/// <summary>
/// Runs the NSGA-II algorithm.
/// </summary>
/// <returns>a <code>SolutionSet</code> that is a set of non dominated solutions as a result of the algorithm execution</returns>
public override SolutionSet Execute()
{
// !!!! NEEDED PARAMETES !!! start
//J* Parameters
int populationSize = -1; // J* store population size
int maxEvaluations = -1; // J* store number of max Evaluations
int evaluations; // J* number of current evaluations
// J* Objects needed to illustrate the use of quality indicators inside the algorithms
QualityIndicator indicators = null; // QualityIndicator object
int requiredEvaluations; // Use in the example of use of the
// indicators object (see below)
// J* populations needed to implement NSGA-II
SolutionSet population; //J* Current population
SolutionSet offspringPopulation; //J* offspring population
SolutionSet union; //J* population resultant from current and offpring population
//J* Genetic Operators
Operator mutationOperator;
Operator crossoverOperator;
Operator selectionOperator;
//J* Used to evaluate crowding distance
Distance distance = new Distance();
//J* !!!! NEEDED PARAMETES !!! end
//J* !!! INITIALIZING PARAMETERS - start !!!
//Read the parameters
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "maxEvaluations", ref maxEvaluations); //J* required
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "populationSize", ref populationSize); //J* required
JMetalCSharp.Utils.Utils.GetIndicatorsFromParameters(this.InputParameters, "indicators", ref indicators); //J* optional
//Initialize the variables
population = new SolutionSet(populationSize);
evaluations = 0;
requiredEvaluations = 0;
//Read the operators
mutationOperator = Operators["mutation"];
crossoverOperator = Operators["crossover"];
selectionOperator = Operators["selection"];
//J* !!! INITIALIZING PARAMETERS - end !!!
//J* !!! Creating first population !!!
JMetalRandom.SetRandom(comp.MyRand);
comp.LogAddMessage("Random seed = " + comp.Seed);
// Create the initial solutionSet
Solution newSolution;
for (int i = 0; i < populationSize; i++)
{
newSolution = new Solution(Problem);
Problem.Evaluate(newSolution);
Problem.EvaluateConstraints(newSolution);
evaluations++;
population.Add(newSolution);
}
// Generations
while (evaluations < maxEvaluations)
{
// Create the offSpring solutionSet
offspringPopulation = new SolutionSet(populationSize);
Solution[] parents = new Solution[2];
for (int i = 0; i < (populationSize / 2); i++)
{
if (evaluations < maxEvaluations)
{
//obtain parents
parents[0] = (Solution)selectionOperator.Execute(population);
parents[1] = (Solution)selectionOperator.Execute(population);
Solution[] offSpring = (Solution[])crossoverOperator.Execute(parents);
mutationOperator.Execute(offSpring[0]);
mutationOperator.Execute(offSpring[1]);
Problem.Evaluate(offSpring[0]);
Problem.EvaluateConstraints(offSpring[0]);
Problem.Evaluate(offSpring[1]);
Problem.EvaluateConstraints(offSpring[1]);
offspringPopulation.Add(offSpring[0]);
offspringPopulation.Add(offSpring[1]);
evaluations += 2;
}
}
// Create the solutionSet union of solutionSet and offSpring
union = ((SolutionSet)population).Union(offspringPopulation);
// Ranking the union
Ranking ranking = new Ranking(union);
int remain = populationSize;
int index = 0;
SolutionSet front = null;
population.Clear();
// Obtain the next front
front = ranking.GetSubfront(index);
while ((remain > 0) && (remain >= front.Size()))
{
//Assign crowding distance to individuals
distance.CrowdingDistanceAssignment(front, Problem.NumberOfObjectives);
//Add the individuals of this front
for (int k = 0; k < front.Size(); k++)
{
population.Add(front.Get(k));
}
//Decrement remain
remain = remain - front.Size();
//Obtain the next front
index++;
if (remain > 0)
{
front = ranking.GetSubfront(index);
}
}
// Remain is less than front(index).size, insert only the best one
if (remain > 0)
{ // front contains individuals to insert
distance.CrowdingDistanceAssignment(front, Problem.NumberOfObjectives);
front.Sort(new CrowdingComparator());
for (int k = 0; k < remain; k++)
{
population.Add(front.Get(k));
}
remain = 0;
}
// This piece of code shows how to use the indicator object into the code
// of NSGA-II. In particular, it finds the number of evaluations required
// by the algorithm to obtain a Pareto front with a hypervolume higher
// than the hypervolume of the true Pareto front.
if ((indicators != null) && (requiredEvaluations == 0))
{
double HV = indicators.GetHypervolume(population);
if (HV >= (0.98 * indicators.TrueParetoFrontHypervolume))
{
requiredEvaluations = evaluations;
}
}
}
// Return as output parameter the required evaluations
SetOutputParameter("evaluations", requiredEvaluations);
comp.LogAddMessage("Evaluations = " + evaluations);
// Return the first non-dominated front
Ranking rank = new Ranking(population);
Result = rank.GetSubfront(0);
return(Result);
}
public SolutionSet Mix()
{
QualityIndicator indicators = new QualityIndicator(problem, fileRead.Qi); // QualityIndicator object
int requiredEvaluations = 0; // Use in the example of use of the
// indicators object (see below)
evaluations = 0;
iteration = 0;
populationSize = int.Parse(fileRead.Ps);
iterationsNumber = int.Parse(fileRead.Itn);
dataDirectory = "Data/Parameters/Weight";
Logger.Log.Info("POPSIZE: " + populationSize);
Console.WriteLine("POPSIZE: " + populationSize);
population = new SolutionSet(populationSize);
indArray = new Solution[problem.NumberOfObjectives];
t = int.Parse(fileRead.T);
nr = int.Parse(fileRead.Nr);
delta = double.Parse(fileRead.Delta);
gamma = double.Parse(fileRead.Gamma);
neighborhood = new int[populationSize][];
for (int i = 0; i < populationSize; i++)
{
neighborhood[i] = new int[t];
}
z = new double[problem.NumberOfObjectives];
//znad = new double[Problem.NumberOfObjectives];
lambda = new double[populationSize][];
for (int i = 0; i < populationSize; i++)
{
lambda[i] = new double[problem.NumberOfObjectives];
}
/*string dir = "Result/" + fileRead.Al + "_" + fileRead.Co + "_" + fileRead.Co2 + "/" + fileRead.Pb + "_" + fileRead.St + "/Record/SBX(" + double.Parse(fileRead.Ra).ToString("#0.00") + ")+ACOR(" + (1-double.Parse(fileRead.Ra)).ToString("#0.00") + ")";
* if (Directory.Exists(dir))
* {
* Console.WriteLine("The directory {0} already exists.", dir);
* }
* else
* {
* Directory.CreateDirectory(dir);
* Console.WriteLine("The directory {0} was created.", dir);
* }*/
//Step 1. Initialization
//Step 1.1 Compute euclidean distances between weight vectors and find T
InitUniformWeight();
InitNeighborhood();
//Step 1.2 Initialize population
InitPoputalion();
//Step 1.3 Initizlize z
InitIdealPoint();
//Step 2 Update
for (int a = 0; a < iterationsNumber; a++)
{
int[] permutation = new int[populationSize];
JMetalCSharp.Metaheuristics.MOEAD.Utils.RandomPermutation(permutation, populationSize);
Solution[] parents = new Solution[2];
int t = 0;
if (a >= double.Parse(fileRead.Ra) * iterationsNumber)
{
for (int i = 0; i < populationSize; i++)
{
int n = permutation[i];
// or int n = i;
int type;
double rnd = JMetalRandom.NextDouble();
// STEP 2.1. ACOR selection based on probability
if (rnd < gamma) // if (rnd < realb)
{
type = 1; // minmum
//parents[0] = population.Get(ACOrSelection2(n, type, pro_T));
}
else
{
type = 2; // whole neighborhood probability
//parents[0] = population.Get(ACOrSelection2(n, type, pro_A));
}
GetStdDev(neighborhood);
//GetStdDev1(neighborhood, type);
//List<int> p = new List<int>();
//MatingSelection(p, n, 1, type);
// STEP 2.2. Reproduction
Solution child;
parents[0] = population.Get(ACOrSelection(n, type));
parents[1] = population.Get(n);
//parents[0] = population.Get(p[0]);
// Apply ACOR crossover
child = (Solution)crossover2.Execute(parents);
child.NumberofReplace = t;
// // Apply mutation
mutation.Execute(child);
// Evaluation
problem.Evaluate(child);
evaluations++;
// STEP 2.3. Repair. Not necessary
// STEP 2.4. Update z_
UpdateReference(child);
// STEP 2.5. Update of solutions
t = UpdateProblemWithReplace(child, n, 1);
}
}
else
{
// Create the offSpring solutionSet
SolutionSet offspringPopulation = new SolutionSet(populationSize);
for (int i = 0; i < (populationSize / 2); i++)
{
int n = permutation[i]; // or int n = i;
int type;
double rnd = JMetalRandom.NextDouble();
// STEP 2.1. Mating selection based on probability
if (rnd < delta) // if (rnd < realb)
{
type = 1; // neighborhood
}
else
{
type = 2; // whole population
}
List <int> p = new List <int>();
MatingSelection(p, n, 2, type);
parents[0] = population.Get(p[0]);
parents[1] = population.Get(p[1]);
//obtain parents
Solution[] offSpring = (Solution[])crossover1.Execute(parents);
//Solution child;
mutation.Execute(offSpring[0]);
mutation.Execute(offSpring[1]);
/*if(rnd < 0.5)
* {
* child = offSpring[0];
* }
* else
* {
* child = offSpring[1];
* }*/
problem.Evaluate(offSpring[0]);
problem.Evaluate(offSpring[1]);
problem.EvaluateConstraints(offSpring[0]);
problem.EvaluateConstraints(offSpring[1]);
offspringPopulation.Add(offSpring[0]);
offspringPopulation.Add(offSpring[1]);
evaluations += 2;
// STEP 2.3. Repair. Not necessary
// STEP 2.4. Update z_
UpdateReference(offSpring[0]);
UpdateReference(offSpring[1]);
// STEP 2.5. Update of solutions
UpdateProblem(offSpring[0], n, type);
UpdateProblem(offSpring[1], n, type);
}
//for (int i = 0; i < populationSize; i++)
//{
// int n = permutation[i]; // or int n = i;
// int type;
// double rnd = JMetalRandom.NextDouble();
// // STEP 2.1. Mating selection based on probability
// if (rnd < delta) // if (rnd < realb)
// {
// type = 1; // neighborhood
// }
// else
// {
// type = 2; // whole population
// }
// List<int> p = new List<int>();
// MatingSelection(p, n, 2, type);
// // STEP 2.2. Reproduction
// Solution child;
// Solution[] parent = new Solution[3];
// parent[0] = population.Get(p[0]);
// parent[1] = population.Get(p[1]);
// parent[2] = population.Get(n);
// // Apply DE crossover
// child = (Solution)crossover1.Execute(new object[] { population.Get(n), parent });
// // Apply mutation
// mutation.Execute(child);
// // Evaluation
// problem.Evaluate(child);
// evaluations++;
// // STEP 2.3. Repair. Not necessary
// // STEP 2.4. Update z_
// UpdateReference(child);
// // STEP 2.5. Update of solutions
// UpdateProblem(child, n, type);
//}
}
/*string filevar = dir + "/VAR" + iteration;
* string filefun = dir + "/FUN" + iteration;
* population.PrintVariablesToFile(filevar);
* population.PrintObjectivesToFile(filefun);*/
iteration++;
if ((indicators != null) && (requiredEvaluations == 0))
{
double HV = indicators.GetHypervolume(population);
if (HV >= (0.98 * indicators.TrueParetoFrontHypervolume))
{
requiredEvaluations = evaluations;
}
}
}
Logger.Log.Info("ITERATION: " + iteration);
Console.WriteLine("ITERATION: " + iteration);
SolutionSet Result = population;
//return population;
// Return the first non-dominated front
Ranking rank = new Ranking(population);
//SolutionSet Result = rank.GetSubfront(0);
return(Result);
}
public void MyAlgorithm()
{
Problem problem = null; // The problem to solve
Algorithm algorithm = null; // The algorithm to use
Operator crossover = null; // Crossover operator
Operator mutation = null; // Mutation operator
Operator selection = null; // Selection operator
QualityIndicator indicators; // Object to get quality indicators
FileRead fileRead = new FileRead();
fileRead.fileread();
problem = fileRead.GetProblem();
algorithm = fileRead.GetAlgorithm();
//crossover = fileRead.GetCrossover();
mutation = fileRead.GetMutation();
selection = fileRead.GetSelection();
// Quality Indicators Operator
//indicators = new QualityIndicator(problem, "DTLZ1.3D.pf");
indicators = new QualityIndicator(problem, fileRead.Qi);
//indicators = new QualityIndicator(problem, "LZ09_F1.pf");
// Add the operators to the algorithm
algorithm.AddOperator("crossover", crossover);
algorithm.AddOperator("mutation", mutation);
algorithm.AddOperator("selection", selection);
// Add the indicator object to the algorithm
algorithm.SetInputParameter("indicators", indicators);
for (int i = 1; i <= int.Parse(fileRead.Rt); i++)
{
// Logger object and file to store log messages
var logger = Logger.Log;
var appenders = logger.Logger.Repository.GetAppenders();
var fileAppender = appenders[0] as log4net.Appender.FileAppender;
fileAppender.File = fileRead.DirPath + "/" + fileRead.Al + i + ".log";
fileAppender.ActivateOptions();
string filevar = fileRead.DirPath + "/VAR" + i;
string filefun = fileRead.DirPath + "/FUN" + i;
FileStream file = new FileStream(fileRead.DirPath + "/" + fileRead.Al + "new" + i + ".txt", FileMode.OpenOrCreate, FileAccess.ReadWrite);
StreamWriter newlogger = new StreamWriter(file);
// Execute the Algorithm
long initTime = Environment.TickCount;
SolutionSet population = algorithm.Execute();
long estimatedTime = Environment.TickCount - initTime;
// Result messages
logger.Info("Total execution time: " + estimatedTime + "ms");
logger.Info("Variables values have been writen to file " + filevar);
newlogger.WriteLine("Total execution time: " + estimatedTime + "ms" + "\n");
newlogger.WriteLine("Variables values have been writen to file " + filevar + "\n");
population.PrintVariablesToFile(filevar);
logger.Info("Objectives values have been writen to file " + filefun);
population.PrintObjectivesToFile(filefun);
Console.WriteLine("Time: " + estimatedTime);
newlogger.WriteLine("Time: " + estimatedTime + "\n");
Console.ReadLine();
if (indicators != null)
{
logger.Info("Quality indicators");
logger.Info("Hypervolume: " + indicators.GetHypervolume(population));
logger.Info("GD : " + indicators.GetGD(population));
logger.Info("IGD : " + indicators.GetIGD(population));
logger.Info("Spread : " + indicators.GetSpread(population));
logger.Info("Epsilon : " + indicators.GetEpsilon(population));
newlogger.WriteLine("Quality indicators");
newlogger.WriteLine("Hypervolume: " + indicators.GetHypervolume(population).ToString("F18") + "\n");
newlogger.WriteLine("GD : " + indicators.GetGD(population).ToString("F18") + "\n");
newlogger.WriteLine("IGD : " + indicators.GetIGD(population).ToString("F18") + "\n");
newlogger.WriteLine("Spread : " + indicators.GetSpread(population).ToString("F18") + "\n");
newlogger.WriteLine("Epsilon : " + indicators.GetEpsilon(population).ToString("F18") + "\n");
int evaluations = (int)algorithm.GetOutputParameter("evaluations");
logger.Info("Speed : " + evaluations + " evaluations");
newlogger.WriteLine("Speed : " + evaluations + " evaluations" + "\n");
}
newlogger.Close();
file.Close();
algorithm.AddOperator("mutation", mutation);
}
}
private void Button_Click_2(object sender, RoutedEventArgs e)
{
FileRead read = new FileRead();
read.fileread();
Problem problem = read.GetProblem();
Algorithm algorithm = read.GetAlgorithm();
QualityIndicator indicators = new QualityIndicator(problem, read.Qi);
NewAlgorithmTest test = new NewAlgorithmTest();
for (int i = 1; i <= int.Parse(read.Rt); i++)
{
// Logger object and file to store log messages
var logger = Logger.Log;
var appenders = logger.Logger.Repository.GetAppenders();
var fileAppender = appenders[0] as log4net.Appender.FileAppender;
fileAppender.File = read.DirPath + "/" + read.Al + i + ".log";
fileAppender.ActivateOptions();
string filevar = read.DirPath + "/VAR" + i;
string filefun = read.DirPath + "/FUN" + i;
FileStream file = new FileStream(read.DirPath + "/MOEADnew" + i + ".txt", FileMode.OpenOrCreate, FileAccess.ReadWrite);
StreamWriter newlogger = new StreamWriter(file);
// Execute the Algorithm
long initTime = Environment.TickCount;
SolutionSet population = test.Mix();
long estimatedTime = Environment.TickCount - initTime;
// Result messages
logger.Info("Total execution time: " + estimatedTime + "ms");
logger.Info("Variables values have been writen to file " + filevar);
newlogger.WriteLine("Total execution time: " + estimatedTime + "ms" + "\n");
newlogger.WriteLine("Variables values have been writen to file " + filevar + "\n");
population.PrintVariablesToFile(filevar);
logger.Info("Objectives values have been writen to file " + filefun);
population.PrintObjectivesToFile(filefun);
Console.WriteLine("Time: " + estimatedTime);
newlogger.WriteLine("Time: " + estimatedTime + "\n");
Console.ReadLine();
if (indicators != null)
{
logger.Info("Quality indicators");
logger.Info("Hypervolume: " + indicators.GetHypervolume(population));
logger.Info("GD : " + indicators.GetGD(population));
logger.Info("IGD : " + indicators.GetIGD(population));
logger.Info("Spread : " + indicators.GetSpread(population));
logger.Info("Epsilon : " + indicators.GetEpsilon(population));
newlogger.WriteLine("Quality indicators");
newlogger.WriteLine("Hypervolume: " + indicators.GetHypervolume(population).ToString("F18") + "\n");
newlogger.WriteLine("GD : " + indicators.GetGD(population).ToString("F18") + "\n");
newlogger.WriteLine("IGD : " + indicators.GetIGD(population).ToString("F18") + "\n");
newlogger.WriteLine("Spread : " + indicators.GetSpread(population).ToString("F18") + "\n");
newlogger.WriteLine("Epsilon : " + indicators.GetEpsilon(population).ToString("F18") + "\n");
int evaluations = test.GetEvaluations();
logger.Info("Speed : " + evaluations + " evaluations");
newlogger.WriteLine("Speed : " + evaluations + " evaluations" + "\n");
}
newlogger.Close();
file.Close();
}
}
/// <summary>
/// Runs the NSGA-II algorithm.
/// </summary>
/// <returns>a <code>SolutionSet</code> that is a set of non dominated solutions as a result of the algorithm execution</returns>
public override SolutionSet Execute()
{
int populationSize = -1;
int maxEvaluations = -1;
int evaluations;
QualityIndicator indicators = null; // QualityIndicator object
int requiredEvaluations; // Use in the example of use of the
// indicators object (see below)
SolutionSet population;
SolutionSet offspringPopulation;
SolutionSet union;
Operator mutationOperator;
Operator crossoverOperator;
Operator selectionOperator;
Distance distance = new Distance();
//Read the parameters
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "maxEvaluations", ref maxEvaluations);
JMetalCSharp.Utils.Utils.GetIntValueFromParameter(this.InputParameters, "populationSize", ref populationSize);
JMetalCSharp.Utils.Utils.GetIndicatorsFromParameters(this.InputParameters, "indicators", ref indicators);
//Initialize the variables
population = new SolutionSet(populationSize);
evaluations = 0;
requiredEvaluations = 0;
//Read the operators
mutationOperator = Operators["mutation"];
crossoverOperator = Operators["crossover"];
selectionOperator = Operators["selection"];
var plotCounter = 0;
var plotModulo = 4;
Random random = new Random(2);
JMetalRandom.SetRandom(random);
// Create the initial solutionSet
IntergenSolution newSolution;
for (int i = 0; i < populationSize; i++)
{
//var test = (IntergenProblem) Problem;
newSolution = new IntergenSolution((IntergenProblem)Problem);
Problem.Evaluate(newSolution);
Problem.EvaluateConstraints(newSolution);
evaluations++;
population.Add(newSolution);
}
// Generations
while (evaluations < maxEvaluations)
{
// Create the offSpring solutionSet
offspringPopulation = new SolutionSet(populationSize);
IntergenSolution[] parents = new IntergenSolution[2];
for (int i = 0; i < (populationSize / 2); i++)
{
if (evaluations < maxEvaluations)
{
//obtain parents
parents[0] = (IntergenSolution)selectionOperator.Execute(population);
parents[1] = (IntergenSolution)selectionOperator.Execute(population);
IntergenSolution[] offSpring = (IntergenSolution[])crossoverOperator.Execute(parents);
mutationOperator.Execute(offSpring[0]);
mutationOperator.Execute(offSpring[1]);
Problem.Evaluate(offSpring[0]);
Problem.EvaluateConstraints(offSpring[0]);
Problem.Evaluate(offSpring[1]);
Problem.EvaluateConstraints(offSpring[1]);
offspringPopulation.Add(offSpring[0]);
offspringPopulation.Add(offSpring[1]);
evaluations += 2;
}
}
// Create the solutionSet union of solutionSet and offSpring
union = ((SolutionSet)population).Union(offspringPopulation);
// Ranking the union
Ranking ranking = new Ranking(union);
int remain = populationSize;
int index = 0;
SolutionSet front = null;
population.Clear();
// Obtain the next front
front = ranking.GetSubfront(index);
while ((remain > 0) && (remain >= front.Size()))
{
//Assign crowding distance to individuals
distance.CrowdingDistanceAssignment(front, Problem.NumberOfObjectives);
//Add the individuals of this front
for (int k = 0; k < front.Size(); k++)
{
population.Add(front.Get(k));
}
//Decrement remain
remain = remain - front.Size();
//Obtain the next front
index++;
if (remain > 0)
{
front = ranking.GetSubfront(index);
}
}
// Remain is less than front(index).size, insert only the best one
if (remain > 0)
{ // front contains individuals to insert
distance.CrowdingDistanceAssignment(front, Problem.NumberOfObjectives);
front.Sort(new CrowdingComparator());
for (int k = 0; k < remain; k++)
{
population.Add(front.Get(k));
}
remain = 0;
}
// This piece of code shows how to use the indicator object into the code
// of NSGA-II. In particular, it finds the number of evaluations required
// by the algorithm to obtain a Pareto front with a hypervolume higher
// than the hypervolume of the true Pareto front.
if ((indicators != null) && (requiredEvaluations == 0))
{
double HV = indicators.GetHypervolume(population);
if (HV >= (0.98 * indicators.TrueParetoFrontHypervolume))
{
requiredEvaluations = evaluations;
}
}
var sol0 = front.Best(new CrowdingComparator());
//if (plotCounter%plotModulo == 0)
SolutionPlotter.Plot(sol0);
ProgressReporter.ReportSolution(evaluations, sol0, _worker);
}
// Return as output parameter the required evaluations
SetOutputParameter("evaluations", requiredEvaluations);
// Return the first non-dominated front
Ranking rank = new Ranking(population);
Result = rank.GetSubfront(0);
return(Result);
}