public override IEnumerable<IndividualBase> Execute()
{
//Initialize Population + Evaluate Objective Values
Population = new Population_MultiObjective_AG(Problem, InitialPopulationSize);
Population.RandomInitialize();
// Assign Rank Based on Pareto Dominance
FastNonDominatedSort(Population);
// Assign Crowding Distance for the Population
CrowdingDistanceAssignment(Population.Content);
// Creates Offspring through Selection, Recombination, and Mutation
Population_MultiObjective_AG childPopulation = Procreate(Population);
Population_MultiObjective_AG Pnext = Population;
Population_MultiObjective_AG Qnext = childPopulation;
Population_MultiObjective_AG currentGeneration = new Population_MultiObjective_AG(Problem, 2 * InitialPopulationSize);
currentGeneration.AddPopulation(Pnext);
currentGeneration.AddPopulation(Qnext);
for (int idxGeracao = 1; idxGeracao < NumberOfGenerations; ++idxGeracao) {
Pnext = RunGeneration(currentGeneration);
Qnext = Procreate(Pnext);
currentGeneration = new Population_MultiObjective_AG(Problem, 2 * InitialPopulationSize);
currentGeneration.AddPopulation(Pnext);
currentGeneration.AddPopulation(Qnext);
}
return FastNonDominatedSort(currentGeneration).First().Content;
}
public override IEnumerable <IndividualBase> Execute()
{
//Initialize Population + Evaluate Objective Values
Population = new Population_MultiObjective_AG(Problem, InitialPopulationSize);
Population.RandomInitialize();
// Assign Rank Based on Pareto Dominance
FastNonDominatedSort(Population);
// Assign Crowding Distance for the Population
CrowdingDistanceAssignment(Population.Content);
// Creates Offspring through Selection, Recombination, and Mutation
Population_MultiObjective_AG childPopulation = Procreate(Population);
Population_MultiObjective_AG Pnext = Population;
Population_MultiObjective_AG Qnext = childPopulation;
Population_MultiObjective_AG currentGeneration = new Population_MultiObjective_AG(Problem, 2 * InitialPopulationSize);
currentGeneration.AddPopulation(Pnext);
currentGeneration.AddPopulation(Qnext);
for (int idxGeracao = 1; idxGeracao < NumberOfGenerations; ++idxGeracao)
{
Pnext = RunGeneration(currentGeneration);
Qnext = Procreate(Pnext);
currentGeneration = new Population_MultiObjective_AG(Problem, 2 * InitialPopulationSize);
currentGeneration.AddPopulation(Pnext);
currentGeneration.AddPopulation(Qnext);
}
return(FastNonDominatedSort(currentGeneration).First().Content);
}
public List <Population_MultiObjective_AG> FastNonDominatedSort()
{
List <Population_MultiObjective_AG> Fronts = new List <Population_MultiObjective_AG>();
Population_MultiObjective_AG NonDominatedFront = new Population_MultiObjective_AG(Problem);
foreach (IndividualBase individual in Content)
{
individual.DominatesList = new List <IndividualBase>();
individual.DominatedByCount = 0;
foreach (IndividualBase another in Content.Where(I => !I.Equals(individual)))
{
if (individual.Dominates(another))
{
individual.DominatesList.Add(another); // Lista dos elementos que sao dominados
}
else if (another.Dominates(individual))
{
individual.DominatedByCount++; // O atributo np nao foi implementado ainda, se refere a qnt de elementos q o dominam
}
}
if (individual.DominatedByCount == 0)
{
individual.NonDominationRank = 1;
NonDominatedFront.AddIndividual(individual);
}
}
int NonDominationRank = 2;
Population_MultiObjective_AG CurrentFront = NonDominatedFront;
Population_MultiObjective_AG FrontQ = NonDominatedFront;
do
{
Fronts.Add(FrontQ);
FrontQ = new Population_MultiObjective_AG(Problem);
foreach (IndividualBase individual in CurrentFront.Content)
{
foreach (IndividualBase dominatedIndividual in individual.DominatesList)
{
dominatedIndividual.DominatedByCount--;
if (dominatedIndividual.DominatedByCount == 0)
{
dominatedIndividual.NonDominationRank = NonDominationRank;
FrontQ.AddIndividual(dominatedIndividual);
}
}
}
CurrentFront = FrontQ;
NonDominationRank++;
} while (FrontQ.IndividualCount != 0);
return(Fronts);
}
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]));
}
protected override Population_MultiObjective_AG RunGeneration(Population_MultiObjective_AG currentGeneration)
{
List<Population_MultiObjective_AG> fronts = FastNonDominatedSort(currentGeneration);
Population_MultiObjective_AG Pnext = new Population_MultiObjective_AG(Problem, InitialPopulationSize);
int i = 0;
while ((Pnext.IndividualCount + fronts[i].IndividualCount) < InitialPopulationSize) {
CrowdingDistanceAssignment(fronts[i].Content);
Pnext.AddPopulation(fronts[i]);
i++;
}
fronts[i].Content.Sort(CrowdedComparison);
fronts[i].Content.Reverse();
Pnext.AddRange(fronts[i].Content.Take(InitialPopulationSize - Pnext.IndividualCount));
return Pnext;
}
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);
}
protected override Population_MultiObjective_AG RunGeneration(Population_MultiObjective_AG currentGeneration)
{
List <Population_MultiObjective_AG> fronts = FastNonDominatedSort(currentGeneration);
Population_MultiObjective_AG Pnext = new Population_MultiObjective_AG(Problem, InitialPopulationSize);
int i = 0;
while ((Pnext.IndividualCount + fronts[i].IndividualCount) < InitialPopulationSize)
{
CrowdingDistanceAssignment(fronts[i].Content);
Pnext.AddPopulation(fronts[i]);
i++;
}
fronts[i].Content.Sort(CrowdedComparison);
fronts[i].Content.Reverse();
Pnext.AddRange(fronts[i].Content.Take(InitialPopulationSize - Pnext.IndividualCount));
return(Pnext);
}
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 override IEnumerable<IndividualBase> Execute()
{
//Inicializacao
Population = new Population_MultiObjective_AG(Problem, InitialPopulationSize);
Population.RandomInitialize();
Archive = new Population_MultiObjective_AG(Problem, ArchivePopulationSize);
Population_MultiObjective_AG currentGeneration = Population;
Population_MultiObjective_AG newGeneration = null;
for (int idxGeracao = 0; idxGeracao < NumberOfGenerations; ++idxGeracao) {
newGeneration = RunGeneration(currentGeneration);
currentGeneration = newGeneration;
}
IEnumerable<IndividualBase> nonDominatedFront = currentGeneration.FastNonDominatedSort().First().Content;
IEnumerable<IndividualBase> firstItemsByMakeSpan = (from i in nonDominatedFront.Cast<TaskSchedulingSolution>()
group i by i.MakeSpan into g
select g.First()).OrderBy(I => I.MakeSpan);
return firstItemsByMakeSpan;
}
public override IEnumerable <IndividualBase> Execute()
{
//Inicializacao
Population = new Population_MultiObjective_AG(Problem, InitialPopulationSize);
Population.RandomInitialize();
Archive = new Population_MultiObjective_AG(Problem, ArchivePopulationSize);
Population_MultiObjective_AG currentGeneration = Population;
Population_MultiObjective_AG newGeneration = null;
for (int idxGeracao = 0; idxGeracao < NumberOfGenerations; ++idxGeracao)
{
newGeneration = RunGeneration(currentGeneration);
currentGeneration = newGeneration;
}
IEnumerable <IndividualBase> nonDominatedFront = currentGeneration.FastNonDominatedSort().First().Content;
IEnumerable <IndividualBase> firstItemsByMakeSpan = (from i in nonDominatedFront.Cast <TaskSchedulingSolution>()
group i by i.MakeSpan into g
select g.First()).OrderBy(I => I.MakeSpan);
return(firstItemsByMakeSpan);
}
protected override Population_MultiObjective_AG RunGeneration(Population_MultiObjective_AG currentGeneration)
{
Population_MultiObjective_AG everyone = new Population_MultiObjective_AG(Problem, InitialPopulationSize + ArchivePopulationSize);
everyone.AddPopulation(currentGeneration);
everyone.AddPopulation(Archive);
#region Fitness Assignment
// The Strength of an Individual is the Count of those he Dominates
foreach (IndividualBase individual in everyone.Content) {
individual.Strength = everyone.Content.Where(I => !I.Equals(individual)).Count(I => individual.Dominates(I));
}
// The RawFitness of an Individual is the sum of the strengths of all those who Dominate him
foreach (IndividualBase individual in everyone.Content) {
individual.RawFitness = everyone.Content.Where(I => I.Dominates(individual)).Sum(D => D.Strength);
}
// The True Fitness is Calculated
everyone.CalculateSortedDistances();
int k = (int)Math.Sqrt(InitialPopulationSize + ArchivePopulationSize);
foreach (IndividualBase i in everyone.Content) {
double distanceSought = i.DistancesToOtherIndividuals[k].Item2;
double density = (1 / (distanceSought + 2));
i.FinalFitness = i.RawFitness + density;
}
#endregion
#region Environment Selection
Population_MultiObjective_AG nextArchive = new Population_MultiObjective_AG(Problem, ArchivePopulationSize);
List<IndividualBase> excludeList = new List<IndividualBase>();
//insere em nextArchive todos os elementos da populacao total com finalFitness < 1
//poderia ser tambem rawFitness = 0, mas no artigo especifica o dito acima, podemos seguir
//aproveitar pra remover do total os elementos inseridos, vai facilitar no proximo passo..
IEnumerable<IndividualBase> nonDominatedSolutions = everyone.Content.Where(I => I.FinalFitness < 1);
nextArchive.AddRange(nonDominatedSolutions);
everyone.Content.RemoveAll(I => nonDominatedSolutions.Contains(I));
if (nextArchive.IndividualCount < ArchivePopulationSize) {
everyone.Content.Sort(finalFitnessComparison);
//TODO: Confirmar se é melhor maior ou menor, esse reverse faz pegar os menores
everyone.Content.Reverse();
IEnumerable<IndividualBase> complement = everyone.Content.Take(ArchivePopulationSize - nextArchive.IndividualCount);
nextArchive.AddRange(complement);
}
else if(nextArchive.IndividualCount > ArchivePopulationSize) {
//trunca a população pela distancia
nextArchive.Truncate(ArchivePopulationSize);
}
//acho que não vai precisar do antigo mais.. conferir !
this.Archive = nextArchive;
#endregion
#region Mating Selection
//Seleção dos Pais da proxima populacao, leva em conta apenas archive
Population_MultiObjective_AG matingPool = Procreate(nextArchive);
#endregion
return matingPool;
}
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;
}
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 abstract Population_MultiObjective_AG Execute(Population_MultiObjective_AG lastGeneration, Population_MultiObjective_AG generatedChildren);
private static IEnumerable<IndividualBase> SPEA2MultiExecution(int executionCount, ProblemBase problem, int populationSize)
{
MultiObjectiveGeneticAlgorithm SPEA2 = new NSGA2(
problem,
populationSize,
200,
200,
10);
Population_MultiObjective_AG Aggregator = new Population_MultiObjective_AG(problem);
for (int i = 0; i < executionCount; ++i) {
IEnumerable<IndividualBase> executionResult = SPEA2.Execute();
Aggregator.AddRange(executionResult);
}
IEnumerable<IndividualBase> nonDominatedFront = Aggregator.FastNonDominatedSort().First().Content;
IEnumerable<IndividualBase> firstItemsByMakeSpan = (from i in nonDominatedFront.Cast<TaskSchedulingSolution>()
group i by i.MakeSpan into g
select g.First()).OrderBy(I => I.MakeSpan);
return firstItemsByMakeSpan;
}
public abstract Population_MultiObjective_AG Execute(Population_MultiObjective_AG lastGeneration, Population_MultiObjective_AG generatedChildren);
protected abstract Population_MultiObjective_AG RunGeneration(Population_MultiObjective_AG currentGeneration);
protected abstract Population_MultiObjective_AG RunGeneration(Population_MultiObjective_AG currentGeneration);
protected override Population_MultiObjective_AG RunGeneration(Population_MultiObjective_AG currentGeneration)
{
Population_MultiObjective_AG everyone = new Population_MultiObjective_AG(Problem, InitialPopulationSize + ArchivePopulationSize);
everyone.AddPopulation(currentGeneration);
everyone.AddPopulation(Archive);
#region Fitness Assignment
// The Strength of an Individual is the Count of those he Dominates
foreach (IndividualBase individual in everyone.Content)
{
individual.Strength = everyone.Content.Where(I => !I.Equals(individual)).Count(I => individual.Dominates(I));
}
// The RawFitness of an Individual is the sum of the strengths of all those who Dominate him
foreach (IndividualBase individual in everyone.Content)
{
individual.RawFitness = everyone.Content.Where(I => I.Dominates(individual)).Sum(D => D.Strength);
}
// The True Fitness is Calculated
everyone.CalculateSortedDistances();
int k = (int)Math.Sqrt(InitialPopulationSize + ArchivePopulationSize);
foreach (IndividualBase i in everyone.Content)
{
double distanceSought = i.DistancesToOtherIndividuals[k].Item2;
double density = (1 / (distanceSought + 2));
i.FinalFitness = i.RawFitness + density;
}
#endregion
#region Environment Selection
Population_MultiObjective_AG nextArchive = new Population_MultiObjective_AG(Problem, ArchivePopulationSize);
List <IndividualBase> excludeList = new List <IndividualBase>();
//insere em nextArchive todos os elementos da populacao total com finalFitness < 1
//poderia ser tambem rawFitness = 0, mas no artigo especifica o dito acima, podemos seguir
//aproveitar pra remover do total os elementos inseridos, vai facilitar no proximo passo..
IEnumerable <IndividualBase> nonDominatedSolutions = everyone.Content.Where(I => I.FinalFitness < 1);
nextArchive.AddRange(nonDominatedSolutions);
everyone.Content.RemoveAll(I => nonDominatedSolutions.Contains(I));
if (nextArchive.IndividualCount < ArchivePopulationSize)
{
everyone.Content.Sort(finalFitnessComparison);
//TODO: Confirmar se é melhor maior ou menor, esse reverse faz pegar os menores
everyone.Content.Reverse();
IEnumerable <IndividualBase> complement = everyone.Content.Take(ArchivePopulationSize - nextArchive.IndividualCount);
nextArchive.AddRange(complement);
}
else if (nextArchive.IndividualCount > ArchivePopulationSize)
{
//trunca a população pela distancia
nextArchive.Truncate(ArchivePopulationSize);
}
//acho que não vai precisar do antigo mais.. conferir !
this.Archive = nextArchive;
#endregion
#region Mating Selection
//Seleção dos Pais da proxima populacao, leva em conta apenas archive
Population_MultiObjective_AG matingPool = Procreate(nextArchive);
#endregion
return(matingPool);
}
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 List<Population_MultiObjective_AG> FastNonDominatedSort()
{
List<Population_MultiObjective_AG> Fronts = new List<Population_MultiObjective_AG>();
Population_MultiObjective_AG NonDominatedFront = new Population_MultiObjective_AG(Problem);
foreach (IndividualBase individual in Content) {
individual.DominatesList = new List<IndividualBase>();
individual.DominatedByCount = 0;
foreach (IndividualBase another in Content.Where(I => !I.Equals(individual))) {
if (individual.Dominates(another)) {
individual.DominatesList.Add(another); // Lista dos elementos que sao dominados
}
else if (another.Dominates(individual)) {
individual.DominatedByCount++; // O atributo np nao foi implementado ainda, se refere a qnt de elementos q o dominam
}
}
if (individual.DominatedByCount == 0) {
individual.NonDominationRank = 1;
NonDominatedFront.AddIndividual(individual);
}
}
int NonDominationRank = 2;
Population_MultiObjective_AG CurrentFront = NonDominatedFront;
Population_MultiObjective_AG FrontQ = NonDominatedFront;
do {
Fronts.Add(FrontQ);
FrontQ = new Population_MultiObjective_AG(Problem);
foreach (IndividualBase individual in CurrentFront.Content) {
foreach (IndividualBase dominatedIndividual in individual.DominatesList) {
dominatedIndividual.DominatedByCount--;
if (dominatedIndividual.DominatedByCount == 0) {
dominatedIndividual.NonDominationRank = NonDominationRank;
FrontQ.AddIndividual(dominatedIndividual);
}
}
}
CurrentFront = FrontQ;
NonDominationRank++;
} while (FrontQ.IndividualCount != 0);
return Fronts;
}
