (breakPoint2 + 1));
int i = index; // for moving in parent2
while (index != breakPoint1) {
if (!copied[parent2[i]]) {
result[index] = parent2[i];
index++;
if (index >= length) index = 0;
}
i++;
if (i >= length) i = 0;
}
}
catch (IndexOutOfRangeException) {
throw new InvalidOperationException("OrderCrossover: The permutation must consist of numbers in the interval [0;N) with N = length of the permutation.");
}
return new Permutation(parent1.PermutationType, result);
}
public static double Evaluate(Permutation tour, Func<int, int, double> distance, DoubleArray probabilities) {
// Analytical evaluation
var firstSum = 0.0;
for (var i = 0; i < tour.Length - 1; i++) {
for (var j = i + 1; j < tour.Length; j++) {
var prod1 = distance(tour[i], tour[j]) * probabilities[tour[i]] * probabilities[tour[j]];
for (var k = i + 1; k < j; k++) {
prod1 = prod1 * (1 - probabilities[tour[k]]);
}
firstSum += prod1;
}
}
var secondSum = 0.0;
for (var j = 0; j < tour.Length; j++) {
for (var i = 0; i < j; i++) {
var prod2 = distance(tour[j], tour[i]) * probabilities[tour[i]] * probabilities[tour[j]];
for (var k = j + 1; k < tour.Length; k++) {
prod2 = prod2 * (1 - probabilities[tour[k]]);
}
for (var k = 0; k < i; k++) {
prod2 = prod2 * (1 - probabilities[tour[k]]);
}
secondSum += prod2;
}
}
return firstSum + secondSum;
}
public static double Apply(Permutation assignment, TranslocationMove move, DoubleMatrix weights, DoubleMatrix distances) {
double moveQuality = 0;
int min = Math.Min(move.Index1, move.Index3);
int max = Math.Max(move.Index2, move.Index3 + (move.Index2 - move.Index1));
int iOffset, changeOffset;
if (move.Index1 < move.Index3) {
iOffset = move.Index2 - move.Index1 + 1;
changeOffset = min + max - move.Index2;
} else {
iOffset = move.Index1 - move.Index3;
changeOffset = min + move.Index2 - move.Index1 + 1;
}
for (int i = min; i <= max; i++) {
if (i == changeOffset) iOffset -= (max - min + 1);
int jOffset = ((move.Index1 < move.Index3) ? (move.Index2 - move.Index1 + 1) : (move.Index1 - move.Index3));
for (int j = 0; j < assignment.Length; j++) {
moveQuality -= weights[i, j] * distances[assignment[i], assignment[j]];
if (j < min || j > max) {
moveQuality -= weights[j, i] * distances[assignment[j], assignment[i]];
moveQuality += weights[i, j] * distances[assignment[i + iOffset], assignment[j]];
moveQuality += weights[j, i] * distances[assignment[j], assignment[i + iOffset]];
} else {
if (j == changeOffset) jOffset -= (max - min + 1);
moveQuality += weights[i, j] * distances[assignment[i + iOffset], assignment[j + jOffset]];
}
}
}
return moveQuality;
}
protected TwoIndexMove(TwoIndexMove original, Cloner cloner)
: base(original, cloner) {
this.Index1 = original.Index1;
this.Index2 = original.Index2;
if (original.Permutation != null)
this.Permutation = cloner.Clone(original.Permutation);
}
/// <summary>
/// Mixes the elements of the given <paramref name="permutation"/> randomly
/// in a randomly chosen interval.
/// </summary>
/// <param name="random">The random number generator.</param>
/// <param name="permutation">The permutation to manipulate.</param>
public static void Apply(IRandom random, Permutation permutation) {
int breakPoint1, breakPoint2;
int[] scrambledIndices, remainingIndices, temp;
int selectedIndex, index;
breakPoint1 = random.Next(permutation.Length - 1);
breakPoint2 = random.Next(breakPoint1 + 1, permutation.Length);
scrambledIndices = new int[breakPoint2 - breakPoint1 + 1];
remainingIndices = new int[breakPoint2 - breakPoint1 + 1];
for (int i = 0; i < remainingIndices.Length; i++) { // initialise indices
remainingIndices[i] = i;
}
for (int i = 0; i < scrambledIndices.Length; i++) { // generate permutation of indices
selectedIndex = random.Next(remainingIndices.Length);
scrambledIndices[i] = remainingIndices[selectedIndex];
temp = remainingIndices;
remainingIndices = new int[temp.Length - 1];
index = 0;
for (int j = 0; j < remainingIndices.Length; j++) {
if (index == selectedIndex) {
index++;
}
remainingIndices[j] = temp[index];
index++;
}
}
Apply(permutation, breakPoint1, scrambledIndices);
}
public static double CalculatePhenotypeDistance(Permutation a, Permutation b, DoubleMatrix weights, DoubleMatrix distances) {
Dictionary<double, Dictionary<double, int>> alleles = new Dictionary<double, Dictionary<double, int>>();
int distance = 0, len = a.Length;
for (int x = 0; x < len; x++) {
for (int y = 0; y < len; y++) {
// there's a limited universe of double values as they're all drawn from the same matrix
double dA = distances[a[x], a[y]], dB = distances[b[x], b[y]];
if (dA == dB) continue;
Dictionary<double, int> dAlleles;
if (!alleles.ContainsKey(weights[x, y])) {
dAlleles = new Dictionary<double, int>();
alleles.Add(weights[x, y], dAlleles);
} else dAlleles = alleles[weights[x, y]];
int countA = 1, countB = -1;
if (dAlleles.ContainsKey(dA)) countA += dAlleles[dA];
if (dAlleles.ContainsKey(dB)) countB += dAlleles[dB];
if (countA <= 0) distance--; // we've found in A an allele that was present in B
else distance++; // we've found in A a new allele
dAlleles[dA] = countA;
if (countB >= 0) distance--; // we've found in B an allele that was present in A
else distance++; // we've found in B a new allele
dAlleles[dB] = countB;
}
}
return distance / (double)(2 * len * len);
}
public static TwoPointFiveMove[] Apply(Permutation permutation, IRandom random, int sampleSize) {
var moves = new TwoPointFiveMove[sampleSize];
for (var i = 0; i < sampleSize; i++) {
moves[i] = StochasticTwoPointFiveSingleMoveGenerator.Apply(permutation, random);
}
return moves;
}
public TwoPointFiveMove(int index1, int index2, Permutation permutation, bool isinvert)
: base() {
Index1 = index1;
Index2 = index2;
IsInvert = isinvert;
Permutation = permutation;
}
/// <summary>
/// Moves a randomly chosen interval of elements to another (randomly chosen) position in the given
/// <paramref name="permutation"/> array and reverses it.
/// </summary>
/// <param name="random">The random number generator.</param>
/// <param name="permutation">The permutation array to manipulate.</param>
public static void Apply(IRandom random, Permutation permutation) {
Permutation original = (Permutation)permutation.Clone();
int breakPoint1, breakPoint2, insertPoint, insertPointLimit;
breakPoint1 = random.Next(original.Length - 1);
breakPoint2 = random.Next(breakPoint1 + 1, original.Length);
insertPointLimit = original.Length - breakPoint2 + breakPoint1 - 1; // get insertion point in remaining part
if (insertPointLimit > 0)
insertPoint = random.Next(insertPointLimit);
else
insertPoint = 0;
int i = 0; // index in new permutation
int j = 0; // index in old permutation
while (i < original.Length) {
if (i == insertPoint) { // copy translocated area
for (int k = breakPoint2; k >= breakPoint1; k--) {
permutation[i] = original[k];
i++;
}
}
if (j == breakPoint1) { // skip area between breakpoints
j = breakPoint2 + 1;
}
if ((i < original.Length) && (j < original.Length)) {
permutation[i] = original[j];
i++;
j++;
}
}
}
/// <summary>
/// Performs the partially matched crossover on <paramref name="parent1"/> and <paramref name="parent2"/>.
/// </summary>
/// <exception cref="ArgumentException">Thrown when <paramref name="parent1"/> and <paramref name="parent2"/> are not of equal length or when the permutations are shorter than 4 elements.</exception>
/// <exception cref="InvalidOperationException">Thrown if the numbers in the permutation elements are not in the range [0,N) with N = length of the permutation.</exception>
/// <remarks>
/// Initially the new offspring is a clone of <paramref name="parent2"/>.
/// Then a segment is extracted from <paramref name="parent1"/> and copied to the offspring position by position.
/// Whenever a position is copied, the number at that position currently in the offspring is transfered to the position where the copied number has been.
/// E.g.: Position 15 is selected to be copied from <paramref name="parent1"/> to <paramref name="parent2"/>. At position 15 there is a '3' in <paramref name="parent1"/> and a '5' in the new offspring.
/// The '5' in the offspring is then moved to replace the '3' in the offspring and '3' is written at position 15.
/// </remarks>
/// <param name="random">A random number generator.</param>
/// <param name="parent1">The first parent permutation to cross.</param>
/// <param name="parent2">The second parent permutation to cross.</param>
/// <returns>The new permutation resulting from the crossover.</returns>
public static Permutation Apply(IRandom random, Permutation parent1, Permutation parent2) {
if (parent1.Length != parent2.Length) throw new ArgumentException("PartiallyMatchedCrossover: The parent permutations are of unequal length.");
if (parent1.Length < 4) throw new ArgumentException("PartiallyMatchedCrossover: The parent permutation must be at least of size 4.");
int length = parent1.Length;
int[] result = new int[length];
int[] invResult = new int[length];
int breakPoint1, breakPoint2;
do {
breakPoint1 = random.Next(length - 1);
breakPoint2 = random.Next(breakPoint1 + 1, length);
} while (breakPoint2 - breakPoint1 >= length - 2); // prevent the case [0,length-1) -> clone of parent1
// clone parent2 and calculate inverse permutation (number -> index)
try {
for (int j = 0; j < length; j++) {
result[j] = parent2[j];
invResult[result[j]] = j;
}
}
catch (IndexOutOfRangeException) {
throw new InvalidOperationException("PartiallyMatchedCrossover: The permutation must consist of consecutive numbers from 0 to N-1 with N = length of the permutation.");
}
for (int j = breakPoint1; j <= breakPoint2; j++) {
int orig = result[j]; // save the former value
result[j] = parent1[j]; // overwrite the former value with the new value
int index = invResult[result[j]]; // look where the new value is in the offspring
result[index] = orig; // write the former value to this position
invResult[orig] = index; // update the inverse mapping
// it's not necessary to do 'invResult[result[j]] = j' as this will not be needed again
}
return new Permutation(parent1.PermutationType, result);
}
/// <summary>
/// Performs a variant of the cyclic crossover on <paramref name="parent1"/> and <paramref name="parent2"/>.
/// </summary>
/// <exception cref="ArgumentException">Thrown when <paramref name="parent1"/> and <paramref name="parent2"/> are not of equal length.</exception>
/// <exception cref="InvalidOperationException">Thrown if the numbers in the permutation elements are not in the range [0;N) with N = length of the permutation.</exception>
/// <remarks>
/// Start at a randomly chosen position x in parent1 and transfer it to the child at the same position.
/// Now this position x is no longer available for the node on position x in parent2, so
/// the value of the node at position x in parent2 is searched in parent1 and is then transferred
/// to the child preserving the position. Now this new position y is no longer available for the node in parent2 and so on.<br/>
/// This procedure is repeated until it is again at position x, then the cycle is over.<br/>
/// All further positions are copied from the second permutation.
/// </remarks>
/// <exception cref="ArgumentException">Thrown if the two parents are not of equal length.</exception>
/// <param name="random">The random number generator.</param>
/// <param name="parent1">The parent scope 1 to cross over.</param>
/// <param name="parent2">The parent scope 2 to cross over.</param>
/// <returns>The created cross over permutation as int array.</returns>
public static Permutation Apply(IRandom random, Permutation parent1, Permutation parent2) {
if (parent1.Length != parent2.Length) throw new ArgumentException("CyclicCrossover2: The parent permutations are of unequal length.");
int length = parent1.Length;
int[] result = new int[length];
bool[] indexCopied = new bool[length];
int j, number;
j = random.Next(length); // get number to start cycle
while (!indexCopied[j]) { // copy whole cycle to new permutation
result[j] = parent1[j];
number = parent2[j];
indexCopied[j] = true;
j = 0;
while ((j < length) && (parent1[j] != number)) { // search copied number in second permutation
j++;
}
}
for (int i = 0; i < length; i++) { // copy rest of secound permutation to new permutation
if (!indexCopied[i]) {
result[i] = parent2[i];
}
}
return new Permutation(parent1.PermutationType, result);
}
private static int[] GetSubSequenceAtPosition(Permutation p1, int index, int subSequenceLength) {
var result = new int[subSequenceLength];
for (int i = 0; i < subSequenceLength; i++)
result[i] = p1[i + index];
return result;
}
public static IEnumerable<TwoPointFiveMove> Generate(Permutation permutation) {
int length = permutation.Length;
if (length == 1) throw new ArgumentException("Exhaustive 2.5-MoveGenerator: There cannot be a 2.5 move given a permutation of length 1.", "permutation");
int totalMoves = (length) * (length - 1) / 2;
if (permutation.PermutationType == PermutationTypes.RelativeUndirected) {
if (totalMoves - 3 > 0) {
for (int i = 0; i < length - 1; i++) {
for (int j = i + 1; j < length; j++) {
// doesn't make sense to inverse the whole permutation or the whole but one in case of relative undirected permutations
if (j - i >= length - 2) continue;
yield return new TwoPointFiveMove(i, j, true);
yield return new TwoPointFiveMove(i, j, false);
}
}
} else { // when length is 3 or less, there's actually no difference, but for the sake of not crashing the algorithm create a dummy move
yield return new TwoPointFiveMove(0, 1, true);
}
} else {
for (int i = 0; i < length - 1; i++)
for (int j = i + 1; j < length; j++) {
yield return new TwoPointFiveMove(i, j, true);
yield return new TwoPointFiveMove(i, j, false);
}
}
}
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);
}
}
private static void ExchangeSubsequences(Permutation p1, int index1, Permutation p2, int index2, int subSequenceLength) {
var aux = (Permutation)p1.Clone();
for (int i = 0; i < subSequenceLength; i++) {
p1[i + index1] = p2[i + index2];
p2[i + index2] = aux[i + index1];
}
}
public static double CalculateGenotypeSimilarity(Permutation a, Permutation b) {
int similar = 0;
for (int i = 0; i < a.Length; i++) {
if (a[i] == b[i]) similar++;
}
return similar / (double)a.Length;
}
/// <summary>
/// Performs a slight variation of the order crossover of two permutations.
/// </summary>
/// <exception cref="ArgumentException">Thrown when <paramref name="parent1"/> and <paramref name="parent2"/> are not of equal length.</exception>
/// <remarks>
/// Crosses two permutations by copying a randomly chosen interval from the first permutation, preserving
/// the positions. Then, from the beginning of the permutation, copies the missing values from the second permutation
/// in the order they occur.
/// </remarks>
/// <param name="random">A random number generator.</param>
/// <param name="parent1">The first parent permutation to cross.</param>
/// <param name="parent2">The second parent permutation to cross.</param>
/// <returns>The new permutation resulting from the crossover.</returns>
public static Permutation Apply(IRandom random, Permutation parent1, Permutation parent2) {
if (parent1.Length != parent2.Length) throw new ArgumentException("OrderCrossover2: The parent permutations are of unequal length.");
int[] result = new int[parent1.Length];
bool[] copied = new bool[result.Length];
int breakPoint1 = random.Next(result.Length - 1);
int breakPoint2 = random.Next(breakPoint1 + 1, result.Length);
for (int i = breakPoint1; i <= breakPoint2; i++) { // copy part of first permutation
result[i] = parent1[i];
copied[parent1[i]] = true;
}
int index = 0;
for (int i = 0; i < parent2.Length; i++) { // copy remaining part of second permutation
if (index == breakPoint1) { // skip already copied part
index = breakPoint2 + 1;
}
if (!copied[parent2[i]]) {
result[index] = parent2[i];
index++;
}
}
return new Permutation(parent1.PermutationType, result);
}
public TranslocationMove(int index1, int index2, int index3, Permutation permutation) : base(index1, index2, index3, permutation)
{
}
protected abstract Swap2Move[] GenerateMoves(Permutation permutation);
/// <summary>
/// Performs the maximal preservative crossover on <paramref name="parent1"/> and <paramref name="parent2"/>
/// by preserving a large number of edges in both parents.
/// </summary>
/// <exception cref="ArgumentException">Thrown when <paramref name="parent1"/> and <paramref name="parent2"/> are not of equal length or when the permutations are shorter than 4 elements.</exception>
/// <exception cref="InvalidOperationException">Thrown if the numbers in the permutation elements are not in the range [0;N) with N = length of the permutation.</exception>
/// <remarks>
/// First one segment is copied from the first parent to the offspring in the same position.
/// Then the tour is completed by adding the next number from the second parent if such an edge exists,
/// or from the first parent, or from the next number of the second parent.
/// The last case results in an unwanted mutation.
/// </remarks>
/// <param name="random">A random number generator.</param>
/// <param name="parent1">The first parent permutation to cross.</param>
/// <param name="parent2">The second parent permutation to cross.</param>
/// <returns>The new permutation resulting from the crossover.</returns>
public static Permutation Apply(IRandom random, Permutation parent1, Permutation parent2)
{
if (parent1.Length != parent2.Length)
{
throw new ArgumentException("MaximalPreservativeCrossover: The parent permutations are of unequal length.");
}
if (parent1.Length < 4)
{
throw new ArgumentException("MaximalPreservativeCrossover: The parent permutation must be at least of size 4.");
}
int length = parent1.Length;
int[] result = new int[length];
bool[] numberCopied = new bool[length];
int breakPoint1, breakPoint2, subsegmentLength, index;
subsegmentLength = random.Next(3, Math.Max(length / 3, 4)); // as mentioned in Pohlheim, H. Evolutionäre Algorithmen: Verfahren, Operatoren und Hinweise für die Praxis, 1999, p.44, Springer.
breakPoint1 = random.Next(length);
breakPoint2 = breakPoint1 + subsegmentLength;
if (breakPoint2 >= length)
{
breakPoint2 -= length;
}
// copy string between position [breakPoint1, breakPoint2) from parent1 to the offspring
index = breakPoint1;
do
{
result[index] = parent1[index];
numberCopied[result[index]] = true;
index++;
if (index >= length)
{
index -= length;
}
} while (index != breakPoint2);
// calculate inverse permutation (number -> index) to help finding the follower of a given number
int[] invParent1 = new int[length];
int[] invParent2 = new int[length];
try {
for (int i = 0; i < length; i++)
{
invParent1[parent1[i]] = i;
invParent2[parent2[i]] = i;
}
}
catch (IndexOutOfRangeException) {
throw new InvalidOperationException("MaximalPreservativeCrossover: The permutation must consist of numbers in the interval [0;N) with N = length of the permutation.");
}
int prevIndex = ((index > 0) ? (index - 1) : (length - 1));
do
{
// look for the follower of the last number in parent2
int p2Follower = GetFollower(parent2, invParent2[result[prevIndex]]);
if (!numberCopied[p2Follower])
{
result[index] = p2Follower;
}
else
{
// if that follower has already been added, look for the follower of the last number in parent1
int p1Follower = GetFollower(parent1, invParent1[result[prevIndex]]);
if (!numberCopied[p1Follower])
{
result[index] = p1Follower;
}
else
{
// if that has also been added, look for the next not already added number in parent2
int tempIndex = index;
for (int i = 0; i < parent2.Length; i++)
{
if (!numberCopied[parent2[tempIndex]])
{
result[index] = parent2[tempIndex];
break;
}
tempIndex++;
if (tempIndex >= parent2.Length)
{
tempIndex = 0;
}
}
}
}
numberCopied[result[index]] = true;
prevIndex = index;
index++;
if (index >= length)
{
index -= length;
}
} while (index != breakPoint1);
return(new Permutation(parent1.PermutationType, result));
}
protected override Swap2Move[] GenerateMoves(Permutation permutation)
{
return(Apply(permutation));
}
protected override Swap2Move[] GenerateMoves(Permutation permutation)
{
IRandom random = RandomParameter.ActualValue;
return(Apply(permutation, random, SampleSizeParameter.ActualValue.Value));
}
/// <summary>
/// Inverts a randomly chosen part of a permutation.
/// </summary>
/// <param name="random">A random number generator.</param>
/// <param name="permutation">The permutation to manipulate.</param>
protected override void Manipulate(IRandom random, Permutation permutation)
{
Apply(random, permutation);
}
protected abstract InversionMove[] GenerateMoves(Permutation permutation);
protected override TranslocationMove[] GenerateMoves(Permutation permutation)
{
return(Apply(permutation));
}
public static TranslocationMove[] Apply(Permutation permutation)
{
return(Generate(permutation).ToArray());
}
protected abstract TranslocationMove[] GenerateMoves(Permutation permutation);
protected override Swap2Move[] GenerateMoves(Permutation permutation)
{
IRandom random = RandomParameter.ActualValue;
return(new Swap2Move[] { Apply(permutation, random) });
}
private bool EvaluateAbsoluteTabuState(ItemList <IItem> tabuList, Swap2Move move, Permutation permutation, double moveQuality, bool maximization, bool useAspiration)
{
bool isTabu = false;
foreach (IItem tabuMove in tabuList)
{
Swap2MoveAbsoluteAttribute attrib = (tabuMove as Swap2MoveAbsoluteAttribute);
if (attrib != null &&
(!useAspiration ||
maximization && moveQuality <= attrib.MoveQuality ||
!maximization && moveQuality >= attrib.MoveQuality))
{
int i1 = move.Index1;
int n1 = permutation[move.Index1];
int i2 = move.Index2;
int n2 = permutation[move.Index2];
if ((attrib.Index1 == i1 || attrib.Index1 == i2) && (attrib.Number1 == n1 || attrib.Number1 == n2) ||
(attrib.Index2 == i2 || attrib.Index2 == i1) && (attrib.Number2 == n2 || attrib.Number2 == n1))
{
isTabu = true;
}
}
if (isTabu)
{
break;
}
}
return(isTabu);
}
public static Permutation Apply(IRandom random, Permutation parent1, Permutation parent2)
{
if (parent1.Length != parent2.Length)
{
throw new ArgumentException("parent1 and parent2 are of different length.");
}
int length = parent1.Length;
int[] child = new int[length];
bool[] numberCopied = new bool[length];
List <int> unoccupied = new List <int>();
for (int i = 0; i < length; i++)
{
bool copied = true;
if (parent1[i] == parent2[i])
{
child[i] = parent1[i];
}
else if (!numberCopied[parent1[i]] && !numberCopied[parent2[i]])
{
child[i] = (random.NextDouble() < 0.5) ? parent1[i] : parent2[i];
}
else if (!numberCopied[parent1[i]])
{
child[i] = parent1[i];
}
else if (!numberCopied[parent2[i]])
{
child[i] = parent2[i];
}
else
{
copied = false;
unoccupied.Add(i);
}
if (copied)
{
numberCopied[child[i]] = true;
}
}
if (unoccupied.Count > 0)
{
for (int i = 0; i < length; i++)
{
if (!numberCopied[i])
{
int r = random.Next(unoccupied.Count);
child[unoccupied[r]] = i;
unoccupied.RemoveAt(r);
}
if (unoccupied.Count == 0)
{
break;
}
}
}
return(new Permutation(parent1.PermutationType, child));
}
public override IOperation Apply()
{
ItemList <IItem> tabuList = TabuListParameter.ActualValue;
TranslocationMove move = TranslocationMoveParameter.ActualValue;
Permutation permutation = PermutationParameter.ActualValue;
int length = permutation.Length;
double moveQuality = MoveQualityParameter.ActualValue.Value;
bool maximization = MaximizationParameter.ActualValue.Value;
bool useAspiration = UseAspirationCriterion.Value;
bool isTabu = false;
if (permutation.PermutationType == PermutationTypes.Absolute)
{
int count = move.Index2 - move.Index1 + 1;
int[] numbers = new int[count];
for (int i = move.Index1; i <= move.Index2; i++)
{
numbers[i - move.Index1] = permutation[i];
}
foreach (IItem tabuMove in tabuList)
{
TranslocationMoveAbsoluteAttribute attribute = (tabuMove as TranslocationMoveAbsoluteAttribute);
if (attribute != null)
{
if (!useAspiration ||
maximization && moveQuality <= attribute.MoveQuality ||
!maximization && moveQuality >= attribute.MoveQuality) // if the move quality is improving beyond what was recorded when the move in the tabu list was recorded the move is regarded as okay
{
for (int i = 0; i < count; i++)
{
for (int j = 0; j < attribute.Number.Length; j++)
{
if (attribute.Number[j] == numbers[i] && attribute.OldPosition + j == move.Index3 + i)
{
isTabu = true;
break;
}
}
if (isTabu)
{
break;
}
}
}
}
if (isTabu)
{
break;
}
}
}
else
{
int E1S = permutation.GetCircular(move.Index1 - 1);
int E1T = permutation[move.Index1];
int E2S = permutation[move.Index2];
int E2T = permutation.GetCircular(move.Index2 + 1);
int E3S, E3T;
if (move.Index3 > move.Index1)
{
E3S = permutation.GetCircular(move.Index3 + move.Index2 - move.Index1);
E3T = permutation.GetCircular(move.Index3 + move.Index2 - move.Index1 + 1);
}
else
{
E3S = permutation.GetCircular(move.Index3 - 1);
E3T = permutation[move.Index3];
}
foreach (IItem tabuMove in tabuList)
{
TranslocationMoveRelativeAttribute attribute = (tabuMove as TranslocationMoveRelativeAttribute);
if (attribute != null)
{
if (!useAspiration ||
maximization && moveQuality <= attribute.MoveQuality ||
!maximization && moveQuality >= attribute.MoveQuality)
{
// if previously deleted Edge1Source-Target is readded
if (permutation.PermutationType == PermutationTypes.RelativeUndirected)
{
if (attribute.Edge1Source == E3S && attribute.Edge1Target == E1T || attribute.Edge1Source == E1T && attribute.Edge1Target == E3S ||
attribute.Edge1Source == E2S && attribute.Edge1Target == E3T || attribute.Edge1Source == E3T && attribute.Edge1Target == E2S
// if previously deleted Edge2Source-Target is readded
|| attribute.Edge2Source == E3S && attribute.Edge2Target == E1T || attribute.Edge2Source == E1T && attribute.Edge2Target == E3S ||
attribute.Edge2Source == E2S && attribute.Edge2Target == E3T || attribute.Edge2Source == E3T && attribute.Edge2Target == E2S
// if previously deleted Edge3Source-Target is readded
|| attribute.Edge3Source == E3S && attribute.Edge3Target == E1T || attribute.Edge3Source == E1T && attribute.Edge3Target == E3S ||
attribute.Edge3Source == E2S && attribute.Edge3Target == E3T || attribute.Edge3Source == E3T && attribute.Edge3Target == E2S)
{
isTabu = true;
break;
}
}
else
{
if (attribute.Edge1Source == E3S && attribute.Edge1Target == E1T ||
attribute.Edge1Source == E2S && attribute.Edge1Target == E3T
// if previously deleted Edge2Source-Target is readded
|| attribute.Edge2Source == E3S && attribute.Edge2Target == E1T ||
attribute.Edge2Source == E2S && attribute.Edge2Target == E3T
// if previously deleted Edge3Source-Target is readded
|| attribute.Edge3Source == E3S && attribute.Edge3Target == E1T ||
attribute.Edge3Source == E2S && attribute.Edge3Target == E3T)
{
isTabu = true;
break;
}
}
}
}
}
}
MoveTabuParameter.ActualValue = new BoolValue(isTabu);
return(base.Apply());
}
private static bool EvaluateRelativeTabuState(ItemList <IItem> tabuList, Swap2Move move, Permutation permutation, double moveQuality, bool maximization, bool useAspiration)
{
bool isTabu = false;
StandardEdgeEqualityComparer eq = new StandardEdgeEqualityComparer();
bool bidirectional = permutation.PermutationType == PermutationTypes.RelativeUndirected;
List <Edge> added = new List <Edge>();
added.Add(new Edge(permutation.GetCircular(move.Index1 - 1), permutation[move.Index2], bidirectional));
added.Add(new Edge(permutation[move.Index2], permutation.GetCircular(move.Index1 + 1), bidirectional));
added.Add(new Edge(permutation.GetCircular(move.Index2 - 1), permutation[move.Index1], bidirectional));
added.Add(new Edge(permutation[move.Index1], permutation.GetCircular(move.Index2 + 1), bidirectional));
foreach (IItem tabuMove in tabuList)
{
Swap2MoveRelativeAttribute relAttrib = (tabuMove as Swap2MoveRelativeAttribute);
if (relAttrib != null &&
(!useAspiration ||
maximization && moveQuality <= relAttrib.MoveQuality ||
!maximization && moveQuality >= relAttrib.MoveQuality))
{
for (int i = 0; i < relAttrib.DeletedEdges.Count; i++)
{
isTabu = eq.Equals(relAttrib.DeletedEdges[i], added[0]) ||
eq.Equals(relAttrib.DeletedEdges[i], added[1]) ||
eq.Equals(relAttrib.DeletedEdges[i], added[2]) ||
eq.Equals(relAttrib.DeletedEdges[i], added[3]);
if (isTabu)
{
break;
}
}
}
if (isTabu)
{
break;
}
}
return(isTabu);
}
public InversionMove(int index1, int index2, Permutation permutation) : base(index1, index2, permutation)
{
}
public Swap2Move(int index1, int index2, Permutation permutation) : base(index1, index2, permutation)
{
}
public static void Apply(Permutation permutation, int startIndex, int[] scrambleArray)
{
permutation.Replace(startIndex, scrambleArray.Select(x => permutation[startIndex + x]).ToArray());
}
protected Permutation(Permutation original, Cloner cloner)
: base(original, cloner)
{
this.permutationType = original.permutationType;
}
public static InversionMove[] Apply(Permutation permutation)
{
return(Generate(permutation).ToArray());
}
/// <summary>
/// Performs a cross over permutation of <paramref name="parent1"/> and <paramref name="2"/>
/// by calculating the edges of each element. Starts at a randomly chosen position,
/// the next element is a neighbour with the least
/// number of neighbours, the next again a neighbour and so on.
/// </summary>
/// <exception cref="ArgumentException">Thrown when <paramref name="parent1"/> and <paramref name="parent2"/> are not of equal length.</exception>
/// <exception cref="InvalidOperationException">Thrown when the permutation lacks a number.
/// </exception>
/// <param name="random">The random number generator.</param>
/// <param name="parent1">The parent scope 1 to cross over.</param>
/// <param name="parent2">The parent scope 2 to cross over.</param>
/// <returns>The created cross over permutation as int array.</returns>
public static Permutation Apply(IRandom random, Permutation parent1, Permutation parent2)
{
if (parent1.Length != parent2.Length)
{
throw new ArgumentException("EdgeRecombinationCrossover: The parent permutations are of unequal length.");
}
int length = parent1.Length;
int[] result = new int[length];
int[,] edgeList = new int[length, 4];
bool[] remainingNumbers = new bool[length];
int index, currentEdge, currentNumber, nextNumber, currentEdgeCount, minEdgeCount;
for (int i = 0; i < length; i++) // generate edge list for every number
{
remainingNumbers[i] = true;
index = 0;
while ((index < length) && (parent1[index] != i)) // search edges in parent1
{
index++;
}
if (index == length)
{
throw (new InvalidOperationException("Permutation doesn't contain number " + i + "."));
}
else
{
edgeList[i, 0] = parent1[(index - 1 + length) % length];
edgeList[i, 1] = parent1[(index + 1) % length];
}
index = 0;
while ((index < length) && (parent2[index] != i)) // search edges in parent2
{
index++;
}
if (index == length)
{
throw (new InvalidOperationException("Permutation doesn't contain number " + i + "."));
}
else
{
currentEdge = parent2[(index - 1 + length) % length];
if ((edgeList[i, 0] != currentEdge) && (edgeList[i, 1] != currentEdge)) // new edge found ?
{
edgeList[i, 2] = currentEdge;
}
else
{
edgeList[i, 2] = -1;
}
currentEdge = parent2[(index + 1) % length];
if ((edgeList[i, 0] != currentEdge) && (edgeList[i, 1] != currentEdge)) // new edge found ?
{
edgeList[i, 3] = currentEdge;
}
else
{
edgeList[i, 3] = -1;
}
}
}
currentNumber = random.Next(length); // get number to start
for (int i = 0; i < length; i++)
{
result[i] = currentNumber;
remainingNumbers[currentNumber] = false;
for (int j = 0; j < 4; j++) // remove all edges to / from currentNumber
{
if (edgeList[currentNumber, j] != -1)
{
for (int k = 0; k < 4; k++)
{
if (edgeList[edgeList[currentNumber, j], k] == currentNumber)
{
edgeList[edgeList[currentNumber, j], k] = -1;
}
}
}
}
minEdgeCount = 5; // every number hasn't more than 4 edges
nextNumber = -1;
for (int j = 0; j < 4; j++) // find next number with least edges
{
if (edgeList[currentNumber, j] != -1) // next number found
{
currentEdgeCount = 0;
for (int k = 0; k < 4; k++) // count edges of next number
{
if (edgeList[edgeList[currentNumber, j], k] != -1)
{
currentEdgeCount++;
}
}
if ((currentEdgeCount < minEdgeCount) ||
((currentEdgeCount == minEdgeCount) && (random.NextDouble() < 0.5)))
{
nextNumber = edgeList[currentNumber, j];
minEdgeCount = currentEdgeCount;
}
}
}
currentNumber = nextNumber;
if (currentNumber == -1) // current number has no more edge
{
index = 0;
while ((index < length) && (!remainingNumbers[index])) // choose next remaining number
{
index++;
}
if (index < length)
{
currentNumber = index;
}
}
}
return(new Permutation(parent1.PermutationType, result));
}
public override IOperation Apply()
{
ItemList <IItem> tabuList = TabuListParameter.ActualValue;
InversionMove move = InversionMoveParameter.ActualValue;
Permutation permutation = PermutationParameter.ActualValue;
int length = permutation.Length;
double moveQuality = MoveQualityParameter.ActualValue.Value;
bool maximization = MaximizationParameter.ActualValue.Value;
bool useAspiration = UseAspirationCriterion.Value;
bool isTabu = false;
foreach (IItem tabuMove in tabuList)
{
PermutationMoveAttribute attrib = (tabuMove as PermutationMoveAttribute);
if (!useAspiration ||
maximization && moveQuality <= attrib.MoveQuality ||
!maximization && moveQuality >= attrib.MoveQuality)
{
switch (permutation.PermutationType)
{
case PermutationTypes.RelativeUndirected: {
int E1S = permutation.GetCircular(move.Index1 - 1);
int E1T = permutation[move.Index1];
int E2S = permutation[move.Index2];
int E2T = permutation.GetCircular(move.Index2 + 1);
InversionMoveRelativeAttribute relAttrib = (attrib as InversionMoveRelativeAttribute);
if (relAttrib != null)
{
if (relAttrib.Edge1Source == E1S && relAttrib.Edge2Source == E1T || relAttrib.Edge1Source == E1T && relAttrib.Edge2Source == E1S ||
relAttrib.Edge1Source == E2S && relAttrib.Edge2Source == E2T || relAttrib.Edge1Source == E2T && relAttrib.Edge2Source == E2S
// if previously added Edge1Target-Edge2Target is deleted
|| relAttrib.Edge1Target == E2S && relAttrib.Edge2Target == E2T || relAttrib.Edge1Target == E2T && relAttrib.Edge2Target == E2S ||
relAttrib.Edge1Target == E1S && relAttrib.Edge2Target == E1T || relAttrib.Edge1Target == E1T && relAttrib.Edge2Target == E1S)
{
isTabu = true;
}
}
}
break;
case PermutationTypes.RelativeDirected: {
int E1S = permutation.GetCircular(move.Index1 - 1);
int E1T = permutation[move.Index1];
int E2S = permutation[move.Index2];
int E2T = permutation.GetCircular(move.Index2 + 1);
InversionMoveRelativeAttribute relAttrib = (attrib as InversionMoveRelativeAttribute);
if (relAttrib != null)
{
if (relAttrib.Edge1Source == E1S && relAttrib.Edge2Source == E1T ||
relAttrib.Edge1Source == E2S && relAttrib.Edge2Source == E2T
// if previously added Edge1Target-Edge2Target is deleted
|| relAttrib.Edge1Target == E2S && relAttrib.Edge2Target == E2T ||
relAttrib.Edge1Target == E1S && relAttrib.Edge2Target == E1T)
{
isTabu = true;
}
}
}
break;
case PermutationTypes.Absolute: {
int i1 = move.Index1;
int n1 = permutation[move.Index1];
int i2 = move.Index2;
int n2 = permutation[move.Index2];
InversionMoveAbsoluteAttribute absAttrib = (attrib as InversionMoveAbsoluteAttribute);
if (absAttrib != null)
{
if (absAttrib.Number1 == n1 || absAttrib.Number1 == n2 ||
absAttrib.Number2 == n2 || absAttrib.Number2 == n1)
{
isTabu = true;
}
}
}
break;
default: {
throw new InvalidOperationException(Name + ": Unknown permutation type.");
}
}
}
if (isTabu)
{
break;
}
}
MoveTabuParameter.ActualValue = new BoolValue(isTabu);
return(base.Apply());
}
protected abstract void Manipulate(IRandom random, Permutation permutation);
protected abstract ScrambleMove[] GenerateMoves(Permutation permutation);
public static TranslocationMove[] Apply(Permutation permutation, IRandom random, int sampleSize)
{
int length = permutation.Length;
if (length == 1)
{
throw new ArgumentException("ExhaustiveSingleInsertionMoveGenerator: There cannot be an insertion move given a permutation of length 1.", "permutation");
}
TranslocationMove[] moves = new TranslocationMove[sampleSize];
int count = 0;
HashSet <int> usedIndices = new HashSet <int>();
while (count < sampleSize)
{
int index = random.Next(length);
if (usedIndices.Count != length)
{
while (usedIndices.Contains(index))
{
index = random.Next(length);
}
}
usedIndices.Add(index);
if (permutation.PermutationType == PermutationTypes.Absolute)
{
for (int j = 1; j <= length - 1; j++)
{
moves[count++] = new TranslocationMove(index, index, (index + j) % length);
if (count == sampleSize)
{
break;
}
}
}
else
{
if (length > 2)
{
for (int j = 1; j < length - 1; j++)
{
int insertPoint = (index + j) % length;
if (index + j >= length)
{
insertPoint++;
}
moves[count++] = new TranslocationMove(index, index, insertPoint);
if (count == sampleSize)
{
break;
}
}
}
else // doesn't make sense, but just create a dummy move to not crash the algorithms
{
moves = new TranslocationMove[1];
moves[0] = new TranslocationMove(0, 0, 1);
count = sampleSize;
}
}
}
return(moves);
}