public static void Improve(IRandom random, Permutation assignment, DoubleMatrix weights, DoubleMatrix distances, DoubleValue quality, IntValue localIterations, IntValue evaluatedSolutions, bool maximization, int maxIterations, int neighborhoodSize, CancellationToken cancellation)
{
for (int i = localIterations.Value; i < maxIterations; i++)
{
ScrambleMove bestMove = null;
double bestQuality = 0; // we have to make an improvement, so 0 is the baseline
double evaluations = 0.0;
for (int j = 0; j < neighborhoodSize; j++)
{
var move = StochasticScrambleMultiMoveGenerator.GenerateRandomMove(assignment, random);
double moveQuality = QAPScrambleMoveEvaluator.Apply(assignment, move, weights, distances);
evaluations += 2.0 * move.ScrambledIndices.Length / assignment.Length;
if (maximization && moveQuality > bestQuality ||
!maximization && moveQuality < bestQuality)
{
bestQuality = moveQuality;
bestMove = move;
}
}
evaluatedSolutions.Value += (int)Math.Ceiling(evaluations);
if (bestMove == null)
{
break;
}
ScrambleManipulator.Apply(assignment, bestMove.StartIndex, bestMove.ScrambledIndices);
quality.Value += bestQuality;
localIterations.Value++;
cancellation.ThrowIfCancellationRequested();
}
}
public void ScrambleMoveEvaluatorTest()
{
for (int i = 0; i < 500; i++)
{
ScrambleMove scramble = StochasticScrambleMultiMoveGenerator.GenerateRandomMove(assignment, random);
// SYMMETRIC MATRICES
double before = QAPEvaluator.Apply(assignment, symmetricWeights, symmetricDistances);
double move = QAPScrambleMoveEvaluator.Apply(assignment, scramble, symmetricWeights, symmetricDistances);
ScrambleManipulator.Apply(assignment, scramble.StartIndex, scramble.ScrambledIndices);
double after = QAPEvaluator.Apply(assignment, symmetricWeights, symmetricDistances);
Assert.IsTrue(move.IsAlmost(after - before), "Failed on symmetric matrices");
// ASYMMETRIC MATRICES
before = QAPEvaluator.Apply(assignment, asymmetricWeights, asymmetricDistances);
move = QAPScrambleMoveEvaluator.Apply(assignment, scramble, asymmetricWeights, asymmetricDistances);
ScrambleManipulator.Apply(assignment, scramble.StartIndex, scramble.ScrambledIndices);
after = QAPEvaluator.Apply(assignment, asymmetricWeights, asymmetricDistances);
Assert.IsTrue(move.IsAlmost(after - before), "Failed on asymmetric matrices");
// NON-ZERO DIAGONAL ASYMMETRIC MATRICES
before = QAPEvaluator.Apply(assignment, nonZeroDiagonalWeights, nonZeroDiagonalDistances);
move = QAPScrambleMoveEvaluator.Apply(assignment, scramble, nonZeroDiagonalWeights, nonZeroDiagonalDistances);
ScrambleManipulator.Apply(assignment, scramble.StartIndex, scramble.ScrambledIndices);
after = QAPEvaluator.Apply(assignment, nonZeroDiagonalWeights, nonZeroDiagonalDistances);
Assert.IsTrue(move.IsAlmost(after - before), "Failed on non-zero diagonal matrices");
}
}
public void ScrambleManipulatorApplyTest()
{
TestRandom random = new TestRandom();
Permutation parent, expected;
// The following test is based on an example from Larranaga, P. et al. 1999. Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators. Artificial Intelligence Review, 13
random.Reset();
random.IntNumbers = new int[] { 3, 6, 1, 1, 1, 0 };
parent = new Permutation(PermutationTypes.RelativeUndirected, new int[] { 0, 1, 2, 3, 4, 5, 6, 7 });
Assert.IsTrue(parent.Validate());
expected = new Permutation(PermutationTypes.RelativeUndirected, new int[] { 0, 1, 2, 4, 5, 6, 3, 7 });
Assert.IsTrue(expected.Validate());
ScrambleManipulator.Apply(random, parent);
Assert.IsTrue(parent.Validate());
Assert.IsTrue(Auxiliary.PermutationIsEqualByPosition(expected, parent));
}