private AlpsGeneticAlgorithm CreateAlpsGaTspSample() {
AlpsGeneticAlgorithm alpsGa = new AlpsGeneticAlgorithm();
#region Problem Configuration
var provider = new TSPLIBTSPInstanceProvider();
var instance = provider.GetDataDescriptors().Single(x => x.Name == "ch130");
TravelingSalesmanProblem tspProblem = new TravelingSalesmanProblem();
tspProblem.Load(provider.LoadData(instance));
tspProblem.UseDistanceMatrix.Value = true;
#endregion
#region Algorithm Configuration
alpsGa.Name = "ALPS Genetic Algorithm - TSP";
alpsGa.Description = "An age-layered population structure genetic algorithm which solves the \"ch130\" traveling salesman problem (imported from TSPLIB)";
alpsGa.Problem = tspProblem;
SamplesUtils.ConfigureAlpsGeneticAlgorithmParameters<GeneralizedRankSelector, MultiPermutationCrossover, InversionManipulator>(alpsGa,
numberOfLayers: 1000,
popSize: 100,
mutationRate: 0.05,
elites: 1,
plusSelection: true,
agingScheme: AgingScheme.Polynomial,
ageGap: 20,
ageInheritance: 1.0,
maxGens: 1000);
var checkedCrossovers = new[] { typeof(EdgeRecombinationCrossover), typeof(MaximalPreservativeCrossover), typeof(OrderCrossover2) };
var multiCrossover = (MultiPermutationCrossover)alpsGa.Crossover;
var crossovers = multiCrossover.Operators.Where(c => checkedCrossovers.Any(cc => cc.IsInstanceOfType(c))).ToList();
foreach (var c in multiCrossover.Operators)
multiCrossover.Operators.SetItemCheckedState(c, crossovers.Contains(c));
#endregion
return alpsGa;
}
private AlpsGeneticAlgorithm CreateAlpsGaTspSample()
{
AlpsGeneticAlgorithm alpsGa = new AlpsGeneticAlgorithm();
#region Problem Configuration
var provider = new TSPLIBTSPInstanceProvider();
var instance = provider.GetDataDescriptors().Single(x => x.Name == "ch130");
TravelingSalesmanProblem tspProblem = new TravelingSalesmanProblem();
tspProblem.Load(provider.LoadData(instance));
tspProblem.UseDistanceMatrix.Value = true;
#endregion
#region Algorithm Configuration
alpsGa.Name = "ALPS Genetic Algorithm - TSP";
alpsGa.Description = "An age-layered population structure genetic algorithm which solves the \"ch130\" traveling salesman problem (imported from TSPLIB)";
alpsGa.Problem = tspProblem;
SamplesUtils.ConfigureAlpsGeneticAlgorithmParameters <GeneralizedRankSelector, MultiPermutationCrossover, InversionManipulator>(alpsGa,
numberOfLayers: 1000,
popSize: 100,
mutationRate: 0.05,
elites: 1,
plusSelection: true,
agingScheme: AgingScheme.Polynomial,
ageGap: 20,
ageInheritance: 1.0,
maxGens: 1000);
var checkedCrossovers = new[] { typeof(EdgeRecombinationCrossover), typeof(MaximalPreservativeCrossover), typeof(OrderCrossover2) };
var multiCrossover = (MultiPermutationCrossover)alpsGa.Crossover;
var crossovers = multiCrossover.Operators.Where(c => checkedCrossovers.Any(cc => cc.IsInstanceOfType(c))).ToList();
foreach (var c in multiCrossover.Operators)
{
multiCrossover.Operators.SetItemCheckedState(c, crossovers.Contains(c));
}
#endregion
return(alpsGa);
}
private TabuSearch CreateTabuSearchTspSample()
{
TabuSearch ts = new TabuSearch();
#region Problem Configuration
var provider = new TSPLIBTSPInstanceProvider();
var instance = provider.GetDataDescriptors().Where(x => x.Name == "ch130").Single();
TravelingSalesmanProblem tspProblem = new TravelingSalesmanProblem();
tspProblem.Load(provider.LoadData(instance));
tspProblem.UseDistanceMatrix.Value = true;
#endregion
#region Algorithm Configuration
ts.Name = "Tabu Search - TSP";
ts.Description = "A tabu search algorithm that solves the \"ch130\" TSP (imported from TSPLIB)";
ts.Problem = tspProblem;
ts.MaximumIterations.Value = 1000;
// move generator has to be set first
var moveGenerator = ts.MoveGeneratorParameter.ValidValues
.OfType <StochasticInversionMultiMoveGenerator>()
.Single();
ts.MoveGenerator = moveGenerator;
var moveEvaluator = ts.MoveEvaluatorParameter.ValidValues
.OfType <TSPInversionMoveRoundedEuclideanPathEvaluator>()
.Single();
ts.MoveEvaluator = moveEvaluator;
var moveMaker = ts.MoveMakerParameter.ValidValues
.OfType <InversionMoveMaker>()
.Single();
ts.MoveMaker = moveMaker;
ts.SampleSize.Value = 750;
ts.Seed.Value = 0;
ts.SetSeedRandomly.Value = true;
var tabuChecker = ts.TabuCheckerParameter.ValidValues
.OfType <InversionMoveSoftTabuCriterion>()
.Single();
tabuChecker.UseAspirationCriterion.Value = true;
ts.TabuChecker = tabuChecker;
var tabuMaker = ts.TabuMakerParameter.ValidValues
.OfType <InversionMoveTabuMaker>()
.Single();
ts.TabuMaker = tabuMaker;
ts.TabuTenure.Value = 60;
#endregion
ts.Engine = new ParallelEngine.ParallelEngine();
return(ts);
}
public void GetTSPLIBTSPInstanceTest() {
var target = new TSPLIBTSPInstanceProvider();
StringBuilder erroneousInstances = new StringBuilder();
int count = 0;
foreach (var id in target.GetDataDescriptors()) {
try {
target.LoadData(id);
} catch (Exception ex) {
erroneousInstances.AppendLine(id.Name + ": " + ex.Message);
}
count++;
}
Assert.IsTrue(count > 0, "No problem instances were found.");
Assert.IsTrue(erroneousInstances.Length == 0, "Some instances could not be parsed: " + Environment.NewLine + erroneousInstances.ToString());
}
private TabuSearch CreateTabuSearchTspSample() {
TabuSearch ts = new TabuSearch();
#region Problem Configuration
var provider = new TSPLIBTSPInstanceProvider();
var instance = provider.GetDataDescriptors().Where(x => x.Name == "ch130").Single();
TravelingSalesmanProblem tspProblem = new TravelingSalesmanProblem();
tspProblem.Load(provider.LoadData(instance));
tspProblem.UseDistanceMatrix.Value = true;
#endregion
#region Algorithm Configuration
ts.Name = "Tabu Search - TSP";
ts.Description = "A tabu search algorithm that solves the \"ch130\" TSP (imported from TSPLIB)";
ts.Problem = tspProblem;
ts.MaximumIterations.Value = 1000;
// move generator has to be set first
var moveGenerator = ts.MoveGeneratorParameter.ValidValues
.OfType<StochasticInversionMultiMoveGenerator>()
.Single();
ts.MoveGenerator = moveGenerator;
var moveEvaluator = ts.MoveEvaluatorParameter.ValidValues
.OfType<TSPInversionMoveRoundedEuclideanPathEvaluator>()
.Single();
ts.MoveEvaluator = moveEvaluator;
var moveMaker = ts.MoveMakerParameter.ValidValues
.OfType<InversionMoveMaker>()
.Single();
ts.MoveMaker = moveMaker;
ts.SampleSize.Value = 750;
ts.Seed.Value = 0;
ts.SetSeedRandomly.Value = true;
var tabuChecker = ts.TabuCheckerParameter.ValidValues
.OfType<InversionMoveSoftTabuCriterion>()
.Single();
tabuChecker.UseAspirationCriterion.Value = true;
ts.TabuChecker = tabuChecker;
var tabuMaker = ts.TabuMakerParameter.ValidValues
.OfType<InversionMoveTabuMaker>()
.Single();
ts.TabuMaker = tabuMaker;
ts.TabuTenure.Value = 60;
#endregion
ts.Engine = new ParallelEngine.ParallelEngine();
return ts;
}
public void GetTSPLIBTSPInstanceTest()
{
var target = new TSPLIBTSPInstanceProvider();
StringBuilder erroneousInstances = new StringBuilder();
int count = 0;
foreach (var id in target.GetDataDescriptors())
{
try {
target.LoadData(id);
} catch (Exception ex) {
erroneousInstances.AppendLine(id.Name + ": " + ex.Message);
}
count++;
}
Assert.IsTrue(count > 0, "No problem instances were found.");
Assert.IsTrue(erroneousInstances.Length == 0, "Some instances could not be parsed: " + Environment.NewLine + erroneousInstances.ToString());
}
private IslandGeneticAlgorithm CreateIslandGaTspSample() {
IslandGeneticAlgorithm ga = new IslandGeneticAlgorithm();
#region Problem Configuration
var provider = new TSPLIBTSPInstanceProvider();
var instance = provider.GetDataDescriptors().Where(x => x.Name == "ch130").Single();
TravelingSalesmanProblem tspProblem = new TravelingSalesmanProblem();
tspProblem.Load(provider.LoadData(instance));
tspProblem.UseDistanceMatrix.Value = true;
#endregion
#region Algorithm Configuration
ga.Name = "Island Genetic Algorithm - TSP";
ga.Description = "An island genetic algorithm which solves the \"ch130\" traveling salesman problem (imported from TSPLIB)";
ga.Problem = tspProblem;
SamplesUtils.ConfigureIslandGeneticAlgorithmParameters<ProportionalSelector, OrderCrossover2, InversionManipulator,
UnidirectionalRingMigrator, BestSelector, WorstReplacer>(
ga, 100, 1, 1000, 0.05, 5, 50, 0.25);
#endregion
return ga;
}
private GeneticAlgorithm CreateGaTspSample()
{
GeneticAlgorithm ga = new GeneticAlgorithm();
#region Problem Configuration
var provider = new TSPLIBTSPInstanceProvider();
var instance = provider.GetDataDescriptors().Where(x => x.Name == "ch130").Single();
TravelingSalesmanProblem tspProblem = new TravelingSalesmanProblem();
tspProblem.Load(provider.LoadData(instance));
tspProblem.UseDistanceMatrix.Value = true;
#endregion
#region Algorithm Configuration
ga.Name = "Genetic Algorithm - TSP";
ga.Description = "A genetic algorithm which solves the \"ch130\" traveling salesman problem (imported from TSPLIB)";
ga.Problem = tspProblem;
SamplesUtils.ConfigureGeneticAlgorithmParameters <ProportionalSelector, OrderCrossover2, InversionManipulator>(
ga, 100, 1, 1000, 0.05);
#endregion
return(ga);
}
