static void Main(string[] args)
{
#if DEBUG
const string testingPath = "C:/Users/Nalyd/Desktop/Current Projects/Github/Solving-TSP-using-GA-ACO/TravellingSalesmanProblem/TSP/TSPInstances/berlin52.tsp";
var tspInstance = new TspInstance(testingPath);
var stoppingCriteriaOptions = new StoppingCriteriaOptions
{
StoppingCriteriaType = StoppingCriteriaType.SpecifiedIterations,
MaximumIterations = 500
};
var gaOptions = new GAOptions(1000, EncodingType.Permutation, SelectionType.Tos, CrossoverType.Cycle, MutationType.SingleSwap, 0.1, 0.3, stoppingCriteriaOptions);
var tspGeneticAlgorithm = new TspGeneticAlgorithm(gaOptions, tspInstance);
tspGeneticAlgorithm.Compute(false);
//var acoOptions = new ACOOptions(4, 3, 2, 0.01, 2.00, stoppingCriteriaOptions);
//var aco = new TspAntColonyOptimization(acoOptions, tspInstance);
//aco.Compute();
Console.ReadLine();
#else
Parser.Default.ParseArguments <TspOptions>(args).WithParsed(SolveTsp);
#endif
}
/// <summary>
/// Default constructor.
/// </summary>
public GAOptions()
{
PopulationSize = 100;
EncodingType = EncodingType.Permutation;
SelectionType = SelectionType.Rws;
CrossoverType = CrossoverType.Pmx;
MutationType = MutationType.SingleSwap;
MutationRate = 0.3f;
Elitism = 0;
// if not specified the GA will use an iteration based stopping criteria, set to a maximum iteration of 1000.
StoppingCriteriaOptions = new StoppingCriteriaOptions
{
StoppingCriteriaType = StoppingCriteriaType.SpecifiedIterations,
MaximumIterations = 1000
};
}
/// <summary>
/// Constructor with parameters.
/// </summary>
/// <param name="totalAnts">The total number of ants used in the ACO.</param>
/// <param name="alpha">Pheromone influence.</param>
/// <param name="beta">Local node influence.</param>
/// <param name="rho">Pheromone evaportation coefficient.</param>
/// <param name="q">Pheromone deposit factor.</param>
/// <param name="stoppingCriteriaOptions">The stopping critieria options used to stop the algorithm from executing forever.
/// Optional if not specified, it will use the default which is set to an iteration threshold with maximum of 1000 iterations.</param>
public ACOOptions(int totalAnts, int alpha, int beta, double rho, double q, StoppingCriteriaOptions stoppingCriteriaOptions = null)
{
TotalAnts = totalAnts;
Alpha = alpha;
Beta = beta;
Rho = rho;
Q = q;
if (stoppingCriteriaOptions == null)
{
// if not specified the GA will use an iteration based stopping criteria, set to a maximum iteration of 1000.
StoppingCriteriaOptions = new StoppingCriteriaOptions
{
StoppingCriteriaType = StoppingCriteriaType.SpecifiedIterations,
MaximumIterations = 1000
};
return;
}
StoppingCriteriaOptions = stoppingCriteriaOptions;
}
/// <summary>
/// Constructor with parameters.
/// </summary>
/// <param name="populationSize">The population size.</param>
/// <param name="encodingType">The encoding type used in the chromosome.</param>
/// <param name="selectionType">The selection type.</param>
/// <param name="crossoverType">The crossover type.</param>
/// <param name="mutationType">The mutation type.</param>
/// <param name="mutationRate">The mutation rate.</param>
/// <param name="elitisim">The elistism rate.</param>
/// <param name="stoppingCriteriaOptions">The stopping critieria options used to stop the algorithm from executing forever.
/// Optional if not specified, it will use the default which is set to an iteration threshold with maximum of 1000 iterations.</param>
public GAOptions(int populationSize, EncodingType encodingType, SelectionType selectionType, CrossoverType crossoverType, MutationType mutationType,
double mutationRate, double elitisim, StoppingCriteriaOptions stoppingCriteriaOptions = null)
{
PopulationSize = populationSize;
EncodingType = encodingType;
SelectionType = selectionType;
CrossoverType = crossoverType;
MutationType = mutationType;
MutationRate = mutationRate;
Elitism = elitisim;
if (stoppingCriteriaOptions == null)
{
// if not specified the GA will use an iteration based stopping criteria, set to a maximum iteration of 1000.
StoppingCriteriaOptions = new StoppingCriteriaOptions
{
StoppingCriteriaType = StoppingCriteriaType.SpecifiedIterations,
MaximumIterations = 1000
};
return;
}
StoppingCriteriaOptions = stoppingCriteriaOptions;
}
/// <summary>
/// Solves the TSP problem with the arguments passed from the console.
/// </summary>
/// <param name="options">The parsed arguments as <see cref="TspOptions"/> .</param>
private static void SolveTsp(TspOptions options)
{
var tspInstancePath = options.TspInstancePath;
var tspInstance = new TspInstance(tspInstancePath);
var algorithmValue = options.Algorithm;
EvolutionaryComputationType algorithm;
if (Enum.TryParse(algorithmValue, true, out algorithm))
{
var stoppingCriteriaValue = options.StoppingCriteriaType;
StoppingCriteriaType stoppingCriteriaType;
if (Enum.TryParse(stoppingCriteriaValue, true, out stoppingCriteriaType))
{
var stoppingCriteriaOptions = new StoppingCriteriaOptions
{
StoppingCriteriaType = stoppingCriteriaType
};
switch (stoppingCriteriaType)
{
case StoppingCriteriaType.TimeBased:
stoppingCriteriaOptions.MinutesPassed = options.StoppingCriteriaTimeMinutes;
break;
case StoppingCriteriaType.SpecifiedIterations:
stoppingCriteriaOptions.MaximumIterations = options.StoppingCriteriaIteration;
break;
}
TimeSpan timeSpan;
long memoryConsumed;
long processMemoryConsumed;
var stopWatch = new Stopwatch();
var population = options.Population;
switch (algorithm)
{
// using GA to solve TSP
case EvolutionaryComputationType.GeneticAlgorithm:
var selectionOperatorValue = options.SelectionOperator;
SelectionType selectionType;
if (!Enum.TryParse(selectionOperatorValue, true, out selectionType))
{
selectionType = SelectionType.Rws;
}
var crossoverOperatorType = options.CrossoverOperator;
CrossoverType crossoverType;
if (!Enum.TryParse(crossoverOperatorType, true, out crossoverType))
{
crossoverType = CrossoverType.Cycle;
}
var mutationOperatorType = options.MutationOperator;
MutationType mutationType;
if (!Enum.TryParse(mutationOperatorType, true, out mutationType))
{
mutationType = MutationType.SingleSwap;
}
var mutationRate = options.MutationRate;
var elitisimRate = options.ElitismRate;
var genticAlgorithmOptions = new GAOptions(population, EncodingType.Permutation, selectionType, crossoverType, mutationType, mutationRate,
elitisimRate, stoppingCriteriaOptions);
var tspGeneticAlgorithm = new TspGeneticAlgorithm(genticAlgorithmOptions, tspInstance);
var showProgression = options.ShowProgressionComments;
stopWatch.Start();
tspGeneticAlgorithm.Compute(showProgression);
memoryConsumed = GC.GetTotalMemory(false);
processMemoryConsumed = Process.GetCurrentProcess().WorkingSet64;
stopWatch.Stop();
timeSpan = stopWatch.Elapsed;
Console.ForegroundColor = ConsoleColor.Green;
Console.WriteLine("Time_Elapsed: {0}\nGC_Memory_Consumed: {1}\nProcess_Memory_Consumed: {2}\nTotal_Cities: {3}",
timeSpan.ToString("mm\\:ss\\.ff"), memoryConsumed, processMemoryConsumed, tspInstance.CitiesLength);
break;
// using ACO to solve TSP
case EvolutionaryComputationType.AntColonyOptimization:
var alpha = options.Alpha;
var beta = options.Beta;
var rho = options.Rho;
var q = options.Q;
var antColonyOptimizationOptions = new ACOOptions(population, alpha, beta, rho, q, stoppingCriteriaOptions);
var tspAntColonyOptimization = new TspAntColonyOptimization(antColonyOptimizationOptions,
tspInstance);
stopWatch.Start();
tspAntColonyOptimization.Compute();
memoryConsumed = GC.GetTotalMemory(false);
processMemoryConsumed = Process.GetCurrentProcess().WorkingSet64;
stopWatch.Stop();
timeSpan = stopWatch.Elapsed;
Console.ForegroundColor = ConsoleColor.Green;
Console.WriteLine("Time_Elapsed: {0}\nGC_Memory_Consumed: {1}\nProcess_Memory_Consumed: {2}\nTotal_Cities: {3}",
timeSpan.ToString("mm\\:ss\\.ff"), memoryConsumed, processMemoryConsumed, tspInstance.CitiesLength);
break;
}
}
else
{
throw new Exception("Stopping Criteria specified does not exist. Use --help for more information.");
}
}
else
{
throw new Exception("Algorithm specified does not exist. Use --help for more information.");
}
Console.ReadLine();
}
