static MediaFactory CreateDiskFactory(bool changable = false)
{
MusicTrack[] tracks = new MusicTrack[]
{
new MusicTrack("Music Track 1", "Noone", new byte[1]),
new MusicTrack("Music Track 2", "Someone", new byte[1]),
new MusicTrack("Music Track 3", "Noone", new byte[1])
};
Photo[] photoes = new Photo[]
{
new Photo("Photo 1", "Some author", new System.Drawing.Bitmap(1, 1)),
new Photo("Photo 2", "No author", new System.Drawing.Bitmap(1, 1)),
new PhotoReference("Photo 3", "Some author", new Uri("file:///nothing.bmp"))
};
MediaFactory factory;
if (changable)
{
factory = new SelectionFactory(tracks, photoes);
}
else
{
factory = new DiskFactory(tracks, photoes);
}
return(factory);
}
public static int RunGAMapFinder(GAFinderOption option)
{
var map = _loadMap(option.MapFileName, option.Diagonal);
if (map == null)
{
return(1);
}
var heuristic = (new HeuristicFactory()).GetImplementation(option.Heuristic);
var mutate = new MutateFactory().GetImplementation(option.Mutate);
var crossover = new CrossoverFactory().GetImplementation(option.Crossover);
var selection = new SelectionFactory().GetImplementation(option.Selection);
var fitness = new FitnessFactory().GetImplementation(option.Fitness);
fitness.Heuristic = heuristic;
var finder = FinderFactory.GetGAImplementation(
crossover,
mutate,
fitness,
selection,
option.Population,
option.GenerationLimit,
option.BestToPick
);
return(_runFinder(finder, heuristic, map, option.Window, option.NoShowSteps, option.BlockSize, option.UISleep));
}
/// <summary>
/// Constructor
/// </summary>
/// <param name="CR"></param>
/// <param name="F"></param>
public DifferentialEvolutionOffspring(double CR, double F)
{
Dictionary <string, object> parameters = null;
this.CR = CR;
this.F = F;
try
{
// Crossover operator
parameters = new Dictionary <string, object>();
parameters.Add("CR", CR);
parameters.Add("F", F);
crossover = new DifferentialEvolutionCrossover(parameters);
// Selecion operator
parameters = null;
selection = SelectionFactory.GetSelectionOperator("DifferentialEvolutionSelection", parameters);
}
catch (Exception ex)
{
Logger.Log.Error("Exception in " + this.GetType().FullName + ".Execute()", ex);
Console.Error.WriteLine("Error in: " + this.GetType().FullName + "\r\n" + ex.StackTrace);
}
Id = "DE";
}
public PolynomialMutationOffspring(double mutationProbability, double distributionIndexForMutation)
{
Dictionary <string, object> parameters; // Operator parameters
parameters = new Dictionary <string, object>();
parameters.Add("probability", this.mutationProbability = mutationProbability);
parameters.Add("distributionIndex", this.distributionIndex = distributionIndexForMutation);
mutation = MutationFactory.GetMutationOperator("PolynomialMutation", parameters);
selection = SelectionFactory.GetSelectionOperator("BinaryTournament", null);
Id = "PolynomialMutation";
}
private void Register()
{
SelectionFactory.Register("roulette", () => new RouletteSelection());
CrossoverOperatorFactory.Register("OX", () => new OrderCrossover());
CrossoverOperatorFactory.Register("CX", () => new CycleCrossover());
CrossoverOperatorFactory.Register("PMX", () => new PartiallyMatchedCrossover());
CrossoverOperatorFactory.Register("UOX", () => new UniformBasedOrderCrossover());
MutationOperatorFactory.Register("swap", () => new SwapOperator());
MutationOperatorFactory.Register("insertion", () => new InsertionOperator());
MutationOperatorFactory.Register("inversion", () => new InversionOperator());
MutationOperatorFactory.Register("displacement", () => new DisplacementOperator());
HeuristicsFactory.Register("SA", () => new SimulatedAnnealing());
}
public void RegisterAlgorithm(string problemName, ParametersWebsite webParameters)
{
string filePath = HttpContext.Current.Server.MapPath("~/Data/Homberger/" + problemName.ToUpper() + @".txt");
VrptwProblem problem = new VrptwProblemReader().ReadFromFile(filePath);
string connecionId = Context.ConnectionId;
Register();
MemeticAlgorithm ma;
if (algorithms.ContainsKey(connecionId))
{
ma = algorithms[connecionId];
ma.Stop();
}
else
{
ma = new MemeticAlgorithm();
}
Parameters parameters = new Parameters
{
Heuristics = HeuristicsFactory.Create(webParameters.Heuristics),
PopulationSize = webParameters.PopulationSize,
Selection = SelectionFactory.Create(webParameters.Selection),
CrossoverOperators = new List <CrossoverOperator>(),
MutationOperators = new List <MutationOperator>(),
CrossoverProbability = webParameters.CrossoverProbability,
MutationProbability = webParameters.MutationProbability,
Fitness = new FitnessFunction(webParameters.FitnessRoutes, webParameters.FitnesDistance),
EliteChildrenCount = webParameters.EliteChildrenCount,
GeneCount = problem.Customers.Count,
FitnessStrategy = FitnessStrategy.MINIMIZE,
ConvergenceLimit = webParameters.ConvergenceLimit,
PreservedChromosomesNumber = webParameters.PreservedChromosomeNumber,
MaxIterations = webParameters.Iterations,
ChromosomeFactory = new SolutionFactory(),
HeuristicsParameters = webParameters.HeuristicsParameters
};
parameters.CrossoverOperators.Add(CrossoverOperatorFactory.Create(webParameters.Crossover));
parameters.MutationOperators.Add(MutationOperatorFactory.Create(webParameters.Mutation));
algorithms.TryAdd(connecionId, ma);
var result = ma.Run(problem, parameters, UpdateSolution, connecionId);
var solution = result.Result as Solution;
string outputPath = HttpContext.Current.Server.MapPath("~/Results/Memetic");
string outputFilePath = outputPath + @"/" + problem.Name + ".txt";
File.WriteAllText(outputFilePath, solution.ToFileText());
}
public Operator GetSelection()
{
Dictionary <string, object> parameters = new Dictionary <string, object>();
switch (s)
{
case "BinaryTournament2":
parameters = null;
selection = SelectionFactory.GetSelectionOperator("BinaryTournament2", parameters);
break;
default:
break;
}
return(selection);
}
public GeneticAlgorithm(
int mutatorValue,
int generationCount,
int populationSize,
CrossoverType crossoverType,
SelectionType selectionType,
List <City> cities)
{
this.mutationProbability = 1.0 / (TourManager.Cities.Count * mutatorValue);
this.generationCount = generationCount;
this.populationSize = populationSize;
this.crossover = CrossoverFactory.Get(crossoverType);
this.selection = SelectionFactory.Get(selectionType);
TourManager.Cities = cities;
this.distances = Distance.Calculate(TourManager.Cities);
}
public SBXCrossoverOffspring(double crossoverProbability, double distributionIndexForCrossover)
{
Dictionary <string, object> parameters;
this.crossoverProbability = crossoverProbability;
this.distributionIndexForCrossover = distributionIndexForCrossover;
// Crossover operator
parameters = new Dictionary <string, object>();
parameters.Add("probability", crossoverProbability);
parameters.Add("distributionIndex", distributionIndexForCrossover);
crossover = CrossoverFactory.GetCrossoverOperator("SBXCrossover", parameters);
selection = SelectionFactory.GetSelectionOperator("BinaryTournament", null);
Id = "SBXCrossover";
}
/// <summary>
/// Usage:
/// - NSGAIImTSP
/// </summary>
/// <param name="args">Command line arguments.</param>
public static void Main(string[] args)
{
Problem problem; // The problem to solve
Algorithm algorithm; // The algorithm to use
Operator crossover; // Crossover operator
Operator mutation; // Mutation operator
Operator selection; // Selection operator
Dictionary <string, object> parameters; // Operator parameters
QualityIndicator indicators; // Object to get quality indicators
// 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 = "NSGAIImTSP.log";
fileAppender.ActivateOptions();
indicators = null;
problem = new MTSP("Permutation", "kroA150.tsp", "kroB150.tsp");
algorithm = new JMetalCSharp.Metaheuristics.NSGAII.NSGAII(problem);
//algorithm = new ssNSGAII(problem);
// Algorithm parameters
algorithm.SetInputParameter("populationSize", 100);
algorithm.SetInputParameter("maxEvaluations", 10000000);
/* Crossver operator */
parameters = new Dictionary <string, object>();
parameters.Add("probability", 0.95);
//crossover = CrossoverFactory.getCrossoverOperator("TwoPointsCrossover", parameters);
crossover = CrossoverFactory.GetCrossoverOperator("PMXCrossover", parameters);
/* Mutation operator */
parameters = new Dictionary <string, object>();
parameters.Add("probability", 0.2);
mutation = MutationFactory.GetMutationOperator("SwapMutation", parameters);
/* Selection Operator */
parameters = null;
selection = SelectionFactory.GetSelectionOperator("BinaryTournament", parameters);
// 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);
// 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 VAR");
population.PrintVariablesToFile("VAR");
logger.Info("Objectives values have been writen to file FUN");
population.PrintObjectivesToFile("FUN");
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));
int evaluations = (int)algorithm.GetOutputParameter("evaluations");
logger.Info("Speed : " + evaluations + " evaluations");
}
}
/// <summary>
/// Usage: three options
/// - NSGAII
/// - NSGAII problemName
/// - NSGAII problemName paretoFrontFile
/// </summary>
/// <param name="args"></param>
public NSGAIIRunner(string[] args, Problem p, string Path, MOO comp)
{
Problem problem = p; // The problem to solve
Algorithm algorithm; // The algorithm to use
Operator crossover; // Crossover operator
Operator mutation; // Mutation operator
Operator selection; // Selection operator
this.comp = comp;
Dictionary <string, object> parameters; // Operator parameters
QualityIndicator indicators; // Object to get quality indicators
// 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 = "NSGAII.log";
//fileAppender.ActivateOptions();
indicators = null;
//if (args.Length == 1)
//{
// object[] param = { "Real" };
// problem = ProblemFactory.GetProblem(args[0], param);
//}
//else if (args.Length == 2)
//{
// object[] param = { "Real" };
// problem = ProblemFactory.GetProblem(args[0], param);
// indicators = new QualityIndicator(problem, args[1]);
//}
//else
//{ // Default problem
// //problem = ;
// //problem = new Kursawe("BinaryReal", 3);
// //problem = new Water("Real");
// //problem = new ZDT3("ArrayReal", 30);
// //problem = new ConstrEx("Real");
// //problem = new DTLZ1("Real");
// //problem = new OKA2("Real") ;
//}
algorithm = new NSGAII(problem, comp);
//algorithm = new ssNSGAII(problem);
// Algorithm parameters
algorithm.SetInputParameter("populationSize", comp.popSize);
algorithm.SetInputParameter("maxEvaluations", comp.maxEvals);
comp.LogAddMessage("Population Size = " + comp.popSize);
comp.LogAddMessage("Max Evaluations = " + comp.maxEvals);
// Mutation and Crossover for Real codification
parameters = new Dictionary <string, object>();
parameters.Add("probability", 0.9);
parameters.Add("distributionIndex", 20.0);
crossover = CrossoverFactory.GetCrossoverOperator("SBXCrossover", parameters);
comp.LogAddMessage("Crossover Type = " + "SBXCrossover");
comp.LogAddMessage("Crossover Probability = " + 0.9);
comp.LogAddMessage("Crossover Distribution Index = " + 20);
parameters = new Dictionary <string, object>();
parameters.Add("probability", 1.0 / problem.NumberOfVariables);
parameters.Add("distributionIndex", 20.0);
mutation = MutationFactory.GetMutationOperator("PolynomialMutation", parameters);
comp.LogAddMessage("Mutation Type = " + "Polynomial Mutation");
comp.LogAddMessage("Mutation Probability = " + (1 / problem.NumberOfVariables));
comp.LogAddMessage("Mutation Distribution Index = " + 20);
// Selection Operator
parameters = null;
selection = SelectionFactory.GetSelectionOperator("BinaryTournament2", parameters);
comp.LogAddMessage("Selection Type = " + "Binary Tournament 2");
// 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);
// Execute the Algorithm
long initTime = Environment.TickCount;
SolutionSet population = algorithm.Execute();
long estimatedTime = Environment.TickCount - initTime;
comp.LogAddMessage("Total Execution Time = " + estimatedTime + "ms");
// Result messages
//logger.Info("Total execution time: " + estimatedTime + "ms");
//logger.Info("Variables values have been writen to file VAR");
//population.PrintVariablesToFile(@"C:\Users\Jonathas\Desktop\text.txt");
population.PrintVariablesToFile(@"" + comp.fileName + "VAR-" + comp.fileName);
//logger.Info("Objectives values have been writen to file FUN");
population.PrintObjectivesToFile(@"" + comp.fileName + "OBJ-" + comp.fileName);
// Saving all solutions to file
//Console.WriteLine("Time: " + estimatedTime);
//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));
int evaluations = (int)algorithm.GetOutputParameter("evaluations");
//logger.Info("Speed : " + evaluations + " evaluations");
}
}
/// <summary>
/// Usage: three options
/// - SPEA2
/// - SPEA2 problemName
/// - SPEA2 problemName ParetoFrontFile
/// </summary>
/// <param name="args">Command line arguments. The first (optional) argument specifies the problem to solve.</param>
public static void Main(string[] args)
{
Problem problem; // The problem to solve
Algorithm algorithm; // The algorithm to use
Operator crossover; // Crossover operator
Operator mutation; // Mutation operator
Operator selection; // Selection operator
QualityIndicator indicators; // Object to get quality indicators
Dictionary <string, object> parameters; // Operator parameters
// 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 = "SPEA2.log";
fileAppender.ActivateOptions();
indicators = null;
if (args.Length == 1)
{
object[] param = { "Real" };
problem = ProblemFactory.GetProblem(args[0], param);
}
else if (args.Length == 2)
{
object[] param = { "Real" };
problem = ProblemFactory.GetProblem(args[0], param);
indicators = new QualityIndicator(problem, args[1]);
}
else
{ // Default problem
problem = new Kursawe("Real", 3);
//problem = new Water("Real");
//problem = new ZDT1("ArrayReal", 1000);
//problem = new ZDT4("BinaryReal");
//problem = new WFG1("Real");
//problem = new DTLZ1("Real");
//problem = new OKA2("Real") ;
}
algorithm = new JMetalCSharp.Metaheuristics.SPEA2.SPEA2(problem);
// Algorithm parameters
algorithm.SetInputParameter("populationSize", 100);
algorithm.SetInputParameter("archiveSize", 100);
algorithm.SetInputParameter("maxEvaluations", 25000);
// Mutation and Crossover for Real codification
parameters = new Dictionary <string, object>();
parameters.Add("probability", 0.9);
parameters.Add("distributionIndex", 20.0);
crossover = CrossoverFactory.GetCrossoverOperator("SBXCrossover", parameters);
parameters = new Dictionary <string, object>();
parameters.Add("probability", 1.0 / problem.NumberOfVariables);
parameters.Add("distributionIndex", 20.0);
mutation = MutationFactory.GetMutationOperator("PolynomialMutation", parameters);
// Selection operator
parameters = null;
selection = SelectionFactory.GetSelectionOperator("BinaryTournament", parameters);
// Add the operators to the algorithm
algorithm.AddOperator("crossover", crossover);
algorithm.AddOperator("mutation", mutation);
algorithm.AddOperator("selection", selection);
// 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("Objectives values have been writen to file FUN");
population.PrintObjectivesToFile("FUN");
logger.Info("Variables values have been writen to file VAR");
population.PrintVariablesToFile("VAR");
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));
}
Console.WriteLine("Total execution time: " + estimatedTime + "ms");
Console.ReadLine();
}
/// <summary>
/// Usage: three options
/// - NSGAIIAdaptive
/// - NSGAIIAdaptive problemName
/// - NSGAIIAdaptive problemName paretoFrontFile
/// </summary>
/// <param name="args">Command line arguments.</param>
public static void Main(string[] args)
{
Problem problem; // The problem to solve
Algorithm algorithm; // The algorithm to use
Operator selection; // Selection operator
Dictionary <string, object> parameters; // Operator parameters
QualityIndicator indicators; // Object to get quality indicators
// 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 = "NSGAIIAdaptive.log";
fileAppender.ActivateOptions();
indicators = null;
if (args.Length == 1)
{
object[] param = { "Real" };
problem = ProblemFactory.GetProblem(args[0], param);
}
else if (args.Length == 2)
{
object[] param = { "Real" };
problem = ProblemFactory.GetProblem(args[0], param);
indicators = new QualityIndicator(problem, args[1]);
}
else
{ // Default problem
problem = new Kursawe("Real", 3);
//problem = new Kursawe("BinaryReal", 3);
//problem = new Water("Real");
//problem = new ZDT1("ArrayReal", 100);
//problem = new ConstrEx("Real");
//problem = new DTLZ1("Real");
//problem = new OKA2("Real") ;
}
problem = new LZ09_F3("Real");
algorithm = new JMetalCSharp.Metaheuristics.NSGAII.NSGAIIAdaptive(problem);
//algorithm = new ssNSGAIIAdaptive(problem);
// Algorithm parameters
algorithm.SetInputParameter("populationSize", 100);
algorithm.SetInputParameter("maxEvaluations", 150000);
// Selection Operator
parameters = null;
selection = SelectionFactory.GetSelectionOperator("BinaryTournament2", parameters);
// Add the operators to the algorithm
algorithm.AddOperator("selection", selection);
// Add the indicator object to the algorithm
algorithm.SetInputParameter("indicators", indicators);
Offspring[] getOffspring = new Offspring[3];
double CR, F;
CR = 1.0;
F = 0.5;
getOffspring[0] = new DifferentialEvolutionOffspring(CR, F);
getOffspring[1] = new SBXCrossoverOffspring(1.0, 20);
//getOffspring[1] = new BLXAlphaCrossoverOffspring(1.0, 0.5);
getOffspring[2] = new PolynomialMutationOffspring(1.0 / problem.NumberOfVariables, 20);
//getOffspring[2] = new NonUniformMutationOffspring(1.0/problem.getNumberOfVariables(), 0.5, 150000);
algorithm.SetInputParameter("offspringsCreators", getOffspring);
// 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 VAR");
population.PrintVariablesToFile("VAR");
logger.Info("Objectives values have been writen to file FUN");
population.PrintObjectivesToFile("FUN");
Console.WriteLine("Time: " + estimatedTime);
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));
}
}
static void Main()
{
Application.EnableVisualStyles();
Application.SetCompatibleTextRenderingDefault(false);
Application.Run(new LoadConfig());
// VariabilityModel model = new VariabilityModel("Model1");
// InfluenceModel infModel = new InfluenceModel(model, new NFProperty("Test"));
// BettyFileParser bfp = new BettyFileParser(model, infModel);
String[] file = System.IO.File.ReadAllLines(@"C:\Users\Tom\Desktop\Masterarbeit\SPLConqueror\SPLConqueror\BettyConqueror\FeatureModel0.afm");
// bfp.readConfiguration(file);
//ILArray<float> A = ILMath.tosingle()
//ILArray<float> A = ILMath.tosingle(ILMath.rand(3, 100));
// RDotNet.NativeLibrary.UnmanagedDll.SetDllDirectory(@"C:\Program Files\R\R-3.2.0\bin\i386\R.dll");
engine = REngine.GetInstance();
NumericVector group1 = engine.CreateNumericVector(new double[] { 30.02, 29.99, 30.11, 29.97, 30.01, 29.99 });
engine.SetSymbol("group1", group1);
// e.Evaluate("hist(group1)");
// e.Evaluate("hist(group1)");
Problem problem;
Operator crossover; // Crossover operator
Operator mutation; // Mutation operator
Operator selection; // Selection operator
var logger = Logger.Log;
// var appenders = logger.Logger.Repository.GetAppenders();
// var fileAppender = appenders[0] as log4net.Appender.FileAppender;
// fileAppender.File = "NSGAII.log";
//.ActivateOptions();
Dictionary <string, object> parameters; // Operator parameters
QualityIndicator indicators; // Object to get quality indicators
problem = new GeneratorProblem("Real", 30);
NSGAII algorithm = new NSGAII(problem);
indicators = null;
algorithm.SetInputParameter("populationSize", 200);
algorithm.SetInputParameter("maxEvaluations", 25000);
// Mutation and Crossover for Real codification
parameters = new Dictionary <string, object>();
parameters.Add("probability", 0.9);
parameters.Add("distributionIndex", 20.0);
crossover = CrossoverFactory.GetCrossoverOperator("SBXCrossover", parameters);
parameters = new Dictionary <string, object>();
parameters.Add("probability", 1.0 / problem.NumberOfVariables);
parameters.Add("distributionIndex", 20.0);
mutation = MutationFactory.GetMutationOperator("PolynomialMutation", parameters);
// Selection Operator
parameters = null;
selection = SelectionFactory.GetSelectionOperator("BinaryTournament2", parameters);
// 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);
// Execute the Algorithm
long initTime = Environment.TickCount;
//SolutionSet population = algorithm.Execute();
long estimatedTime = Environment.TickCount - initTime;
logger.Info("Variables values have been writen to file VAR");
//population.PrintVariablesToFile("VAR");
logger.Info("Objectives values have been writen to file FUN");
// population.PrintObjectivesToFile("FUN");
Console.WriteLine("Time: " + estimatedTime);
Console.ReadLine();
//Application.Run(new Form1(e, group1, new double[] { 30.02, 29.99, 30.11, 29.97, 30.01, 29.99, 1.3, 1.7, 9.4, 11.1 }));
}
/// <summary>
///Usage: three choices
/// - GDE3
/// - GDE3 problemName
/// - GDE3 problemName paretoFrontFile
/// </summary>
/// <param name="args">Command line arguments.</param>
public static void Main(string[] args)
{
Problem problem; // The problem to solve
Algorithm algorithm; // The algorithm to use
Operator selection;
Operator crossover;
Dictionary <string, object> parameters; // Operator parameters
QualityIndicator indicators; // Object to get quality indicators
// 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 = "GDE3.log";
fileAppender.ActivateOptions();
indicators = null;
if (args.Length == 1)
{
object[] param = { "Real" };
problem = ProblemFactory.GetProblem(args[0], param);
}
else if (args.Length == 2)
{
object[] param = { "Real" };
problem = ProblemFactory.GetProblem(args[0], param);
indicators = new QualityIndicator(problem, args[1]);
}
else
{ // Default problem
problem = new Kursawe("Real", 3);
//problem = new Water("Real");
//problem = new ZDT1("ArrayReal", 100);
//problem = new ConstrEx("Real");
//problem = new DTLZ1("Real");
//problem = new OKA2("Real") ;
}
algorithm = new JMetalCSharp.Metaheuristics.GDE3.GDE3(problem);
// Algorithm parameters
algorithm.SetInputParameter("populationSize", 100);
algorithm.SetInputParameter("maxIterations", 250);
// Crossover operator
parameters = new Dictionary <string, object>();
parameters.Add("CR", 0.5);
parameters.Add("F", 0.5);
crossover = CrossoverFactory.GetCrossoverOperator("DifferentialEvolutionCrossover", parameters);
// Add the operators to the algorithm
parameters = null;
selection = SelectionFactory.GetSelectionOperator("DifferentialEvolutionSelection", parameters);
algorithm.AddOperator("crossover", crossover);
algorithm.AddOperator("selection", selection);
// 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("Objectives values have been writen to file FUN");
population.PrintObjectivesToFile("FUN");
logger.Info("Variables values have been writen to file VAR");
population.PrintVariablesToFile("VAR");
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));
}
Console.WriteLine("Total execution time gde: moead" + estimatedTime + "ms");
Console.ReadLine();
}