public static void Main()
{
// define cost matrix
Double INF = Double.PositiveInfinity;
/* Double[,] costMatrix = new Double[,]
{
{ INF, 2.0, 4.0, 22.0, 2.0, INF},
{ 2.0, INF, 8.0, 15.0, 13.0, 10.0},
{ 4.0, 8.0, INF, 5.0, 2.0, INF},
{22.0, 15.0, 5.0, INF, 11.0, 12.0},
{ 2.0, 13.0, 2.0, 11.0, INF, 14.0},
{ INF, 10.0, INF, 12.0, 14.0, INF},
};
*/
Double[,] costMatrix = new Double[,]
{
{ INF, 2.0, INF, INF, 7.0},
{ 2.0, INF, 1.0, 3.0, INF},
{ INF, 1.0, INF, 1.0, 5.0},
{ INF, 3.0, 1.0, INF, INF},
{ 7.0, INF, 5.0, INF, INF},
};
costMatrix = new Double[,]
{
{ INF, 20.0, INF, 70.0},
{ 20.0, INF, 40.0, INF},
{ INF, 40.0, INF,50.0},
{ 70.0, INF, 50.0,INF},
};
// create prototype chromosome
PermutationChromosome prototype = new PermutationChromosome(0, costMatrix.GetLength(0) - 1);
// set prototype's parameters
prototype.CrossOverStrategy = new PermutationChromosome.CycleCrossOverStrategy();
prototype.RandomGenerator = new ThreadSafeRandomGenerator();
prototype.MutationStrategy = new PermutationChromosome.InsertMutationStrategy();
prototype.Fitness = new TSPFitness(costMatrix);
// stop condition, keep reference for selecting the leader
NoChangeStopCondion stopCondition = new NoChangeStopCondion(10);
// create population
DefaultPopulation population = new DefaultPopulation(prototype, 40);
// set population's parameters
population.SelectionStrategy
= new StochasticUniversalSamplingStrategy(new FixedSizeStrategy(40), new ThreadSafeRandomGenerator());
population.RandomGenerator = new ThreadSafeRandomGenerator();
population.StopCondition = stopCondition;
// perform util the stop condition returns false
while (population.NextGeneration())
;
// print the result
System.Console.WriteLine("Best fitness: " + (1.0 / stopCondition.Leader.Evaluate()));
System.Console.WriteLine(stopCondition.Leader.ToString());
System.Console.ReadKey();
}
/// <summary>
/// Realizuje algorytm mutacji na zadanym chromosomie.
/// </summary>
/// <param name="chromosome">Chromosom, który zostanie poddany mutacji.</param>
public void Mutate(PermutationChromosome chromosome)
{
Int32 mutationPoint1 = chromosome.RandomGenerator.Next(0, chromosome.Data.Length);
Int32 mutationPoint2 = chromosome.RandomGenerator.Next(0, chromosome.Data.Length);
if (mutationPoint1 != mutationPoint2)
{
Int32 mpLower = Math.Min(mutationPoint1, mutationPoint2);
Int32 mpUpper = Math.Max(mutationPoint1, mutationPoint2);
Array.Reverse(chromosome.Data, mpLower, mpUpper - mpLower);
}
}
/// <summary>
/// Dokonuje oceny dopasowania.
/// </summary>
/// <param name="chromosome">Oceniany chromosom.</param>
/// <returns>Ocena dopasowania.</returns>
public double Evaluate(PermutationChromosome chromosome)
{
Int32[] permutation = chromosome.Data;
#if DEBUG
Debug.Assert(costMatrix.GetLength(0) == costMatrix.GetLength(1));
Debug.Assert(costMatrix.GetLength(0) == permutation.Length);
Debug.Assert(permutation.Min() == 0);
Debug.Assert(permutation.Max() == permutation.Length - 1);
#endif
Double cost = 0.0;
for (Int32 i = 1; i < permutation.Length; ++i)
{
cost += costMatrix[permutation[i - 1], permutation[i]];
}
cost += costMatrix[permutation[permutation.Length - 1], permutation[0]];
return Double.IsInfinity(cost) ? 0.0 : 1.0 / cost;
}
/// <summary>
/// Realizuje algorytm mutacji na zadanym chromosomie.
/// </summary>
/// <param name="chromosome">Chromosom, który zostanie poddany mutacji.</param>
public void Mutate(PermutationChromosome chromosome)
{
Int32 mutationPoint1 = chromosome.RandomGenerator.Next(0, chromosome.Data.Length);
Int32 mutationPoint2 = chromosome.RandomGenerator.Next(0, chromosome.Data.Length);
if (mutationPoint1 != mutationPoint2)
{
Int32 mpLower = Math.Min(mutationPoint1, mutationPoint2);
Int32 mpUpper = Math.Max(mutationPoint1, mutationPoint2);
for (Int32 i = mpLower; i < mpUpper; ++i)
{
Int32 value = chromosome.Data[i];
Int32 point = chromosome.RandomGenerator.Next(i, mpUpper);
chromosome.Data[i] = chromosome.Data[point];
chromosome.Data[point] = value;
}
}
}
/// <summary>
/// Krzyżuje dwa osobniki modyfikując je.
/// </summary>
/// <param name="ch1">Osobnik pierwszy.</param>
/// <param name="ch2">Osobnik drugi.</param>
public void CrossOver(PermutationChromosome ch1, PermutationChromosome ch2)
{
#if DEBUG
Debug.Assert(ch1.RandomGenerator.GetType() == ch2.RandomGenerator.GetType());
Debug.Assert(ch1.CrossOverStrategy.GetType() == ch2.CrossOverStrategy.GetType());
Debug.Assert(ch1.HasSameData(ch2));
#endif
Int32[] child1 = new Int32[ch1.Data.Length];
Int32[] child2 = new Int32[ch2.Data.Length];
Boolean[] visited = new Boolean[ch2.Data.Length];
Int32 pos = 0;
do
{
// odd circle
pos = Array.IndexOf(visited, false);
while (pos >= 0 && false == visited[pos])
{
visited[pos] = true;
child1[pos] = ch1.Data[pos];
child2[pos] = ch2.Data[pos];
pos = Array.IndexOf(ch1.Data, ch2.Data[pos]);
}
// even circle
pos = Array.IndexOf(visited, false);
while (pos >= 0 && false == visited[pos])
{
visited[pos] = true;
child1[pos] = ch2.Data[pos];
child2[pos] = ch1.Data[pos];
pos = Array.IndexOf(ch2.Data, ch1.Data[pos]);
}
} while (Array.IndexOf(visited, false) >= 0);
ch1.Data = child1;
ch2.Data = child2;
}
/// <summary>
/// Krzyżuje dwa osobniki modyfikując je.
/// </summary>
/// <param name="ch1">Osobnik pierwszy.</param>
/// <param name="ch2">Osobnik drugi.</param>
public void CrossOver(PermutationChromosome ch1, PermutationChromosome ch2)
{
#if DEBUG
Debug.Assert(ch1.RandomGenerator.GetType() == ch2.RandomGenerator.GetType());
Debug.Assert(ch1.CrossOverStrategy.GetType() == ch2.CrossOverStrategy.GetType());
Debug.Assert(ch1.HasSameData(ch2));
#endif
Int32 crossOverPoint1 = ch1.RandomGenerator.Next(0, ch1.Data.Length);
Int32 crossOverPoint2 = ch1.RandomGenerator.Next(0, ch1.Data.Length);
if (crossOverPoint1 != crossOverPoint2)
{
Int32 copLower = Math.Min(crossOverPoint1, crossOverPoint2);
Int32 copUpper = Math.Max(crossOverPoint1, crossOverPoint2);
Int32[] data1 = PartiallyMappedCrossOver(ch1.Data, ch2.Data, copLower, copUpper);
Int32[] data2 = PartiallyMappedCrossOver(ch2.Data, ch1.Data, copLower, copUpper);
ch1.Data = data1;
ch2.Data = data2;
}
}
/// <summary>
/// Realizuje algorytm mutacji na zadanym chromosomie.
/// </summary>
/// <param name="chromosome">Chromosom, który zostanie poddany mutacji.</param>
public void Mutate(PermutationChromosome chromosome)
{
Int32 mutationPoint1 = chromosome.RandomGenerator.Next(0, chromosome.Data.Length);
Int32 mutationPoint2 = chromosome.RandomGenerator.Next(0, chromosome.Data.Length);
if (mutationPoint1 != mutationPoint2)
{
Int32 mpLower = Math.Min(mutationPoint1, mutationPoint2);
Int32 mpUpper = Math.Max(mutationPoint1, mutationPoint2);
Int32 mutationValueLower = chromosome.Data[mpLower];
Int32 mutationValueUpper = chromosome.Data[mpUpper];
Int32 position = mpUpper;
while ((--position) > mpLower)
{
chromosome.Data[position + 1] = chromosome.Data[position];
}
chromosome.Data[mpLower + 0] = mutationValueLower;
chromosome.Data[mpLower + 1] = mutationValueUpper;
}
}
/// <summary>
/// Porównuje allele dwóch chromosomów.
/// </summary>
/// <param name="chromosome">Chromosom, z którym jest realizowane porównywanie.</param>
/// <returns>True, jeżeli oba chromosomy mają takie same allele na odpowiadających sobie pozycjach.</returns>
private bool HasSameData(PermutationChromosome chromosome)
{
foreach (Int32 value in chromosome.Data)
{
if (false == Data.Contains(value))
{
return false;
}
}
return true;
}
/// <summary>
/// Kopiuje instancję obiektu chromosomu.
/// </summary>
/// <returns></returns>
public IChromosome Clone()
{
PermutationChromosome chromosome = new PermutationChromosome(Data);
chromosome.RandomGenerator = RandomGenerator;
chromosome.MutationStrategy = MutationStrategy;
chromosome.CrossOverStrategy = CrossOverStrategy;
chromosome.Fitness = Fitness;
return chromosome;
}
private void startProcessingButton_Click(object sender, EventArgs e)
{
if (worker.IsBusy)
{
worker.CancelAsync();
return;
}
if (costMatrix == null)
{
MessageBox.Show("Macierz jest pusta. Uzupełnij graf.");
return;
}
/*
* Prototyp chromosomu.
*/
PermutationChromosome chromosomePrototype = new PermutationChromosome(0, costMatrix.GetLength(0) - 1);
chromosomePrototype.MutationStrategy = (PermutationChromosome.IMutationStrategy)mutationAlgorithmComboBox.SelectedItem;
chromosomePrototype.CrossOverStrategy = (PermutationChromosome.ICrossOverStrategy)crossoverAlgorithmComboBox.SelectedItem;
chromosomePrototype.RandomGenerator = new ThreadSafeRandomGenerator();
chromosomePrototype.Fitness = new TSPFitness(costMatrix);
/*
* Populacja
*/
population = new DefaultPopulation(chromosomePrototype, (UInt32)populationSpinner.Value);
population.RandomGenerator = new ThreadSafeRandomGenerator();
population.StopCondition = stopCondition;
/*
* Selekcja
*/
ISelectionSizeStrategy selectionSizeStrategy = selectionSizeCheckBox.Checked ? (ISelectionSizeStrategy)new ProportionalSizeStrategy((Double)selectionSizeSpinner.Value) : (ISelectionSizeStrategy)new FixedSizeStrategy((UInt32)selectionSizeSpinner.Value);
switch ((SelectionStrategies)selectionAlgorithmComboBox.SelectedValue)
{
case SelectionStrategies.NoSelectionStrategy:
population.SelectionStrategy = new NoSelectionStrategy();
break;
case SelectionStrategies.RouletteWheelSelectionStrategy:
population.SelectionStrategy = new RouletteWheelSelectionStrategy(selectionSizeStrategy, new ThreadSafeRandomGenerator());
break;
case SelectionStrategies.StochasticUniversalSamplingStrategy:
population.SelectionStrategy = new StochasticUniversalSamplingStrategy(selectionSizeStrategy, new ThreadSafeRandomGenerator());
break;
case SelectionStrategies.TournamentSelectionStrategy:
ISelectionSizeStrategy tournamentGroupSizeStrategy = tournamentGroupSizeCheckBox.Checked ? (ISelectionSizeStrategy)new ProportionalSizeStrategy((Double)tournamentGroupSizeSpinner.Value) : (ISelectionSizeStrategy)new FixedSizeStrategy((UInt32)tournamentGroupSizeSpinner.Value);
population.SelectionStrategy = new TournamentSelectionStrategy(selectionSizeStrategy, tournamentGroupSizeStrategy, new ThreadSafeRandomGenerator());
break;
default:
return;
}
ISelectionSizeStrategy eliteSizeStrategy = eliteSizeCheckBox.Checked ? (ISelectionSizeStrategy)new ProportionalSizeStrategy((Double)eliteSizeSpinner.Value) : (ISelectionSizeStrategy)new FixedSizeStrategy((UInt32)eliteSizeSpinner.Value);
ISelectionSizeStrategy survivorSizeStrategy = eliteSizeCheckBox.Checked ? (ISelectionSizeStrategy)new ProportionalSizeStrategy((Double)survivorSizeSpinner.Value) : (ISelectionSizeStrategy)new FixedSizeStrategy((UInt32)survivorSizeSpinner.Value);
switch ((ModifierCombination)selecitonModifierComboBox.SelectedValue)
{
case ModifierCombination.None:
break;
case ModifierCombination.Elite:
population.SelectionStrategy = new EliteSelectionStrategyAdapter(population.SelectionStrategy, eliteSizeStrategy);
break;
case ModifierCombination.Survivor:
population.SelectionStrategy = new SurvivorSelectionStrategyAdapter(population.SelectionStrategy, survivorSizeStrategy);
break;
case ModifierCombination.EliteSurvivor:
population.SelectionStrategy = new EliteSelectionStrategyAdapter(population.SelectionStrategy, eliteSizeStrategy);
population.SelectionStrategy = new SurvivorSelectionStrategyAdapter(population.SelectionStrategy, survivorSizeStrategy);
break;
case ModifierCombination.SurvivorElite:
population.SelectionStrategy = new SurvivorSelectionStrategyAdapter(population.SelectionStrategy, survivorSizeStrategy);
population.SelectionStrategy = new EliteSelectionStrategyAdapter(population.SelectionStrategy, eliteSizeStrategy);
break;
}
/*
* Warunek stopu. Zachować referencję dla wskazania lidera.
*/
stopCondition = new NoChangeStopCondion((UInt32)stopSpinner.Value);
population.StopCondition = stopCondition;
/*
* Rozpocznij przetwarzanie.
*/
startProcessingButton.Text = "Stop";
TimeStart = DateTime.Now;
worker.RunWorkerAsync(stopCondition);
}