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 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);
}
public override IOperation Apply()
{
DoubleMatrix distances = DistancesParameter.ActualValue;
DoubleMatrix weights = WeightsParameter.ActualValue;
ItemArray <Permutation> permutations = PermutationParameter.ActualValue;
ItemArray <DoubleValue> qualities = QualityParameter.ActualValue;
ResultCollection results = ResultsParameter.ActualValue;
bool max = MaximizationParameter.ActualValue.Value;
DoubleValue bestKnownQuality = BestKnownQualityParameter.ActualValue;
var sorted = qualities.Select((x, index) => new { index, x.Value }).OrderBy(x => x.Value).ToArray();
if (max)
{
sorted = sorted.Reverse().ToArray();
}
int i = sorted.First().index;
if (bestKnownQuality == null ||
max && qualities[i].Value > bestKnownQuality.Value ||
!max && qualities[i].Value < bestKnownQuality.Value)
{
// if there isn't a best-known quality or we improved the best-known quality we'll add the current solution as best-known
BestKnownQualityParameter.ActualValue = new DoubleValue(qualities[i].Value);
BestKnownSolutionParameter.ActualValue = (Permutation)permutations[i].Clone();
BestKnownSolutionsParameter.ActualValue = new ItemSet <Permutation>(new PermutationEqualityComparer());
BestKnownSolutionsParameter.ActualValue.Add((Permutation)permutations[i].Clone());
}
else if (bestKnownQuality.Value == qualities[i].Value)
{
// if we matched the best-known quality we'll try to set the best-known solution if it isn't null
// and try to add it to the pool of best solutions if it is different
if (BestKnownSolutionParameter.ActualValue == null)
{
BestKnownSolutionParameter.ActualValue = (Permutation)permutations[i].Clone();
}
if (BestKnownSolutionsParameter.ActualValue == null)
{
BestKnownSolutionsParameter.ActualValue = new ItemSet <Permutation>(new PermutationEqualityComparer());
}
foreach (var k in sorted) // for each solution that we found check if it is in the pool of best-knowns
{
if (!max && k.Value > qualities[i].Value ||
max && k.Value < qualities[i].Value)
{
break; // stop when we reached a solution worse than the best-known quality
}
Permutation p = permutations[k.index];
if (!BestKnownSolutionsParameter.ActualValue.Contains(p))
{
BestKnownSolutionsParameter.ActualValue.Add((Permutation)permutations[k.index].Clone());
}
}
}
QAPAssignment assignment = BestSolutionParameter.ActualValue;
if (assignment == null)
{
assignment = new QAPAssignment(weights, (Permutation)permutations[i].Clone(), new DoubleValue(qualities[i].Value));
assignment.Distances = distances;
BestSolutionParameter.ActualValue = assignment;
results.Add(new Result("Best QAP Solution", assignment));
}
else
{
if (max && assignment.Quality.Value < qualities[i].Value ||
!max && assignment.Quality.Value > qualities[i].Value)
{
assignment.Distances = distances;
assignment.Weights = weights;
assignment.Assignment = (Permutation)permutations[i].Clone();
assignment.Quality.Value = qualities[i].Value;
}
}
return(base.Apply());
}
public override IOperation Apply() {
DoubleMatrix distances = DistancesParameter.ActualValue;
DoubleMatrix weights = WeightsParameter.ActualValue;
ItemArray<Permutation> permutations = PermutationParameter.ActualValue;
ItemArray<DoubleValue> qualities = QualityParameter.ActualValue;
ResultCollection results = ResultsParameter.ActualValue;
bool max = MaximizationParameter.ActualValue.Value;
DoubleValue bestKnownQuality = BestKnownQualityParameter.ActualValue;
var sorted = qualities.Select((x, index) => new { index, x.Value }).OrderBy(x => x.Value).ToArray();
if (max) sorted = sorted.Reverse().ToArray();
int i = sorted.First().index;
if (bestKnownQuality == null
|| max && qualities[i].Value > bestKnownQuality.Value
|| !max && qualities[i].Value < bestKnownQuality.Value) {
// if there isn't a best-known quality or we improved the best-known quality we'll add the current solution as best-known
BestKnownQualityParameter.ActualValue = new DoubleValue(qualities[i].Value);
BestKnownSolutionParameter.ActualValue = (Permutation)permutations[i].Clone();
BestKnownSolutionsParameter.ActualValue = new ItemSet<Permutation>(new PermutationEqualityComparer());
BestKnownSolutionsParameter.ActualValue.Add((Permutation)permutations[i].Clone());
} else if (bestKnownQuality.Value == qualities[i].Value) {
// if we matched the best-known quality we'll try to set the best-known solution if it isn't null
// and try to add it to the pool of best solutions if it is different
if (BestKnownSolutionParameter.ActualValue == null)
BestKnownSolutionParameter.ActualValue = (Permutation)permutations[i].Clone();
if (BestKnownSolutionsParameter.ActualValue == null)
BestKnownSolutionsParameter.ActualValue = new ItemSet<Permutation>(new PermutationEqualityComparer());
foreach (var k in sorted) { // for each solution that we found check if it is in the pool of best-knowns
if (!max && k.Value > qualities[i].Value
|| max && k.Value < qualities[i].Value) break; // stop when we reached a solution worse than the best-known quality
Permutation p = permutations[k.index];
if (!BestKnownSolutionsParameter.ActualValue.Contains(p))
BestKnownSolutionsParameter.ActualValue.Add((Permutation)permutations[k.index].Clone());
}
}
QAPAssignment assignment = BestSolutionParameter.ActualValue;
if (assignment == null) {
assignment = new QAPAssignment(weights, (Permutation)permutations[i].Clone(), new DoubleValue(qualities[i].Value));
assignment.Distances = distances;
BestSolutionParameter.ActualValue = assignment;
results.Add(new Result("Best QAP Solution", assignment));
} else {
if (max && assignment.Quality.Value < qualities[i].Value ||
!max && assignment.Quality.Value > qualities[i].Value) {
assignment.Distances = distances;
assignment.Weights = weights;
assignment.Assignment = (Permutation)permutations[i].Clone();
assignment.Quality.Value = qualities[i].Value;
}
}
return base.Apply();
}
