/// <summary>
/// Usage: three options
/// - NSGAII
/// - NSGAII problemName
/// - NSGAII problemName paretoFrontFile
/// </summary>
/// <param name="args"></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 = "NSGAII.log";
fileAppender.ActivateOptions();
indicators = null;
if (args.Length == 1)
{
object[] param = { "Int", RowCol[1], RowCol[0], Matrix };
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 ZDT3("ArrayReal", 30);
//problem = new ConstrEx("Real");
//problem = new DTLZ1("Real");
//problem = new OKA2("Real") ;
//problem = new JMetalCSharp.Problems.Fonseca.Fonseca("Real");
problem = new JMetalCSharp.Problems.Regression.Regression("Int", RowCol[1], RowCol[0], Matrix);
//problem = new JMetalCSharp.Problems.IntRealProblem.IntRealProblem("Int");
}
algorithm = new JMetalCSharp.Metaheuristics.NSGAII.NSGAII(problem);
//algorithm = new ssNSGAII(problem);
// Algorithm parameters
algorithm.SetInputParameter("populationSize", 400); //100
algorithm.SetInputParameter("maxEvaluations", 105000); //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("SinglePointCrossover", parameters);
parameters = new Dictionary <string, object>();
parameters.Add("probability", 1.0 / problem.NumberOfVariables);
parameters.Add("distributionIndex", 20.0);
mutation = MutationFactory.GetMutationOperator("BitFlipMutation", 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();
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");
variables = population.GetVariable();
logger.Info("Objectives values have been writen to file FUN");
fun = population.GetFun();
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));
int evaluations = (int)algorithm.GetOutputParameter("evaluations");
logger.Info("Speed : " + evaluations + " evaluations");
}
}