public FSPSolution GenerateNeighbor(TabuMemory explicitLocalTabuMemory)
{
var copyCurrentSolution = new FSPSolution(this);
var selectedPhrases = copyCurrentSolution.ObtainSelectedPhrasesSortedByCoverageCosine();
// Remove the last sentence (phrases with low coverage) ... C = 1
copyCurrentSolution.InActivate(selectedPhrases[selectedPhrases.Count - 1].Position);
var excludePhrases = new List <int> {
selectedPhrases[selectedPhrases.Count - 1].Position
};
var tries = 0;
const int maxTries = 5;
FSPSolution newCandidateSolution = null;
do
{
newCandidateSolution = new FSPSolution(copyCurrentSolution);
// Try to add one or more randomly selected phrases to complete the solution
newCandidateSolution.AddValidPhrases(excludePhrases);
if (tries++ < maxTries)
{
break; // avoid long time in the loop
}
} while (explicitLocalTabuMemory.IsTabu(newCandidateSolution.SelectedPhrases));
newCandidateSolution.CalculateFitness();
return(newCandidateSolution);
}
public List <PositionValue> Execute()
{
CurrentFFEs = 0;
MaximumNumberOfFitnessFunctionEvaluations = MyParameters.MaximumNumberOfFitnessFunctionEvaluations;
var myParameters = (FSPParameters)MyParameters;
CalculateRankingPhrasePosition();
SortLengths();
CapturePoints = new List <FSPSolution>();
var pi = new List <FSPSolution>(); // Local memory of the best solution found in each neighborhood of the capture point
var lemt = new List <TabuMemory>();
Best = new FSPSolution(this);
// Population initialization, fitness is calculated for each agent and local search is applied
for (var i = 0; i < myParameters.N; i++)
{
var newCapturePoint = new FSPSolution(this);
newCapturePoint.RandomInitialization();
CapturePoints.Add(newCapturePoint);
pi.Add(new FSPSolution(newCapturePoint));
lemt.Add(new TabuMemory(myParameters.Tenure));
lemt[lemt.Count - 1].Include(newCapturePoint.SelectedPhrases);
if (CurrentFFEs > MaximumNumberOfFitnessFunctionEvaluations) // MaxFFEs exceeded?
{
break;
}
}
CapturePoints.Sort((x, y) => - 1 * x.Fitness.CompareTo(y.Fitness));
pi.Sort((x, y) => - 1 * x.Fitness.CompareTo(y.Fitness));
Best = new FSPSolution(CapturePoints[0]);
while (CurrentFFEs < MaximumNumberOfFitnessFunctionEvaluations)
{
for (var i = 0; i < myParameters.N; i++)
{
// Each fisherman at his capture throws the net L times
for (var auxL = 0; auxL < myParameters.L; auxL++)
{
// Throw the network, update PI and Best if it is the case
var mejorsolucionLocal =
CapturePoints[i].GenerateNeighbor(lemt[i]);
lemt[i].Include(mejorsolucionLocal.SelectedPhrases);
if (mejorsolucionLocal.Fitness > pi[i].Fitness)
{
pi[i] = new FSPSolution(mejorsolucionLocal);
}
if (CurrentFFEs >= MaximumNumberOfFitnessFunctionEvaluations)
{
break;
}
}
if (pi[i].Fitness > CapturePoints[i].Fitness)
{
CapturePoints[i] = new FSPSolution(pi[i]);
}
if (CapturePoints[i].Fitness > Best.Fitness)
{
Best = new FSPSolution(CapturePoints[i]);
}
if (CurrentFFEs >= MaximumNumberOfFitnessFunctionEvaluations)
{
break;
}
}
CapturePoints.Sort((x, y) => - 1 * x.Fitness.CompareTo(y.Fitness));
pi.Sort((x, y) => - 1 * x.Fitness.CompareTo(y.Fitness));
if (CurrentFFEs >= MaximumNumberOfFitnessFunctionEvaluations)
{
break;
}
}
var mostRepeated = SelectToCompleteSummary(new List <BaseSolution>(CapturePoints), Best);
var phrasesList = SelectPhrasesFromFinalSummary(Best.SelectedPhrases, mostRepeated);
return(phrasesList);
}
