/// <summary>Initializes a new instance of the <see cref="AdaptiveGaussKronrodIntegrator"/> class.
/// </summary>
/// <param name="integrationRule">The integration rule.</param>
/// <param name="exitCondition">The exit condition.</param>
public AdaptiveGaussKronrodIntegrator(Approach integrationRule, StoppingCriteria exitCondition)
: base(BoundDescriptor.Closed, BoundDescriptor.Closed)
{
if (exitCondition == null)
{
throw new ArgumentNullException("exitCondition");
}
ExitCondition = exitCondition;
IntegrationRule = integrationRule;
m_Name = new IdentifierString(String.Format("Adaptive Gauss-Kronrod {0} Integrator", integrationRule.ToFormatString()));
}
/// <summary>
/// Processes the GA to search for an optimal TSP solution until the stopping criteria is met.
/// </summary>
public void Compute(bool showProgression = true)
{
Console.ForegroundColor = ConsoleColor.White;
Console.WriteLine($"\nStart TSP Genetic Algorithm [Starting Date Time {DateTime.Now}]");
Console.ForegroundColor = ConsoleColor.Green;
Console.WriteLine($"\nTSP_Instance\n************\nName: {_tspInstance.Name}\nComment: {_tspInstance.Comment}\nDimension: {_tspInstance.Dimensions}\nEdge_Weight_Type: {_tspInstance.EdgeWeightType}");
Console.WriteLine($"\nOptions\n*********\nPopulation: {GaOptions.PopulationSize}\nEncoding_Type: {GaOptions.EncodingType}\nSelection_Operator: {GaOptions.SelectionType}\nCrossover_Operator: {GaOptions.CrossoverType}\nMutation_Operator: {GaOptions.MutationType}" +
$"\nMutation_Rate: {GaOptions.MutationRate}\nElite_Rate: {GaOptions.Elitism}\n{StoppingCriteria.CriteriaToString()}\n");
Console.ForegroundColor = ConsoleColor.White;
CreateInitialPopulation();
var bestIndex = Population.HighestFitnessIndex;
var bestChromomsome = Population.Chromosomes[bestIndex];
Console.WriteLine($"Best initial distance: {bestChromomsome.Distance} \nComputing...");
// stops when the stopping criteria is met
while (!StoppingCriteria.IsCriteriaMet())
{
bestIndex = Population.HighestFitnessIndex;
var currentBestChromomsome = Population.Chromosomes[bestIndex];
if (currentBestChromomsome.Fitness > bestChromomsome.Fitness)
{
bestChromomsome = currentBestChromomsome;
if (showProgression)
{
Console.WriteLine($"A new shorter distance was found at generation {Generation} with distance {bestChromomsome.Distance}");
}
}
CreateNextGeneration();
}
// since we dont have a reference to the starting and ending city [1] in the Genome we add these so we could display them,
// as they are taken into consideration when calculating fitness and we always start and end at City with ID 1
Console.WriteLine($"\nDistance for the most optimal tour: {bestChromomsome.Distance}");
Console.WriteLine($"1,{string.Join(",", bestChromomsome.GetGenome())},1");
Console.WriteLine($"\nEnd TSP Genetic Algorithm [Finished Date Time {DateTime.Now}]\n");
}
/// <summary>Initializes a new instance of the <see cref="AdaptiveGaussLobattoIntegrator"/> class.
/// </summary>
/// <param name="exitCondition">The exit condition.</param>
public AdaptiveGaussLobattoIntegrator(StoppingCriteria exitCondition)
: base(BoundDescriptor.Closed, BoundDescriptor.Closed)
{
ExitCondition = exitCondition ?? throw new ArgumentNullException(nameof(exitCondition));
m_Name = new IdentifierString("Adaptive Gauss-Lobatto Integrator");
}
