public override bool Execute()
{
string aim = "Hello friends";
int dim = aim.Length;
Func <string, double> fn1 = (s => s.Select((b, i) => ((int)b - aim[i]) * 2 * ((int)b - aim[i])).Sum());
Func <string, double> fn = (s => fn1(s));
SimulatedAnnealing <string> sa = new SimulatedAnnealing <string>()
{
NeighborhoodProvider = Neighbors,
EnergyProvider = fn,
CurrentElement = "^ùsprôqù;rqô;ùvere!wlpc\"!".Substring(0, dim)
};
for (int i = 0; i < 100; ++i)
{
sa.Run(100);
sa.CurrentElement = sa.BestFound;
}
if (IsVerbose)
{
Console.WriteLine(" ::!:: " + sa.BestFound);
}
if (sa.BestFound != aim)
{
return(false);
}
return(true);
}
private static ISolver SimulatedAnnealingGenerator()
{
GreedyFastHeuristic randomSolver = new GreedyFastHeuristic()
{
MaxOverfillUnits = 0,
DiagnosticMessages = true,
};
CompoundSolver compundSolver = new CompoundSolver()
{
MaxLoops = 7,
DiagnosticMessages = true,
};
SimulatedAnnealing simulatedAnnealing = new SimulatedAnnealing()
{
DiagnosticMessages = true,
ScoringFunction = new Scorer(),
StepsAnalyzedWithoutImprovementToStop = 200,
NumberOfLoopsWithoutActionsToStop = 1,
PropagateRandomSeed = true,
Seed = 100,
Description = "SimulatedAnnealing",
IntegrityLossMultiplier = 1000.0,
};
simulatedAnnealing.InitialSolvers.Add(randomSolver);
simulatedAnnealing.InitialSolvers.Add(compundSolver);
return(simulatedAnnealing);
}
public ActionResult StartSimulatedAnnealing(SimulatedAnnealingRequestViewModel algorithmRequest)
{
if (!ModelState.IsValid)
{
return(new HttpStatusCodeResult(HttpStatusCode.BadRequest));
}
var graph = new Graph();
graph.FillGraphRandomly(algorithmRequest.NumberOfNodesToGenerate, algorithmRequest.MaxNeighboursForNode);
var hubContext = GlobalHost.ConnectionManager.GetHubContext <SimulatedAnnealingHub>();
var algorithm = new SimulatedAnnealing(graph, new MinimumVertexCover(),
new CoolingSetup(4000, 0.03, new AllRandomCooling()));
algorithm.LaunchAlgorithm();
algorithm.ProgressReportChanged.Subscribe(report =>
{
Observable.Timer(TimeSpan.FromMilliseconds(500)).Subscribe(_ =>
{
hubContext.Clients.Group(algorithm.Id.ToString()).send(report.AsReportViewModel());
});
});
return(new JsonResult
{
Data = new { graph = graph.AsD3ViewModel(), hubId = algorithm.Id },
ContentType = "application/x-javascript",
JsonRequestBehavior = JsonRequestBehavior.AllowGet
});
}
static void Main(string[] args)
{
IntelligentRandom intelligentRandom = new IntelligentRandom();
BasicFitnessCalculator basicCalculator = new BasicFitnessCalculator();
var simulatedAnnealing = new SimulatedAnnealing();
simulatedAnnealing.Simulate(
startTemperature: 10000,
boardSize: 10,
generator: intelligentRandom,
calculator: basicCalculator
);
Console.WriteLine(simulatedAnnealing.Result);
var tabuSearch = new TabuSearch();
tabuSearch.Simulate(
maxTabuListCount: 1500,
boardSize: 10,
generator: intelligentRandom,
calculator: basicCalculator
);
Console.WriteLine(tabuSearch.Result);
}
static void Main(string[] args)
{
string[] lines = File.ReadAllLines("C:\\Users\\igork\\source\\repos\\TLD\\TLD\\tests.txt");
string[] _density = lines[1].Split(",");
List <int> _den = new List <int>();
foreach (string d in _density)
{
_den.Add(int.Parse(d));
}
Junction jn = new Junction(int.Parse(lines[0]), _den.ToArray());
Constraints cons = new Constraints(int.Parse(lines[0]));
string[] temp = lines[2].Split("|");
foreach (string s in temp)
{
string[] p = s.Split(",");
cons.Add_cons(int.Parse(p[0]), int.Parse(p[1]));
}
jn = SimulatedAnnealing.Compute(jn, 20f, 0.000001f, 50);
// Console.WriteLine(tools.DeepToString(_den.ToArray()));
Console.WriteLine(jn);
//for(int i = 0; i < jn.getOrder().Length; i++) {
// Console.Write(" " + jn.getOrder()[i]);
//}
}
static List <Simulation> RunSimulatedAnnealing(Simulation s, int duration)
{
SimulatedAnnealing SA = HeuristicFactory.CreateSA(s, duration, SimulatedAnnealingBase.DebugLevel.All);
SA.Run();
return(SA.BestSolutions);
}
private void CalculateDistancesAndCreateAreas()
{
if (NrOfSalesmans <= 1)
{
foreach (SilverlightEdge edge in _slEdges)
{
foreach (SilverlightEdge edge1 in _slEdges)
{
edge.PolarCords.Center(new Point(300, 400));
}
}
annealingForOne = new SimulatedAnnealing();
List <int> order = annealingForOne.CalculateInit(_slEdges, distances, Alpha, Temp, Epsilon);
DrawLines(order);
}
else
{
Clusters = CreateAreas(NrOfSalesmans);
foreach (Cluster clust in Clusters)
{
foreach (SilverlightEdge edge in clust.Edges)
{
foreach (SilverlightEdge edge1 in clust.Edges)
{
edge.PolarCords.Center(clust.ClusterCenter);
}
}
clust.Color = Common.ColorPallete.GetColor();
List <int> order = clust.Annealing.CalculateInit(clust.Edges, distances, Alpha, Temp, Epsilon);
DrawLines(order);
}
}
}
private static void Main(string[] args)
{
var voc = Vocabulary.GetVocabularyFromFile(@"Vocabulary.json");
var grammar = new Grammar(voc);
ProgramParams programParams;
using (var file = File.OpenText(@"ProgramParameters.json"))
{
var serializer = new JsonSerializer();
programParams = (ProgramParams)serializer.Deserialize(file, typeof(ProgramParams));
}
if (programParams.DataWithMovement)
{
CreateMovementGrammar(grammar);
}
else
{
CreateSimpleGrammar(grammar);
}
grammar.GenerateDerivedRulesFromSchema();
var p = new Parser(grammar);
var data = p.GenerateSentences(programParams.NumberOfDataSentences);
using (var sw = File.AppendText("SessionReport.txt"))
{
sw.WriteLine("-------------------");
sw.WriteLine("Session {0} ", DateTime.Now.ToString("MM/dd/yyyy h:mm tt"));
sw.WriteLine("sentences: {0}, runs: {1}, movement: {2}", programParams.NumberOfDataSentences,
programParams.NumberOfRuns, programParams.DataWithMovement);
}
var stopWatch = StartWatch();
var learner = new Learner(voc, grammar.NonTerminalsTypeDictionary, grammar.POSTypes, data, grammar);
learner.originalGrammar.GenerateDerivedRulesFromSchema();
var targetGrammarEnergy = learner.Energy(learner.originalGrammar);
learner.originalGrammar.GenerateInitialRulesFromDerivedRules();
var s = string.Format("Target Hypothesis:\r\n{0} with energy: {1}\r\n", learner.originalGrammar,
targetGrammarEnergy);
Console.WriteLine(s);
using (var sw = File.AppendText("SessionReport.txt"))
{
sw.WriteLine(s);
}
for (var i = 0; i < programParams.NumberOfRuns; i++)
{
var sa = new SimulatedAnnealing(learner);
sa.Run();
}
StopWatch(stopWatch);
}
public void Test_That_Simulated_Annealing_On_Sphere_Works()
{
SimulatedAnnealing <double[]> garden = new SimulatedAnnealing <double[]>();
Sphere sphere = new Sphere();
garden.create(sphere.getConfiguration());
garden.fullIteration();
}
public SimulatedAnnealingTests()
{
Algorithm =
new SimulatedAnnealing(
_graphFactory.ProvideValid(),
_problemProvider.ProvideSubstitute(),
_coolingSetupFactory.ProvideValid());
}
public void Test_That_Knapsack_Simulated_Annealing_Works()
{
this.Load(Constants.SAMPLE_MKNAPCB4_DATASET);
Console.WriteLine($"Goal:\t{this.goal}");
SimulatedAnnealing <List <int> > sa = new SimulatedAnnealing <List <int> >();
sa.create(this.getConfiguration());
List <int> finalSolution = sa.fullIteration();
}
public SimulatedAnnealingTests()
{
Algorithm =
new SimulatedAnnealing(
_graphFactory.ProvideValid(),
_problemProvider.ProvideSubstitute(),
_coolingSetupFactory.ProvideValid());
}
private void btnSimAne_Click(object sender, EventArgs e)
{
label5.Text = "Processando, aguarde...";
label5.Refresh();
CarregaValores();
SimulatedAnnealing s = new SimulatedAnnealing();
s.Process();
ImprimeResultado(s.MelhorResultado);
//this
}
private SimulatedAnnealing CreateSimulatedAnnealingRastriginSample()
{
SimulatedAnnealing sa = new SimulatedAnnealing();
#region Problem Configuration
var problem = new SingleObjectiveTestFunctionProblem();
problem.BestKnownQuality.Value = 0.0;
problem.BestKnownSolutionParameter.Value = new RealVector(new double[] { 0, 0 });
problem.Bounds = new DoubleMatrix(new double[, ] {
{ -5.12, 5.12 }
});
problem.EvaluatorParameter.Value = new RastriginEvaluator();
problem.Maximization.Value = false;
problem.ProblemSize.Value = 2;
problem.SolutionCreatorParameter.Value = new UniformRandomRealVectorCreator();
#endregion
#region Algorithm Configuration
sa.Name = "Simulated Annealing - Rastrigin";
sa.Description = "A simulated annealing algorithm that solves the 2-dimensional Rastrigin test function";
sa.Problem = problem;
var annealingOperator = sa.AnnealingOperatorParameter.ValidValues
.OfType <ExponentialDiscreteDoubleValueModifier>()
.Single();
annealingOperator.StartIndexParameter.Value = new IntValue(0);
sa.AnnealingOperator = annealingOperator;
sa.EndTemperature.Value = 1E-6;
sa.InnerIterations.Value = 50;
sa.MaximumIterations.Value = 100;
var moveEvaluator = sa.MoveEvaluatorParameter.ValidValues
.OfType <RastriginAdditiveMoveEvaluator>()
.Single();
moveEvaluator.A.Value = 10;
sa.MoveEvaluator = moveEvaluator;
var moveGenerator = sa.MoveGeneratorParameter.ValidValues
.OfType <StochasticNormalMultiMoveGenerator>()
.Single();
moveGenerator.SigmaParameter.Value = new DoubleValue(1);
sa.MoveGenerator = moveGenerator;
sa.MoveMaker = sa.MoveMakerParameter.ValidValues
.OfType <AdditiveMoveMaker>()
.Single();
sa.Seed.Value = 0;
sa.SetSeedRandomly.Value = true;
sa.StartTemperature.Value = 1;
#endregion
sa.Engine = new ParallelEngine.ParallelEngine();
return(sa);
}
private ISolver SimulatedAnnealingMaker()
{
SimulatedAnnealing solver = new SimulatedAnnealing()
{
ScoringFunction = new Scorer(),
DiagnosticMessages = true,
PropagateRandomSeed = true,
TimeLimit = new TimeSpan(0, 3, 0),
StepsAnalyzedWithoutImprovementToStop = 300,
Description = "simulated_annealing_test",
};
return(solver);
}
static void Main(string[] args)
{
var inf = int.MaxValue;
var arr = new int[, ]
{
{ inf, 21, 53, 11, 72, 13, 54, 25, 66, 57 },
{ 21, inf, 32, 28, 97, 16, 95, 54, 73, 23 },
{ 53, 32, inf, 86, 65, 24, 83, 12, 31, 40 },
{ 11, 28, 86, inf, 31, 42, 13, 24, 45, 16 },
{ 72, 97, 65, 31, inf, 78, 57, 76, 45, 65 },
{ 13, 16, 24, 42, 78, inf, 4, 95, 15, 35 },
{ 54, 95, 83, 13, 57, 4, inf, 11, 72, 83 },
{ 25, 54, 12, 24, 76, 95, 11, inf, 44, 44 },
{ 66, 73, 31, 45, 45, 15, 72, 44, inf, 59 },
{ 57, 23, 40, 16, 65, 35, 83, 44, 95, inf }
};
var graph = new Graph(arr);
Console.WriteLine($"{"algorithm name",40} {"iterations",15} {"result",10} {"time (ms)",15}");
ReturnData bruteForceSearch = BruteForceSearch.Search(graph);
Console.WriteLine($"{bruteForceSearch.AlgorithmName,40} {bruteForceSearch.Iterations,15} " +
$"{bruteForceSearch.Result,10} {bruteForceSearch.TimeInMs,15}");
ReturnData greedyAlgorithm = GreedyAlgorithm.Search(graph);
Console.WriteLine($"{greedyAlgorithm.AlgorithmName,40} {greedyAlgorithm.Iterations,15} " +
$"{greedyAlgorithm.Result,10} {greedyAlgorithm.TimeInMs,15}");
ReturnData branchAndBoundAlgorithm = BranchAndBoundAlgorithm.Search(graph);
Console.WriteLine($"{branchAndBoundAlgorithm.AlgorithmName,40} {branchAndBoundAlgorithm.Iterations,15} " +
$"{branchAndBoundAlgorithm.Result,10} {branchAndBoundAlgorithm.TimeInMs,15}");
ReturnData localSearch2Algorithm = LocalSearch2.Search(graph);
Console.WriteLine($"{localSearch2Algorithm.AlgorithmName,40} {localSearch2Algorithm.Iterations,15} " +
$"{localSearch2Algorithm.Result,10} {localSearch2Algorithm.TimeInMs,15}");
ReturnData simulatedAnnealingAlgorithm_lin = SimulatedAnnealing.Search(graph, SimulatedAnnealing.GetNewTemperature_Linear);
Console.WriteLine($"{simulatedAnnealingAlgorithm_lin.AlgorithmName + "(lin)",40} {simulatedAnnealingAlgorithm_lin.Iterations,15} " +
$"{simulatedAnnealingAlgorithm_lin.Result,10} {simulatedAnnealingAlgorithm_lin.TimeInMs,15}");
ReturnData simulatedAnnealingAlgorithm_pow = SimulatedAnnealing.Search(graph, SimulatedAnnealing.GetNewTemperature_Power);
Console.WriteLine($"{simulatedAnnealingAlgorithm_pow.AlgorithmName + "(pow)",40} {simulatedAnnealingAlgorithm_pow.Iterations,15} " +
$"{simulatedAnnealingAlgorithm_pow.Result,10} {simulatedAnnealingAlgorithm_pow.TimeInMs,15}");
}
private void CalculateDistancesAndCreateAreasThread()
{
if (_nrOfSalesman <= 1)
{
annealingForOne = new SimulatedAnnealing();
List <int> order = annealingForOne.CalculateInit(_slEdges, distances, _alpha, _temp, _epsilon);
}
else
{
foreach (Cluster clust in Clusters)
{
List <int> order = clust.Annealing.CalculateInit(clust.Edges, distances, _alpha, _temp, _epsilon);
}
}
}
private ISolver SimulatedAnnealingMakerParametrized(double alpha, double beta, string name)
{
SimulatedAnnealing solver = new SimulatedAnnealing()
{
ScoringFunction = new Scorer(),
DiagnosticMessages = true,
PropagateRandomSeed = true,
TimeLimit = new TimeSpan(0, 3, 0),
TemperatureMultiplier = alpha,
TemperatureAddition = beta,
StepsAnalyzedWithoutImprovementToStop = 300,
Description = name,
};
return(solver);
}
private ISolver SimulatedAnnealingBest()
{
SimulatedAnnealing solver = new SimulatedAnnealing()
{
ScoringFunction = new Scorer(),
DiagnosticMessages = true,
PropagateRandomSeed = true,
TimeLimit = new TimeSpan(1, 30, 0),
TemperatureMultiplier = 0.999973432905173,
TemperatureAddition = 3.51023731470062E-06,
StepsAnalyzedWithoutImprovementToStop = 600,
Description = "annealing_best",
};
return(solver);
}
/// <summary>
/// Compute traffic lights cycle's order, MUST HAVE CONSTRAINTS IN PLACE!
/// sends to the result adapter the results
/// </summary>
/// <param name="_nor"> Number of elements (roads or traffic lights) </param>
/// <param name="_density"> Density of each element as list : (road number 1, density(0)),.... </param>
/// <returns> Open order for elements based on constraints. each row represent cycles segment,
/// each column represents number of element to open. </returns>
public void AlgoRun(int _nor, List <int> _density)
{
cycleTime = numOfRoads * timeForCycle;
Constraints _cons = new Constraints(_nor);
foreach (string s in constraintList)
{
string[] temp = s.Split(',');
_cons.Add_cons(int.Parse(temp[0]), int.Parse(temp[1]));
}
Junction _jn = new Junction(_nor, _density.ToArray());
Junction _result = SimulatedAnnealing.Compute(_jn, 20, 0.00001f, 3500);
Debug.Log(_result);
resultAdapter.GetComponent <resultAdapter>().schedule(_result, cycleTime);
}
public static SimulatedAnnealingEntity AsEntityModel(this SimulatedAnnealing model, GraphEntity graph = null)
{
var usedGraph = graph ?? model.Graph.AsEntityModel();
var problem = model.Problem.AsEntityModelResolved(graph);
return(new SimulatedAnnealingEntity
{
Id = model.Id,
Graph = graph,
CoolingRate = model.CoolingSetup.CoolingRate,
CoolingStrategy = CoolingFactory.ProvideMappingType(model.CoolingSetup.CoolingStrategy),
CurrentTemperature = model.CurrentTemperature,
InitialTemperature = model.CoolingSetup.InitialTemperature,
Problem = problem,
TotalProcessorTimeCost = model.TotalProcessorTimeCost
});
}
public void AnnealingTest1()
{
file = root + "\\bays29.xml";
XDocument tspFile = XDocument.Load(file);
AdjacencyMatrix testMatrix = new AdjacencyMatrix(tspFile);
SimulatedAnnealing test = new SimulatedAnnealing(testMatrix);
test.SetTemperature(10, (float)0.997, 1, 5000);
for (int i = 0; i < 1000; i++)
{
float result = test.Calculate();
if (result > 3500) // best result world known is 2020
{
Assert.Fail();
}
}
}
public static void KnapsackTest()
{
Console.WriteLine("============");
Console.WriteLine("SA Knapsack problem example");
Console.WriteLine("Knapsack instances are generated according to this paper:");
Console.WriteLine("http://www.dcs.gla.ac.uk/~pat/cpM/jchoco/knapsack/papers/hardInstances.pdf");
Console.WriteLine("============");
var logger = new Logger <SimulatedAnnealing <KnapsackSolution> >(new LoggerFactory());
var cooler = new QuadraticCooler(0.998, 800);
var mover = new KnapsackMover();
var instances = new KnapsackInstancesFactory(50, 5000, 1)
.GenDistribution(DistributionVariant.Strong, 3)
.GenDistribution(DistributionVariant.Uncorrelated, 3)
.Collect();
var stop = new NotGettingBetter <KnapsackSolution>(cooler, OptimizationType.Maximization, 500000, 300000);
Console.WriteLine("COMMON ELEMENTS");
Console.WriteLine($" Stop criteria: {stop}");
Console.WriteLine($" Cooler: {cooler}");
Console.WriteLine("INSTANCES");
for (int i = 0; i < instances.Count; i++)
{
var instance = instances[i];
Console.WriteLine($"Instance name: {instance.Name}");
var sa = new SimulatedAnnealing <KnapsackSolution>(cooler, mover, stop, logger);
var obj = new KnapsackObj(instance);
var constraint = new KnapsackConstraint(instance);
var objAggr = new WeightedObjectiveAggregator <KnapsackSolution>(new[] { obj }, new double[] { 1 });
var constraintAggr = new WeightedConstraintAggregator <KnapsackSolution>(new[] { constraint }, new double[] { 1 });
var criterion = new Criterion <KnapsackSolution>(OptimizationType.Maximization, objAggr, constraintAggr);
var solution = new KnapsackSolution(instance);
var bestSol = sa.Solve(solution, criterion);
Console.WriteLine(instance);
Console.WriteLine(bestSol);
Console.WriteLine("----");
}
}
public static void Main(string[] args)
{
Utils.Parse.CSV <Category>(FILE_CATEGORY);
Utils.Parse.CSV <Location>(FILE_LOCATION);
Distances.Parse(FILE_DISTANCES);
GRASP GRASP = new GRASP(Location.Instance("Plaza del Adelantado"));
SimulatedAnnealing SA = new SimulatedAnnealing(50, .05, Location.Instance("Plaza del Adelantado"));
TabuSearch TS = new TabuSearch(7, Location.Instance("Plaza del Adelantado"));
VariableNeighborhoodSearch VNS = new VariableNeighborhoodSearch(5, Location.Instance("Plaza del Adelantado"));
Console.WriteLine("GRASP:");
Run(GRASP, 20);
Console.WriteLine("\nSA:");
//Run(SA, 20);
Console.WriteLine("\nTS:");
Run(TS, 20);
//Console.WriteLine("\nVNS:");
//Run(VNS, 20);
}
/// <summary>
/// Tests a simulated annealing.
/// </summary>
/// <typeparam name="TGene"></typeparam>
/// <param name="testNumber">The number of the test.</param>
/// <param name="testDescription">The description of the test.</param>
/// <param name="simulatedAnnealing">The simulated annealing to test.</param>
/// <param name="objectiveFunction">The objective funtion to test.</param>
///
/// <param name="maxIterationCount">The maximum number of iterations (the computational budget).</param>
/// <param name="acceptableEnergy">The acceptable energy.</param>
///
/// <param name="initialTemperature">The initial temperature.</param>
/// <param name="finalTemperature">The final temperature.</param>
/// <apra
private static void Test <T>(int testNumber, string testDescription, SimulatedAnnealing <T> simulatedAnnealing, ObjectiveFunction <T> objectiveFunction,
int maxIterationCount, double acceptableEnergy,
double initialTemperature, double finalTemperature)
{
// Print the number of the test and its description.
Console.WriteLine("Test " + testNumber + " : " + testDescription);
// Run the simulated annealing.
DateTime startTime = DateTime.Now;
int usedIterationCount;
double achievedEnergy;
T[] solution = simulatedAnnealing.Run(objectiveFunction,
maxIterationCount, out usedIterationCount, acceptableEnergy, out achievedEnergy,
initialTemperature, finalTemperature
);
DateTime endTime = DateTime.Now;
// Build the solution string.
StringBuilder solutionSB = new StringBuilder();
solutionSB.Append("[");
foreach (T component in solution)
{
solutionSB.Append(component + ", ");
}
if (solution.Length != 0)
{
solutionSB.Remove(solutionSB.Length - 2, 2);
}
solutionSB.Append("]");
// Print the results.
Console.WriteLine("Test " + testNumber + " : Duration: " + (endTime - startTime));
Console.WriteLine("Test " + testNumber + " : Number of iterations taken : " + usedIterationCount);
Console.WriteLine("Test " + testNumber + " : Best solution : " + solutionSB.ToString());
Console.WriteLine("Test " + testNumber + " : Best solution's evaluation : " + achievedEnergy);
}
void Start()
{
World initialWorld = new World();
for (int i = 0; i < locationCount; i++)
{
float angle = Random.Range(0, 2 * Mathf.PI);
float r = Random.Range(minWorldRadius, maxWorldRadius);
initialWorld.Locations.Add(new Vector3(r * Mathf.Sin(angle), r * Mathf.Cos(angle), r * Mathf.Cos(angle)));
}
VertexProvider vProvider = new VertexProvider(targetObj.GetComponent <MeshFilter>().mesh, targetObj.transform);
initialWorld.Locations = vProvider.GetVertexSamples(1000);
sa = new SimulatedAnnealing(initialTemp, coolingRate, initialWorld);
sa.Findsolution();
// Prepare camera for render
Camera.main.clearFlags = CameraClearFlags.SolidColor;
Camera.main.backgroundColor = Color.white;
StartCoroutine(DontClearCamera());
}
private static void Main(string[] args)
{
// Load file with instances
if (args.Length != 1)
{
Console.WriteLine("Wrong number of arguments! Second argument has to be file with instances!");
return;
}
var files = Directory.GetFiles(args[0], "*.txt");
var instances = new List <Formula>();
foreach (var file in files)
{
var formula = Reader.LoadInstance(file);
if (!formula.CheckInstance())
{
Console.WriteLine("Formula wrongly loaded! (count of variables of clauses not match)");
continue;
}
instances.Add(formula);
}
// INIT TEMP
for (var i = 100; i <= 2500; i += 600)
{
long time = 0;
foreach (var formula in instances)
{
var sw = new Stopwatch();
sw.Start();
var sa = new SimulatedAnnealing(i, 10, 0.95, 400);
sa.Solve(formula);
sw.Stop();
time += sw.ElapsedMilliseconds;
}
Console.WriteLine("INIT TEMP: " + i);
Console.WriteLine("TIME: " + Math.Round((double)time / instances.Count, 4));
}
Console.WriteLine();
// INSIDE CYCLE
for (var i = 100; i <= 500; i += 100)
{
long time = 0;
foreach (var formula in instances)
{
var sw = new Stopwatch();
sw.Start();
var sa = new SimulatedAnnealing(500, 10, 0.95, i);
sa.Solve(formula);
sw.Stop();
time += sw.ElapsedMilliseconds;
}
Console.WriteLine("CYCLE: " + i);
Console.WriteLine("TIME: " + Math.Round((double)time / instances.Count, 4));
}
Console.WriteLine();
// COEFF. COOLING
for (var i = 0.55; i < 1; i += 0.1)
{
long time = 0;
foreach (var formula in instances)
{
var sw = new Stopwatch();
sw.Start();
var sa = new SimulatedAnnealing(500, 10, i, 400);
sa.Solve(formula);
sw.Stop();
time += sw.ElapsedMilliseconds;
}
Console.WriteLine("CC: " + i);
Console.WriteLine("TIME: " + Math.Round((double)time / instances.Count, 4));
}
/*
* var inst = instances[0];
* // solve formula with brute-force
* //var bruteResult = BruteForce.Solve(inst);
*
* //Console.WriteLine(bruteResult);
*
* // solve formula with simulated annealing
* var sw = new Stopwatch();
* sw.Start();
* var saSolver = new SimulatedAnnealing();
* var saResult = saSolver.Solve(inst);
* sw.Stop();
*
* Console.WriteLine(saResult);
* Console.WriteLine(sw.ElapsedMilliseconds);
*/
}
public async Task <IActionResult> Index(ICollection <IFormFile> files, UserInput u)
{
/* Uploader File */
user_input = u;
var uploads = Path.Combine(_environment.WebRootPath, "uploads");
foreach (var file in files)
{
if (file.Length > 0)
{
uploads = Path.Combine(uploads, file.FileName);
using (var fileStream = new FileStream(uploads, FileMode.Create))
{
await file.CopyToAsync(fileStream);
}
}
}
var openFile = new FileStream(uploads, FileMode.Open, FileAccess.Read);
using (var readStream = new StreamReader(openFile))
{
int conflict = 0;
string line;
Boolean isRuangan = false;
Boolean isKelas = false;
string temp = "";
char b;
RuanganManagement listOfRuangan = new RuanganManagement();
KelasManagement listOfKelas = new KelasManagement();
/* ADT RUANGAN */
string nama = ""; // Contoh: "7602"
int jam_buka = 0; // Contoh: 7 (buka mulai jam 7)
int jam_tutup = 0; // Contoh: 14 (tutup jam 14)
List <int> hari_buka = new List <int>();
/* ADT KELAS */
// Domain value yang dimiliki 'Kelas'
string ruangan = "";
List <string> domainRuangan = new List <string>();
List <int> domainMulai = new List <int>(); // Contoh: [7, 8, 9] (Kelas hanya bisa mulai jam 7, 8, atau 9)
List <int> domainHari = new List <int>(); // Contoh: [1, 3, 5] (Kelas hanya bisa dilakukan hari Senin, Rabu, atau Jumat)
int durasi = 0; // Contoh: 4 (4 jam)
while ((line = readStream.ReadLine()) != null)
{
if (line.Equals(""))
{
// Do nothing
}
else if (line.Equals("Ruangan"))
{
isRuangan = true;
isKelas = false;
}
else if (line.Equals("Jadwal"))
{
isRuangan = false;
isKelas = true;
}
/* Membaca data ruangan */
else if (isRuangan)
{
var sr = new StringReader(line);
int point = 0;
temp = "";
hari_buka = new List <int>();
domainMulai = new List <int>();
for (int k = 0; k < line.Length; k++)
{
b = (char)sr.Read();
if (b == ';')
{
if (point == 0)
{
nama = temp;
}
else if (point == 1)
{
jam_buka = timeToInt(temp);
}
else if (point == 2)
{
jam_tutup = timeToInt(temp);
}
point++;
temp = "";
}
else
{
if (b != ',')
{
temp += b;
}
}
}
var parse = new StringReader(temp);
for (int j = 0; j < temp.Length; j++)
{
char val = (char)parse.Read();
hari_buka.Add(Int32.Parse(val.ToString()));
}
listOfRuangan.addRuangan(new Ruangan(nama, jam_buka, jam_tutup, hari_buka));
}
/* Menambah info kelas */
else if (isKelas)
{
var sr = new StringReader(line);
int point = 0;
temp = "";
hari_buka = new List <int>();
domainRuangan = new List <string>();
for (int k = 0; k < line.Length; k++)
{
b = (char)sr.Read();
if ((point == 1) && (b != ';'))
{
if (b == ',')
{
domainRuangan.Add(temp);
temp = "";
}
else
{
temp += b;
}
}
else if (b == ';')
{
if (point == 0)
{
nama = temp;
}
else if (point == 1)
{
domainRuangan.Add(temp);
}
else if (point == 2)
{
jam_buka = timeToInt(temp);
}
else if (point == 3)
{
jam_tutup = timeToInt(temp);
}
else if (point == 4)
{
durasi = Int32.Parse(temp);
}
point++;
temp = "";
}
else
{
if (b != ',')
{
temp += b;
}
}
}
var parse = new StringReader(temp);
for (int j = 0; j < temp.Length; j++)
{
char val = (char)parse.Read();
hari_buka.Add(Int32.Parse(val.ToString()));
}
Kelas c = new Kelas(nama, domainRuangan, jam_buka, jam_tutup, durasi, hari_buka, listOfRuangan);
listOfKelas.addKelas(c);
}
}
KelasManagement ans = new KelasManagement();
if (user_input.choice == 0)
{
HillClimbing hc = new HillClimbing(listOfKelas, listOfRuangan);
ans = hc.getSol();
ViewData["Choice"] = "Hill Climbing";
}
else if (user_input.choice == 1)
{
SimulatedAnnealing sa = new SimulatedAnnealing(listOfKelas, listOfRuangan);
sa.execute(1000, 0.9f);
ans = sa.getSol();
ViewData["Choice"] = "Simulated Annealing";
}
else if (user_input.choice == 2)
{
Population p = new Population();
p.generatePopulation(200, listOfKelas);
GeneticAlgorithm ga = new GeneticAlgorithm(p);
for (int ax = 0; ax < 100000; ax++)
{
if (ga.getSolution().getConflict() == 0)
{
break;
}
else
{
ga.crossover();
ga.mutation();
}
}
ans = ga.getSolution();
ViewData["Choice"] = "Genetic Algorithm";
}
ViewData["tes"] = ans.getConflict();
int i = 0;
// Inisialisasi
for (int j = 7; j < 18; j++)
{
for (int k = 1; k < 6; k++)
{
ViewData["marker" + j.ToString() + k.ToString()] = 5;
ViewData["nama" + j.ToString() + k.ToString()] = "";
ViewData["ruangan" + j.ToString() + k.ToString()] = "";
}
}
// Pengisian kelas di tabel
foreach (Kelas k in ans.getArrayKelas())
{
ViewData["nama" + i.ToString()] = k.getNama();
ViewData["ruangan" + i.ToString()] = k.getNamaRuangan();
ViewData["durasi" + i.ToString()] = k.getDurasi();
ViewData["hari" + i.ToString()] = k.getHari();
ViewData["jam" + i.ToString()] = k.getMulai();
for (int z = k.getMulai(); z < (k.getMulai() + k.getDurasi()); z++)
{
if (ViewData["ruangan" + z.ToString() + k.getHari().ToString()].Equals(""))
{
ViewData["kelas" + z.ToString() + k.getHari().ToString()] = i.ToString();
ViewData["marker" + z.ToString() + k.getHari().ToString()] = 0;
ViewData["nama" + z.ToString() + k.getHari().ToString()] = k.getNama();
ViewData["ruangan" + z.ToString() + k.getHari().ToString()] = k.getNamaRuangan();
//Kurangi waktu available dan tambahkan waktu terpakai;
listOfRuangan.getRuangan(k.getNamaRuangan()).decrement_waktu_available();
}
else if (cekRuanganSama(k.getNamaRuangan(), ViewData["ruangan" + z.ToString() + k.getHari().ToString()].ToString()))
{
conflict++;
ViewData["kelas" + z.ToString() + k.getHari().ToString()] = i.ToString();
ViewData["marker" + z.ToString() + k.getHari().ToString()] = 1;
ViewData["nama" + z.ToString() + k.getHari().ToString()] += " " + k.getNama();
ViewData["ruangan" + z.ToString() + k.getHari().ToString()] += " " + k.getNamaRuangan();
}
else
{
ViewData["kelas" + z.ToString() + k.getHari().ToString()] += " " + i.ToString();
ViewData["marker" + z.ToString() + k.getHari().ToString()] = 2;
ViewData["nama" + z.ToString() + k.getHari().ToString()] += " " + k.getNama();
ViewData["ruangan" + z.ToString() + k.getHari().ToString()] += " " + k.getNamaRuangan();
/* Kurangi waktu available dan tambahkan waktu terpakai*/
listOfRuangan.getRuangan(k.getNamaRuangan()).decrement_waktu_available();
}
}
i++;
}
ViewData["tes"] = conflict;
ViewData["Length"] = i;
/* Memasukkan data keefektifan untuk ditampilkan di web */
int nRuangan = 0;
foreach (Ruangan ruang_ruang in listOfRuangan.getAllRuangan())
{
ViewData["namaruangan" + nRuangan.ToString()] = ruang_ruang.getName();
ViewData["efektifitas" + nRuangan.ToString()] = ruang_ruang.getEfektifitasRuangan();
nRuangan++;
}
ViewData["banyakruangan"] = nRuangan;
}
ViewData["color0"] = "green";
ViewData["color1"] = "yellow";
ViewData["color2"] = "brown";
ViewData["color3"] = "blue";
ViewData["color4"] = "black";
ViewData["color5"] = "aqua";
ViewData["color6"] = "purple";
ViewData["color7"] = "wood";
ViewData["color8"] = "pink";
ViewData["color9"] = "orange";
ViewData["Start"] = 1;
return(View());
}
public ActionResult Result(TSPViewModel model)
{
var algorithmType = (TSPAlgorithm)Enum.Parse(
typeof(TSPAlgorithm),
model.SelectedPosition);
var crossoverType = (CrossoverType)Enum.Parse(
typeof(CrossoverType),
model.SelectedCrossoverPosition);
var selectionType = (SelectionType)Enum.Parse(
typeof(SelectionType),
model.SelectedSelectionPosition);
IAlgorithm algorithm = null;
List <City> cities;
if (!string.IsNullOrEmpty(model.Cities))
{
cities = new List <City>();
foreach (var line in model.Cities.Split('\n'))
{
var words = line.Split(';');
var city = new City()
{
Name = words[0],
X = double.Parse(words[1]),
Y = double.Parse(words[2])
};
cities.Add(city);
}
}
else if (model.RandomData)
{
cities = TSPCreatorHelper.GenerateCity(30);
}
else
{
cities = DemoTSPData.Get();
}
if (algorithmType == TSPAlgorithm.GeneticAlgorithm)
{
algorithm = new GeneticAlgorithm(
model.MutatorValue,
model.GenerationCount,
model.PopulationSize,
crossoverType,
selectionType,
cities);
}
else if (algorithmType == TSPAlgorithm.SimulatedAnnealing)
{
algorithm = new SimulatedAnnealing(
model.Temperature,
model.CoolingRate,
model.Number,
cities);
}
else
{
throw new Exception();
}
algorithm.Execute();
var result = new TSPResultViewModel()
{
Result = algorithm.GetResult(),
Cities = TourManager.Cities
};
return(View(result));
}
public async Task CalculateSA()
{
ClearTextBox();
cts[3].Cancel();
cts[3] = new CancellationTokenSource();
progressSA.Visibility = Visibility.Visible;
double temperature;
if (!Double.TryParse(textB_temperature.Text, out temperature))
{
textB_temperature.Background = Brushes.Coral;
errorTB.Push(textB_temperature);
return;
}
double absTemperature;
if (!Double.TryParse(textB_absoluteTemperature.Text, out absTemperature))
{
textB_absoluteTemperature.Background = Brushes.Coral;
errorTB.Push(textB_absoluteTemperature);
return;
}
double coolingRate;
if (!Double.TryParse(textB_coolingRate.Text, out coolingRate))
{
textB_coolingRate.Background = Brushes.Coral;
errorTB.Push(textB_coolingRate);
return;
}
SimulatedAnnealing algorithm = null;
Stopwatch time = null;
int[] solve = null;
Task thisTask = (Task.Run(() =>
{
algorithm = new SimulatedAnnealing(temperature, absTemperature, coolingRate);
time = new Stopwatch();
time.Start();
solve = algorithm.Solution(cities);
time.Stop();
}));
tasks.Enqueue(thisTask);
await Task.Run(() =>
{
while (true)
{
if (cts[3].Token.IsCancellationRequested || thisTask.IsCompleted)
{
break;
}
}
});
if (solve != null)
{
DrawLines(solve, graphs[3]);
timeSA.Content = time.ElapsedMilliseconds.ToString();
lengthSA.Content = algorithm.TotalDistance.ToString("F2");
}
progressSA.Visibility = Visibility.Hidden;
}
private static void Main()
{
Parameters.Verbosity = VerbosityLevels.Normal;
// this next line is to set the Debug statements from OOOT to the Console.
Trace.Listeners.Add(new TextWriterTraceListener(Console.Out));
/* In this example, we first present how the details of an optimzation
* problem can be saved to an XML-file so that it can be read in
* and solved as opposed to defining all the details in an imperative
* (code line by code line) way. In the first function, the xml file
* name "test1.xml" is created. */
makeAndSaveProblemDefinition();
/* now we create a series of different optimization methods and test
* them on the problem. The problem is now opened from the file and
* the details are stored in an object of class "Problem Definition".*/
var stream = new FileStream(filename, FileMode.Open);
double[] xStar;
ProblemDefinition probTest1 = ProblemDefinition.OpenprobFromXml(stream);
abstractOptMethod opty;
/******************Exhaustive Search ***********************/
//SearchIO.output("******************Exhaustive Search ***********************");
//Console.ReadKey();
//opty = new ExhaustiveSearch(probTest1.SpaceDescriptor, optimize.minimize);
//opty.Add(probTest1);
///* No convergence criteria is needed as the process concludes when all
// * states have been visited but for this problem that is 4 trillion states.*/
//opty.ConvergenceMethods.Clear();
///* if you DID KNOW the best, you can include a criteria like...*/
//opty.ConvergenceMethods.Add(new ToKnownBestXConvergence(new[] { 3.0, 3.0 }, 0.0000001));
//var timer = Stopwatch.StartNew();
//var fStar = opty.Run(out xStar);
///* you probably will never see this process complete. Even with the added
// * convergence criteria (which is not factored into the estimated time of
// * completion), you are probably looking at 1 to 2 years. */
//printResults(opty, xStar, fStar, timer);
/***********Gradient Based Optimization with Steepest Descent****************/
//SearchIO.output("***********Gradient Based Optimization with Steepest Descent****************");
//Console.ReadKey();
//opty = new GradientBasedOptimization();
//opty.Add(probTest1);
//abstractSearchDirection searchDirMethod = new SteepestDescent();
//opty.Add(searchDirMethod);
////abstractLineSearch lineSearchMethod = new ArithmeticMean(0.0001, 1, 100);
////abstractLineSearch lineSearchMethod = new DSCPowell(0.0001, 1, 100);
//abstractLineSearch lineSearchMethod = new GoldenSection(0.0001, 1);
//opty.Add(lineSearchMethod);
//opty.Add(new squaredExteriorPenalty(opty, 10));
///* since this is not a population-based optimization method, we need to remove the MaxSpan criteria. */
//opty.ConvergenceMethods.RemoveAll(a => a is MaxSpanInPopulationConvergence);
//timer = Stopwatch.StartNew();
//fStar = opty.Run(out xStar);
//printResults(opty, xStar, fStar, timer);
///***********Gradient Based Optimization with Fletcher-Reeves****************/
//SearchIO.output("***********Gradient Based Optimization with Fletcher-Reeves****************");
//Console.ReadKey();
///* we don't need to reset (invoke the constructor) for GradientBasedOptimization since we are only
// * change the seaach direction method. */
//searchDirMethod = new FletcherReevesDirection();
///* you could also try the remaining 3 search direction methods. */
////searchDirMethod = new CyclicCoordinates();
////searchDirMethod = new BFGSDirection();
////searchDirMethod = new PowellMethod(0.001, 6);
//opty.Add(searchDirMethod);
//timer = Stopwatch.StartNew();
//opty.ResetFunctionEvaluationDatabase();
//fStar = opty.Run(out xStar);
//printResults(opty, xStar, fStar, timer);
///******************Generalized Reduced Gradient***********************/
//SearchIO.output("******************Generalized Reduced Gradient***********************");
//Console.ReadKey();
//opty = new GeneralizedReducedGradientActiveSet();
//opty.Add(probTest1);
//opty.Add(new squaredExteriorPenalty(opty, 10));
//opty.ConvergenceMethods.RemoveAll(a => a is MaxSpanInPopulationConvergence);
//timer = Stopwatch.StartNew();
//fStar = opty.Run(out xStar);
//printResults(opty, xStar, fStar, timer);
/* GRG is the ONLY one here that handles constraints explicity. It find the
* optimal very quickly and accurately. However, many of the other show a
* better value of f*, this is because they are using an imperfect penalty
* function (new squaredExteriorPenalty(opty, 10)). While it seems that GRG
* includes it as well, it is only used in the the line search method. */
/******************Random Hill Climbing ***********************/
probTest1.SpaceDescriptor = new DesignSpaceDescription(new[] { new VariableDescriptor(-5000, 5000, 0.1),
new VariableDescriptor(-5000, 5000, 0.1) });
SearchIO.output("******************Random Hill Climbing ***********************");
Console.ReadKey();
opty = new HillClimbing();
opty.Add(probTest1);
opty.Add(new squaredExteriorPenalty(opty, 8));
opty.Add(new RandomNeighborGenerator(probTest1.SpaceDescriptor));
opty.Add(new KeepSingleBest(optimize.minimize));
opty.ConvergenceMethods.RemoveAll(a => a is MaxSpanInPopulationConvergence);
/* the deltaX convergence needs to be removed as well, since RHC will end many iterations
* at the same point it started. */
opty.ConvergenceMethods.RemoveAll(a => a is DeltaXConvergence);
var timer = Stopwatch.StartNew();
var fStar = opty.Run(out xStar);
printResults(opty, xStar, fStar, timer);
/******************Exhaustive Hill Climbing ***********************/
SearchIO.output("******************Exhaustive Hill Climbing ***********************");
Console.ReadKey();
/* Everything else about the Random Hill Climbing stays the same. */
opty.Add(new ExhaustiveNeighborGenerator(probTest1.SpaceDescriptor));
timer = Stopwatch.StartNew();
fStar = opty.Run(out xStar);
printResults(opty, xStar, fStar, timer);
/******************Simulated Annealing***********************/
SearchIO.output("******************Simulated Annealing***********************");
Console.ReadKey();
opty = new SimulatedAnnealing(optimize.minimize);
opty.Add(probTest1);
opty.Add(new squaredExteriorPenalty(opty, 10));
opty.Add(new RandomNeighborGenerator(probTest1.SpaceDescriptor, 100));
opty.Add(new SACoolingSangiovanniVincentelli(100));
opty.ConvergenceMethods.RemoveAll(a => a is MaxSpanInPopulationConvergence);
/* the deltaX convergence needs to be removed as well, since RHC will end many iterations
* at the same point it started. */
opty.ConvergenceMethods.RemoveAll(a => a is DeltaXConvergence);
timer = Stopwatch.StartNew();
fStar = opty.Run(out xStar);
printResults(opty, xStar, fStar, timer);
}
public void NelderOtherHeuristics()
{
int thinningPeriod = 4;
int treeCount = 75;
#if DEBUG
treeCount = 25;
#endif
PlotsWithHeight nelder = PublicApi.GetNelder();
OrganonConfiguration configuration = OrganonTest.CreateOrganonConfiguration(new OrganonVariantNwo());
configuration.Treatments.Harvests.Add(new ThinByIndividualTreeSelection(thinningPeriod));
OrganonStand stand = nelder.ToOrganonStand(configuration, 20, 130.0F, treeCount);
stand.PlantingDensityInTreesPerHectare = TestConstant.NelderReplantingDensityInTreesPerHectare;
Objective landExpectationValue = new Objective()
{
IsLandExpectationValue = true,
PlanningPeriods = 9
};
Objective volume = new Objective()
{
PlanningPeriods = landExpectationValue.PlanningPeriods
};
HeuristicParameters defaultParameters = new HeuristicParameters()
{
UseScaledVolume = false
};
GeneticParameters geneticParameters = new GeneticParameters(treeCount)
{
PopulationSize = 7,
MaximumGenerations = 5,
UseScaledVolume = defaultParameters.UseScaledVolume
};
GeneticAlgorithm genetic = new GeneticAlgorithm(stand, configuration, landExpectationValue, geneticParameters);
TimeSpan geneticRuntime = genetic.Run();
GreatDeluge deluge = new GreatDeluge(stand, configuration, volume, defaultParameters)
{
RainRate = 5,
LowerWaterAfter = 9,
StopAfter = 10
};
deluge.RandomizeTreeSelection(TestConstant.Default.SelectionPercentage);
TimeSpan delugeRuntime = deluge.Run();
RecordTravel recordTravel = new RecordTravel(stand, configuration, landExpectationValue, defaultParameters)
{
StopAfter = 10
};
recordTravel.RandomizeTreeSelection(TestConstant.Default.SelectionPercentage);
TimeSpan recordRuntime = recordTravel.Run();
SimulatedAnnealing annealer = new SimulatedAnnealing(stand, configuration, volume, defaultParameters)
{
Iterations = 100
};
annealer.RandomizeTreeSelection(TestConstant.Default.SelectionPercentage);
TimeSpan annealerRuntime = annealer.Run();
TabuParameters tabuParameters = new TabuParameters()
{
UseScaledVolume = defaultParameters.UseScaledVolume
};
TabuSearch tabu = new TabuSearch(stand, configuration, landExpectationValue, tabuParameters)
{
Iterations = 7,
//Jump = 2,
MaximumTenure = 5
};
tabu.RandomizeTreeSelection(TestConstant.Default.SelectionPercentage);
TimeSpan tabuRuntime = tabu.Run();
ThresholdAccepting thresholdAcceptor = new ThresholdAccepting(stand, configuration, volume, defaultParameters);
thresholdAcceptor.IterationsPerThreshold.Clear();
thresholdAcceptor.Thresholds.Clear();
thresholdAcceptor.IterationsPerThreshold.Add(10);
thresholdAcceptor.Thresholds.Add(1.0F);
thresholdAcceptor.RandomizeTreeSelection(TestConstant.Default.SelectionPercentage);
TimeSpan acceptorRuntime = thresholdAcceptor.Run();
RandomGuessing random = new RandomGuessing(stand, configuration, volume, defaultParameters)
{
Iterations = 4
};
TimeSpan randomRuntime = random.Run();
configuration.Treatments.Harvests.Clear();
configuration.Treatments.Harvests.Add(new ThinByPrescription(thinningPeriod));
PrescriptionParameters prescriptionParameters = new PrescriptionParameters()
{
Maximum = 60.0F,
Minimum = 50.0F,
Step = 10.0F,
UseScaledVolume = defaultParameters.UseScaledVolume
};
PrescriptionEnumeration enumerator = new PrescriptionEnumeration(stand, configuration, landExpectationValue, prescriptionParameters);
TimeSpan enumerationRuntime = enumerator.Run();
// heuristics assigned to volume optimization
this.Verify(deluge);
this.Verify(annealer);
this.Verify(thresholdAcceptor);
this.Verify(random);
// heuristics assigned to net present value optimization
this.Verify(genetic);
this.Verify(enumerator);
this.Verify(recordTravel);
this.Verify(tabu);
HeuristicSolutionDistribution distribution = new HeuristicSolutionDistribution(1, thinningPeriod, treeCount);
distribution.AddRun(annealer, annealerRuntime, defaultParameters);
distribution.AddRun(deluge, delugeRuntime, defaultParameters);
distribution.AddRun(thresholdAcceptor, acceptorRuntime, defaultParameters);
distribution.AddRun(genetic, geneticRuntime, defaultParameters);
distribution.AddRun(enumerator, enumerationRuntime, defaultParameters);
distribution.AddRun(recordTravel, recordRuntime, defaultParameters);
distribution.AddRun(tabu, tabuRuntime, defaultParameters);
distribution.AddRun(random, randomRuntime, defaultParameters);
distribution.OnRunsComplete();
}