public override void CiclicCrossover(IndividualBase parent1, IndividualBase parent2, out IndividualBase child1, out IndividualBase child2)
{
int[] L1 = (int[])(parent1 as CryptArithmeticSolution).LettersValues.Clone();
int[] L2 = (int[])(parent2 as CryptArithmeticSolution).LettersValues.Clone();
List<int> positionsToShare = new List<int>();
int amountOfChars = charArray.Count();
int itemIdx = Aleatoriety.GetRandomInt(amountOfChars);
int tempValue;
while (!positionsToShare.Contains(itemIdx)) {
positionsToShare.Add(itemIdx);
tempValue = L1[itemIdx];
itemIdx = Array.IndexOf(L2, tempValue);
}
int tempVal;
foreach (int position in positionsToShare) {
tempVal = L2[position];
L2[position] = L1[position];
L1[position] = tempVal;
}
child1 = new CryptArithmeticSolution(this, L1);
child2 = new CryptArithmeticSolution(this, L2);
}
// TODO: Testing
public override void ValidateIndividual(IndividualBase individual)
{
TaskSchedulingSolution schedulingSolution = (individual as TaskSchedulingSolution);
List <List <TaskIndexingNode> > TasksGroupedByProcessor = GroupTasksByProcessor(schedulingSolution);
for (int processorIdx = 0; processorIdx < ProcessorCount; ++processorIdx)
{
List <TaskIndexingNode> TasksInProcessor = TasksGroupedByProcessor[processorIdx];
for (int probableDependantIdx = 0; probableDependantIdx < TasksInProcessor.Count; ++probableDependantIdx)
{
int probableDependantTask = TasksInProcessor[probableDependantIdx].Task;
for (int probableDependencyIdx = probableDependantIdx + 1; probableDependencyIdx < TasksInProcessor.Count; ++probableDependencyIdx)
{
int probableDependencyTask = TasksInProcessor[probableDependencyIdx].Task;
if (CommGraph.DependsOn(probableDependantTask, probableDependencyTask))
{
schedulingSolution.SwapGenesAt(TasksInProcessor[probableDependantIdx].Index, TasksInProcessor[probableDependencyIdx].Index);
TasksInProcessor.Swap(probableDependantIdx, probableDependencyIdx);
int Temp = TasksInProcessor[probableDependantIdx].Index;
TasksInProcessor[probableDependantIdx].Index = TasksInProcessor[probableDependencyIdx].Index;
TasksInProcessor[probableDependencyIdx].Index = Temp;
probableDependantIdx = -1;
break;
}
}
}
}
}
public override void EvaluateIndividual(IndividualBase individual)
{
CryptArithmeticSolution cryptoSolution = (individual as CryptArithmeticSolution);
long termsSum = 0;
int charValue = -1;
foreach (string term in terms) {
long termCharSum = 0;
long decimalBaseValTerm = 1;
foreach (char c in term.ToCharArray().Reverse()) {
charValue = cryptoSolution.LettersValues[Array.IndexOf(charArray, c)];
termCharSum += (charValue * decimalBaseValTerm);
decimalBaseValTerm *= 10;
}
termsSum += termCharSum;
}
long resultCharSum = 0;
long decimalBaseValResult = 1;
foreach (char c in result.ToCharArray().Reverse()) {
charValue = cryptoSolution.LettersValues[Array.IndexOf(charArray, c)];
resultCharSum += (charValue * decimalBaseValResult);
decimalBaseValResult *= 10;
}
long resultValue = resultCharSum;
cryptoSolution.DifferenceInExpectedResult = (int)Math.Abs(resultValue - termsSum);
individual = cryptoSolution;
}
public override void EvaluateIndividual(IndividualBase individual)
{
CryptArithmeticSolution cryptoSolution = (individual as CryptArithmeticSolution);
long termsSum = 0;
int charValue = -1;
foreach (string term in terms)
{
long termCharSum = 0;
long decimalBaseValTerm = 1;
foreach (char c in term.ToCharArray().Reverse())
{
charValue = cryptoSolution.LettersValues[Array.IndexOf(charArray, c)];
termCharSum += (charValue * decimalBaseValTerm);
decimalBaseValTerm *= 10;
}
termsSum += termCharSum;
}
long resultCharSum = 0;
long decimalBaseValResult = 1;
foreach (char c in result.ToCharArray().Reverse())
{
charValue = cryptoSolution.LettersValues[Array.IndexOf(charArray, c)];
resultCharSum += (charValue * decimalBaseValResult);
decimalBaseValResult *= 10;
}
long resultValue = resultCharSum;
cryptoSolution.DifferenceInExpectedResult = (int)Math.Abs(resultValue - termsSum);
individual = cryptoSolution;
}
private IndividualBase BestByCrowdedComparison(IndividualBase individual1, IndividualBase individual2)
{
if (individual1.NonDominationRank < individual2.NonDominationRank)
{
return(individual1);
}
if (individual2.NonDominationRank < individual1.NonDominationRank)
{
return(individual2);
}
// Se Estão no Mesmo Ranking de Não-Dominância
if (individual1.CrowdingDistance > individual2.CrowdingDistance)
{
return(individual1);
}
if (individual2.CrowdingDistance > individual1.CrowdingDistance)
{
return(individual2);
}
// Se têm a Mesma CrowdingDistance
if (Aleatoriety.GetRandomInt(2) == 0)
{
return(individual1);
}
else
{
return(individual2);
}
}
public override void CiclicCrossover(IndividualBase parent1, IndividualBase parent2, out IndividualBase child1, out IndividualBase child2)
{
int[] L1 = (int[])(parent1 as CryptArithmeticSolution).LettersValues.Clone();
int[] L2 = (int[])(parent2 as CryptArithmeticSolution).LettersValues.Clone();
List <int> positionsToShare = new List <int>();
int amountOfChars = charArray.Count();
int itemIdx = Aleatoriety.GetRandomInt(amountOfChars);
int tempValue;
while (!positionsToShare.Contains(itemIdx))
{
positionsToShare.Add(itemIdx);
tempValue = L1[itemIdx];
itemIdx = Array.IndexOf(L2, tempValue);
}
int tempVal;
foreach (int position in positionsToShare)
{
tempVal = L2[position];
L2[position] = L1[position];
L1[position] = tempVal;
}
child1 = new CryptArithmeticSolution(this, L1);
child2 = new CryptArithmeticSolution(this, L2);
}
//CONFERIR
public int finalFitnessComparison(IndividualBase i1, IndividualBase i2)
{
return(i1.FinalFitness < i2.FinalFitness ? // se i1 for menor
1 : //retorna 1 (i1)
i2.FinalFitness < i1.FinalFitness ? //se i2 for menor
-1 : //retorna -1 (i2)
0); //se for igual, retorna 0
}
public void RandomInitialize()
{
// Generate Initial Population
for (int idx = 0; idx < this.InitialPopulationSize; ++idx)
{
IndividualBase individual = problem.CreateRandomSolution();
IndividualEvaluator.Execute(individual, problem);
AddIndividual(individual);
}
}
public IndividualBase Execute(int numberOfExecutions, out string convergenceReport)
{
int bestResultCount;
IndividualBase bestSolutionFound = Execute(numberOfExecutions, out bestResultCount);
int bestSolutionOccurencePercentage = (bestResultCount * 100 / numberOfExecutions);
convergenceReport = "A melhor solução encontrada foi de valor: " +
bestSolutionFound.GetValueForObjective(Problem.MonoObjectiveGoal) +
", tendo ocorrido: " + bestSolutionOccurencePercentage + "% das execuções.";
return(bestSolutionFound);
}
private double DistanceBetween(IndividualBase i1, IndividualBase i2)
{
double distance = 0;
foreach (Objective objective in i1.Problem.MultiObjectiveGoal)
{
double difference = i1.GetNormalizedValueForObjective(objective) - i2.GetNormalizedValueForObjective(objective); //calcular a distancia parcial como sendo a diferença
distance += Math.Pow(difference, 2);
}
return(distance);
}
private IndividualBase BinaryTournment(Population_MultiObjective_AG population)
{
IndividualBase[] contestants = new IndividualBase[2];
contestants[0] = population.GetRandomIndividual();
do
{
contestants[1] = population.GetRandomIndividual();
} while (contestants[0] == contestants[1]);
return(BestByCrowdedComparison(contestants[0], contestants[1]));
}
public override void MutateIndividual(IndividualBase individual)
{
TaskSchedulingSolution schedulingSolution = (individual as TaskSchedulingSolution);
int indexA = Aleatoriety.GetRandomInt(TaskCount);
int indexB;
do
{
indexB = Aleatoriety.GetRandomInt(TaskCount);
} while (indexB == indexA);
schedulingSolution.SwapGenesAt(indexA, indexB);
schedulingSolution.SwapProcessorAt(indexA);
}
public static int CrowdedComparison(IndividualBase i1, IndividualBase i2)
{
int partial = i1.NonDominationRank < i2.NonDominationRank ? // se i1 for de um front menor
1 : //retorna 1 (i1)
i2.NonDominationRank < i1.NonDominationRank ? //se i2 for de um front menor
-1 : //retorna -1 (i2)
0; //se for igual, retorna a comparação entre as distâncias
if (!partial.Equals(0))
{
return(partial);
}
return(i1.CrowdingDistance.CompareTo(i2.CrowdingDistance));
}
public override void PMXCrossover(IndividualBase parent1, IndividualBase parent2, out IndividualBase child1, out IndividualBase child2)
{
int[,] parent1Scheduling = (parent1 as TaskSchedulingSolution).GeneticMaterial;
int[,] parent2Scheduling = (parent2 as TaskSchedulingSolution).GeneticMaterial;
int startIdxInclusive = Aleatoriety.GetRandomInt(TaskCount - 1);
int endIdxExclusive = Aleatoriety.GetRandomInt(startIdxInclusive + 1, TaskCount + 1);
int[,] L1 = new int[2, TaskCount];
int[,] L2 = new int[2, TaskCount];
int L1idx;
int L2idx;
for (int idx = 0; idx < TaskCount; ++idx)
{
if ((idx < startIdxInclusive) || (idx >= endIdxExclusive))
{
int L1CopyFromTask = parent1Scheduling[0, idx];
int L1CopyFromProcessor = parent1Scheduling[1, idx];
while ((L1idx = IndexOfTaskInInterval(L1CopyFromTask, startIdxInclusive, endIdxExclusive, parent2Scheduling)) != -1)
{
L1CopyFromTask = parent1Scheduling[0, L1idx];
L1CopyFromProcessor = parent1Scheduling[1, L1idx];
}
L1[0, idx] = L1CopyFromTask;
L1[1, idx] = L1CopyFromProcessor;
int L2CopyFromTask = parent2Scheduling[0, idx];
int L2CopyFromProcessor = parent2Scheduling[1, idx];
while ((L2idx = IndexOfTaskInInterval(L2CopyFromTask, startIdxInclusive, endIdxExclusive, parent1Scheduling)) != -1)
{
L2CopyFromTask = parent2Scheduling[0, L2idx];
L2CopyFromProcessor = parent2Scheduling[1, L2idx];
}
L2[0, idx] = L2CopyFromTask;
L2[1, idx] = L2CopyFromProcessor;
}
else
{
L1[0, idx] = parent2Scheduling[0, idx];
L1[1, idx] = parent2Scheduling[1, idx];
L2[0, idx] = parent1Scheduling[0, idx];
L2[1, idx] = parent1Scheduling[1, idx];
}
}
child1 = new TaskSchedulingSolution(this, L1);
child2 = new TaskSchedulingSolution(this, L2);
}
public void RandomInitialize()
{
// Generate Initial Population
while (IndividualCount < this.InitialPopulationSize)
{
IndividualBase individual = Problem.CreateRandomSolution();
Problem.ValidateIndividual(individual);
IndividualEvaluator.Execute(individual, Problem);
if (!Content.Contains(individual))
{
AddIndividual(individual);
}
}
}
public static void Execute(IndividualBase individual, ProblemBase problem)
{
problem.EvaluateIndividual(individual);
if (!individual.WasEvaluated)
{
individual.ObjectivesValuesForMaximization.Add(double.MaxValue);
individual.ObjectivesValuesForNormalization.Add(double.MaxValue);
}
Objective objective = problem.MonoObjectiveGoal;
individual.SetFitnessForObjective(objective);
individual.WasEvaluated = true;
}
protected override void EvaluateObjectiveValues()
{
// Create Temporary Individuals
List <IndividualBase> temporaryList = new List <IndividualBase>();
for (int iCount = 0; iCount < 200; ++iCount)
{
IndividualBase i = Problem.CreateRandomSolution();
IndividualEvaluator.Execute(i, Problem);
temporaryList.Add(i);
}
double maxValueFound = temporaryList.Max(I => I.GetFitnessForObjective(Problem.MonoObjectiveGoal));
double adaptedMaxValue = Math.Abs(maxValueFound) * 2;
Problem.MonoObjectiveGoal.BigValueForMaximization = adaptedMaxValue;
}
public override string SerializeIndividual(IndividualBase individual)
{
StringBuilder SB = new StringBuilder();
SB.AppendLine("Individual:");
SB.AppendLine("\t" + "Value: " + individual.GetValueForObjective(MonoObjectiveGoal));
CryptArithmeticSolution cryptoSolution = (individual as CryptArithmeticSolution);
int charValue = -1;
for (int idx = 0; idx < charArray.Length; ++idx)
{
charValue = cryptoSolution.LettersValues[idx];
SB.AppendLine("\t" + charArray[idx] + ": " + charValue);
}
return(SB.ToString());
}
private Population_MultiObjective_AG Procreate(Population_MultiObjective_AG mating)
{
int expectedChildCount = InitialPopulationSize;
Population_MultiObjective_AG newGeneration = new Population_MultiObjective_AG(Problem, expectedChildCount);
while (newGeneration.IndividualCount < expectedChildCount)
{
// Selection Method
IndividualBase parent1 = BinaryTournment(mating);
IndividualBase parent2 = BinaryTournment(mating);
IndividualBase child1 = null, child2 = null;
// Crossover Method
Problem.PMXCrossover(parent1, parent2, out child1, out child2);
for (int i = 0; i < 10; ++i)
{
if (!newGeneration.Content.Contains(child1) || i == 9)
{
newGeneration.AddIndividual(child1);
break;
}
}
for (int i = 0; i < 10; ++i)
{
if (!newGeneration.Content.Contains(child2) || i == 9)
{
newGeneration.AddIndividual(child2);
break;
}
}
}
foreach (IndividualBase individual in newGeneration.Content)
{
int aleatoryPercentage = Aleatoriety.GetRandomInt(100);
if (aleatoryPercentage < mutationPct)
{
Problem.MutateIndividual(individual);
}
Problem.ValidateIndividual(individual);
IndividualEvaluator.Execute(individual, Problem);
}
return(newGeneration);
}
public override string NewSerializeIndividual(IndividualBase individual)
{
TaskSchedulingSolution schedulingSolution = (individual as TaskSchedulingSolution);
List <List <TaskIndexingNode> > tasksGroupedByProcessor = GroupTasksByProcessor(schedulingSolution);
StringBuilder sb = new StringBuilder();
sb.Append("MakeSpan: " + individual.GetValueForObjective(MultiObjectiveGoal.First()) + " _ ");
sb.Append("Potencia: " + individual.GetValueForObjective(MultiObjectiveGoal.Last()) + " _ ");
for (int idx = 0; idx < TaskCount - 1; ++idx)
{
sb.Append(" " + schedulingSolution.GeneticMaterial[0, idx] + " (" + schedulingSolution.GeneticMaterial[1, idx] + ") -");
}
sb.Append(" " + schedulingSolution.GeneticMaterial[0, TaskCount - 1] + " (" + schedulingSolution.GeneticMaterial[1, TaskCount - 1] + ")");
return(sb.ToString());
}
public override void MutateIndividual(IndividualBase individual)
{
CryptArithmeticSolution cryptoSolution = (individual as CryptArithmeticSolution);
int amountOfChars = charArray.Count();
int firstPositionIdx = Aleatoriety.GetRandomInt(amountOfChars);
int secondPositionIdx;
do
{
secondPositionIdx = Aleatoriety.GetRandomInt(amountOfChars);
} while (secondPositionIdx == firstPositionIdx);
int tempValue = cryptoSolution.LettersValues[secondPositionIdx];
cryptoSolution.LettersValues[secondPositionIdx] = cryptoSolution.LettersValues[firstPositionIdx];
cryptoSolution.LettersValues[firstPositionIdx] = tempValue;
individual = cryptoSolution;
}
public override void Execute(PopulationBase population, out IndividualBase chosenIndividual1, out IndividualBase chosenIndividual2)
{
IndividualBase[] selectedParents = new IndividualBase[2];
for (int individualIdx = 0; individualIdx < 2; ++individualIdx)
{
selectedParents[individualIdx] = population.GetRandomIndividual();
for (int selectionIdx = 1; selectionIdx < tourSize; ++selectionIdx)
{
IndividualBase randomIndividual = population.GetRandomIndividual();
if (randomIndividual.GetFitnessForObjective(Problem.MonoObjectiveGoal) > selectedParents[individualIdx].GetFitnessForObjective(Problem.MonoObjectiveGoal))
{
selectedParents[individualIdx] = randomIndividual;
}
}
}
chosenIndividual1 = selectedParents[0];
chosenIndividual2 = selectedParents[1];
}
private IndividualBase BinaryTournment(Population_MultiObjective_AG population)
{
IndividualBase[] contestants = new IndividualBase[2];
contestants[0] = population.GetRandomIndividual();
for (int i = 0; i < 10; ++i)
{
contestants[1] = population.GetRandomIndividual();
if ((contestants[0] != contestants[1]) || i == 9)
{
break;
}
}
//do {
// contestants[1] = population.GetRandomIndividual();
//} while (contestants[0] == contestants[1]);
return(finalFitnessComparison(contestants[0], contestants[1]) >= 0 ? contestants[0] : contestants[1]);
}
public void Truncate(int archiveSize)
{
CalculateSortedDistances();
while (IndividualCount > archiveSize)
{
double smallestDistanceValue = double.MaxValue;
int smallestDistanceIdx = 0;
bool IsSmallestDistanceUniqueByIdx = true;
for (int distanceIdx = 0; distanceIdx < Content.Count; ++distanceIdx)
{
IsSmallestDistanceUniqueByIdx = true;
foreach (IndividualBase individual in Content)
{
double distanceForIdx = individual.DistancesToOtherIndividuals[distanceIdx].Item2;
if (distanceForIdx < smallestDistanceValue)
{
IsSmallestDistanceUniqueByIdx = true;
smallestDistanceIdx = distanceIdx;
smallestDistanceValue = distanceForIdx;
}
else
{
IsSmallestDistanceUniqueByIdx = false;
if (smallestDistanceValue == 0)
{
break;
}
}
}
if (IsSmallestDistanceUniqueByIdx)
{
break;
}
}
IndividualBase individualForRemoval = Content[smallestDistanceIdx];
Content.Remove(individualForRemoval);
foreach (IndividualBase i in Content)
{
i.DistancesToOtherIndividuals.RemoveAll(I => I.Item1 == individualForRemoval);
}
}
}
public override string SerializeIndividual(IndividualBase individual)
{
TaskSchedulingSolution schedulingSolution = (individual as TaskSchedulingSolution);
List <List <TaskIndexingNode> > tasksGroupedByProcessor = GroupTasksByProcessor(schedulingSolution);
StringBuilder sb = new StringBuilder();
sb.AppendLine("Individual:");
sb.AppendLine("\t" + "Value: " + individual.GetValueForObjective(MonoObjectiveGoal));
for (int processorIdx = 0; processorIdx < ProcessorCount; ++processorIdx)
{
sb.Append("\t" + "P" + processorIdx + ": " + "[");
foreach (int task in tasksGroupedByProcessor[processorIdx].Select(TIN => TIN.Task))
{
sb.Append(" " + task + " |");
}
sb.Append("| " + schedulingSolution.ProcessorStatus[processorIdx] + " ]");
sb.Append(Environment.NewLine);
}
return(sb.ToString());
}
private IndividualBase Execute(int numberOfExecutions, out int bestResultCount)
{
IndividualBase bestIndividual = Execute();
double bestCurrentValue = bestIndividual.GetFitnessForObjective(Problem.MonoObjectiveGoal);
bestResultCount = 1;
for (int executionCount = 1; executionCount < numberOfExecutions; ++executionCount)
{
IndividualBase currentIndividual = Execute();
double currentValue = currentIndividual.GetFitnessForObjective(Problem.MonoObjectiveGoal);
if (currentValue > bestCurrentValue)
{
bestIndividual = currentIndividual;
bestCurrentValue = currentValue;
bestResultCount = 1;
}
else if (currentValue == bestCurrentValue)
{
++bestResultCount;
}
}
return(bestIndividual);
}
private Population_MonoObjective_AG RunGeneration(Population_MonoObjective_AG currentGeneration, SelectionMethodBase selectionMethod, CrossoverMethodBase crossoverMethod, /*IReinsertionMethod reinsertionMethod, */ ReinsertionMethodBase reinsertionMethod)
{
Population_MonoObjective_AG recentlyBorn = new Population_MonoObjective_AG(Problem, InitialPopulationSize);
int expectedChildCount = ((InitialPopulationSize * reinsertionMethod.OffspringPercentage) / 100);
while (recentlyBorn.IndividualCount < expectedChildCount)
{
IndividualBase parent1 = null, parent2 = null, child1 = null, child2 = null;
selectionMethod.Execute(currentGeneration, out parent1, out parent2);
crossoverMethod.Execute(parent1, parent2, out child1, out child2);
recentlyBorn.AddIndividual(child1);
recentlyBorn.AddIndividual(child2);
}
int mutatedChildCount = ((recentlyBorn.IndividualCount * mutationPct) / 100);
for (int idx = 0; idx < mutatedChildCount; ++idx)
{
IndividualBase randomIndividual = recentlyBorn.GetRandomIndividual();
Problem.MutateIndividual(randomIndividual);
}
IndividualBase individual = null;
for (int idx = 0; idx < recentlyBorn.IndividualCount; ++idx)
{
individual = recentlyBorn.Content[idx];
Problem.ValidateIndividual(individual);
IndividualEvaluator.Execute(individual, Problem);
}
Population_MonoObjective_AG newGeneration = reinsertionMethod.Execute(currentGeneration, recentlyBorn);
return(newGeneration);
}
public override void CiclicCrossover(IndividualBase parent1, IndividualBase parent2, out IndividualBase child1, out IndividualBase child2)
{
throw new NotImplementedException();
}
// TODO: Testing
public override void ValidateIndividual(IndividualBase individual)
{
TaskSchedulingSolution schedulingSolution = (individual as TaskSchedulingSolution);
List<List<TaskIndexingNode>> TasksGroupedByProcessor = GroupTasksByProcessor(schedulingSolution);
for (int processorIdx = 0; processorIdx < ProcessorCount; ++processorIdx) {
List<TaskIndexingNode> TasksInProcessor = TasksGroupedByProcessor[processorIdx];
for (int probableDependantIdx = 0; probableDependantIdx < TasksInProcessor.Count; ++probableDependantIdx) {
int probableDependantTask = TasksInProcessor[probableDependantIdx].Task;
for (int probableDependencyIdx = probableDependantIdx + 1; probableDependencyIdx < TasksInProcessor.Count; ++probableDependencyIdx) {
int probableDependencyTask = TasksInProcessor[probableDependencyIdx].Task;
if(CommGraph.DependsOn(probableDependantTask, probableDependencyTask)) {
schedulingSolution.SwapGenesAt(TasksInProcessor[probableDependantIdx].Index, TasksInProcessor[probableDependencyIdx].Index);
TasksInProcessor.Swap(probableDependantIdx, probableDependencyIdx);
int Temp = TasksInProcessor[probableDependantIdx].Index;
TasksInProcessor[probableDependantIdx].Index = TasksInProcessor[probableDependencyIdx].Index;
TasksInProcessor[probableDependencyIdx].Index = Temp;
probableDependantIdx = -1;
break;
}
}
}
}
}
public override string SerializeIndividual(IndividualBase individual)
{
TaskSchedulingSolution schedulingSolution = (individual as TaskSchedulingSolution);
List<List<TaskIndexingNode>> tasksGroupedByProcessor = GroupTasksByProcessor(schedulingSolution);
StringBuilder sb = new StringBuilder();
sb.AppendLine("Individual:");
sb.AppendLine("\t" + "Value: " + individual.GetValueForObjective(MonoObjectiveGoal));
for (int processorIdx = 0; processorIdx < ProcessorCount; ++processorIdx) {
sb.Append("\t" + "P" + processorIdx + ": " + "[");
foreach (int task in tasksGroupedByProcessor[processorIdx].Select(TIN => TIN.Task)) {
sb.Append(" " + task + " |");
}
sb.Append("| " + schedulingSolution.ProcessorStatus[processorIdx] + " ]");
sb.Append(Environment.NewLine);
}
return sb.ToString();
}
public override void PMXCrossover(IndividualBase parent1, IndividualBase parent2, out IndividualBase child1, out IndividualBase child2)
{
int[,] parent1Scheduling = (parent1 as TaskSchedulingSolution).GeneticMaterial;
int[,] parent2Scheduling = (parent2 as TaskSchedulingSolution).GeneticMaterial;
int startIdxInclusive = Aleatoriety.GetRandomInt(TaskCount - 1);
int endIdxExclusive = Aleatoriety.GetRandomInt(startIdxInclusive + 1, TaskCount + 1);
int[,] L1 = new int[2, TaskCount];
int[,] L2 = new int[2, TaskCount];
int L1idx;
int L2idx;
for (int idx = 0; idx < TaskCount; ++idx) {
if ((idx < startIdxInclusive) || (idx >= endIdxExclusive)) {
int L1CopyFromTask = parent1Scheduling[0, idx];
int L1CopyFromProcessor = parent1Scheduling[1, idx];
while ((L1idx = IndexOfTaskInInterval(L1CopyFromTask, startIdxInclusive, endIdxExclusive, parent2Scheduling)) != -1) {
L1CopyFromTask = parent1Scheduling[0, L1idx];
L1CopyFromProcessor = parent1Scheduling[1, L1idx];
}
L1[0, idx] = L1CopyFromTask;
L1[1, idx] = L1CopyFromProcessor;
int L2CopyFromTask = parent2Scheduling[0, idx];
int L2CopyFromProcessor = parent2Scheduling[1, idx];
while ((L2idx = IndexOfTaskInInterval(L2CopyFromTask, startIdxInclusive, endIdxExclusive, parent1Scheduling)) != -1) {
L2CopyFromTask = parent2Scheduling[0, L2idx];
L2CopyFromProcessor = parent2Scheduling[1, L2idx];
}
L2[0, idx] = L2CopyFromTask;
L2[1, idx] = L2CopyFromProcessor;
}
else {
L1[0, idx] = parent2Scheduling[0, idx];
L1[1, idx] = parent2Scheduling[1, idx];
L2[0, idx] = parent1Scheduling[0, idx];
L2[1, idx] = parent1Scheduling[1, idx];
}
}
child1 = new TaskSchedulingSolution(this, L1);
child2 = new TaskSchedulingSolution(this, L2);
}
public override string NewSerializeIndividual(IndividualBase individual)
{
TaskSchedulingSolution schedulingSolution = (individual as TaskSchedulingSolution);
List<List<TaskIndexingNode>> tasksGroupedByProcessor = GroupTasksByProcessor(schedulingSolution);
StringBuilder sb = new StringBuilder();
sb.Append("MakeSpan: " + individual.GetValueForObjective(MultiObjectiveGoal.First()) + " _ ");
sb.Append("Potencia: " + individual.GetValueForObjective(MultiObjectiveGoal.Last()) + " _ ");
for (int idx = 0; idx < TaskCount - 1; ++idx)
sb.Append(" " + schedulingSolution.GeneticMaterial[0, idx] + " (" + schedulingSolution.GeneticMaterial[1, idx] + ") -");
sb.Append(" " + schedulingSolution.GeneticMaterial[0, TaskCount - 1] + " (" + schedulingSolution.GeneticMaterial[1, TaskCount - 1] + ")");
return sb.ToString();
}
public override void MutateIndividual(IndividualBase individual)
{
TaskSchedulingSolution schedulingSolution = (individual as TaskSchedulingSolution);
int indexA = Aleatoriety.GetRandomInt(TaskCount);
int indexB;
do {
indexB = Aleatoriety.GetRandomInt(TaskCount);
} while (indexB == indexA);
schedulingSolution.SwapGenesAt(indexA, indexB);
schedulingSolution.SwapProcessorAt(indexA);
}
public override void EvaluateIndividual(IndividualBase individual)
{
REDO:
TaskSchedulingSolution schedulingSolution = individual as TaskSchedulingSolution;
schedulingSolution.SpentPower = 0;
double pCommunication = 0;
int pTask = 0;
bool[] scheduledTasks = new bool[TaskCount];
int remainingTasks = TaskCount;
int[] processorsSchedullingTimeForTask = new int[TaskCount];
schedulingSolution.ProcessorStatus = new int[ProcessorCount];
int currentAllocationCost;
List<List<TaskIndexingNode>> tasksGroupedByProcessor = GroupTasksByProcessor(schedulingSolution);
bool deadLockOcurred;
do {
for (int currentProcessor = 0; currentProcessor < ProcessorCount; ++currentProcessor) {
if (tasksGroupedByProcessor[currentProcessor].Count != 0) {
int nextTaskForProcessor = tasksGroupedByProcessor[currentProcessor][0].Task;
IEnumerable<int> notScheduledTaskDependencies = CommGraph.GetDirectDependencies(nextTaskForProcessor).Where(Dep => scheduledTasks[Dep] == false);
bool readyForScheduling = (notScheduledTaskDependencies.Count() == 0);
if (readyForScheduling) {
int currentTask = nextTaskForProcessor;
currentAllocationCost = schedulingSolution.ProcessorStatus[currentProcessor];
foreach (int dependencyTask in CommGraph.GetDirectDependencies(currentTask)) {
int idx = 0;
while (schedulingSolution.GeneticMaterial[0, idx] != dependencyTask)
++idx;
int dependencyProcessor = schedulingSolution.GeneticMaterial[1, idx];
if (dependencyProcessor != currentProcessor) {
pCommunication += Math.Pow(CommGraph.GetCommunicationCost(dependencyTask, currentTask), 2);
int dependencyCost = processorsSchedullingTimeForTask[dependencyTask] + CommGraph.GetCommunicationCost(dependencyTask, currentTask);
if (dependencyCost > currentAllocationCost)
currentAllocationCost = dependencyCost;
}
}
currentAllocationCost += CommGraph.GetEdgeCost(currentTask);
schedulingSolution.ProcessorStatus[currentProcessor] = currentAllocationCost;
processorsSchedullingTimeForTask[currentTask] = currentAllocationCost;
pTask += CommGraph.GetEdgeCost(currentTask);
scheduledTasks[currentTask] = true;
tasksGroupedByProcessor[currentProcessor].RemoveAt(0);
remainingTasks--;
currentProcessor = -1;
}
}
}
if (remainingTasks != 0) {
deadLockOcurred = true;
// Tratamento de DeadLock
int chosenSourceProcessor;
do {
chosenSourceProcessor = Aleatoriety.GetRandomInt(ProcessorCount);
} while (tasksGroupedByProcessor[chosenSourceProcessor].Count == 0);
int chosenDestProcessor;
do {
chosenDestProcessor = Aleatoriety.GetRandomInt(ProcessorCount);
} while (chosenSourceProcessor == chosenDestProcessor);
int taskToBeTransfered = tasksGroupedByProcessor[chosenSourceProcessor].First().Task;
int idx = 0;
while (schedulingSolution.GeneticMaterial[0, idx] != taskToBeTransfered)
++idx;
schedulingSolution.GeneticMaterial[1, idx] = chosenDestProcessor;
ValidateIndividual(schedulingSolution);
goto REDO;
}
else
deadLockOcurred = false;
} while (deadLockOcurred);
// Houve DeadLock
schedulingSolution.MakeSpan = schedulingSolution.ProcessorStatus.Max();
schedulingSolution.SpentPower = (pTask * k1) + (pCommunication * k2);
}
public override void Execute(PopulationBase population, out IndividualBase chosenIndividual1, out IndividualBase chosenIndividual2)
{
throw new NotImplementedException();
}
public override string SerializeIndividual(IndividualBase individual)
{
StringBuilder SB = new StringBuilder();
SB.AppendLine("Individual:");
SB.AppendLine("\t" + "Value: " + individual.GetValueForObjective(MonoObjectiveGoal));
CryptArithmeticSolution cryptoSolution = (individual as CryptArithmeticSolution);
int charValue = -1;
for (int idx = 0; idx < charArray.Length; ++idx) {
charValue = cryptoSolution.LettersValues[idx];
SB.AppendLine("\t" + charArray[idx] + ": " + charValue);
}
return SB.ToString();
}
public override void ValidateIndividual(IndividualBase individual)
{
}
public override void CiclicCrossover(IndividualBase parent1, IndividualBase parent2, out IndividualBase child1, out IndividualBase child2)
{
throw new NotImplementedException();
}
public override void Execute(IndividualBase parent1, IndividualBase parent2, out IndividualBase child1, out IndividualBase child2)
{
Problem.CiclicCrossover(parent1, parent2, out child1, out child2);
}
public abstract void Execute(IndividualBase parent1, IndividualBase parent2, out IndividualBase child1, out IndividualBase child2);
public abstract void Execute(PopulationBase population, out IndividualBase chosenIndividual1, out IndividualBase chosenIndividual2);
public override void MutateIndividual(IndividualBase individual)
{
CryptArithmeticSolution cryptoSolution = (individual as CryptArithmeticSolution);
int amountOfChars = charArray.Count();
int firstPositionIdx = Aleatoriety.GetRandomInt(amountOfChars);
int secondPositionIdx;
do {
secondPositionIdx = Aleatoriety.GetRandomInt(amountOfChars);
} while (secondPositionIdx == firstPositionIdx);
int tempValue = cryptoSolution.LettersValues[secondPositionIdx];
cryptoSolution.LettersValues[secondPositionIdx] = cryptoSolution.LettersValues[firstPositionIdx];
cryptoSolution.LettersValues[firstPositionIdx] = tempValue;
individual = cryptoSolution;
}
