/// <summary>
/// Creates a new solver.
/// </summary>
/// <param name="problem"></param>
/// <param name="customers"></param>
public EdgeAssemblyCrossOverSolver(OsmSharp.Math.TSP.Problems.IProblem problem, IList <int> customers)
{
//_stopped = false;
//_customers = customers;
}
/// <summary>
/// Returns a solution found using best-placement.
/// </summary>
/// <returns></returns>
protected override IRoute DoSolve(OsmSharp.Math.TSP.Problems.IProblem problem)
{
// create the settings.
SolverSettings settings = new SolverSettings(
-1,
-1,
1000000000,
-1,
-1,
-1);
Solver <List <int>, GeneticProblem, Fitness> solver =
new Solver <List <int>, GeneticProblem, Fitness>(
new GeneticProblem(problem),
settings,
null,
null,
null,
_generation_operation,
new FitnessCalculator(),
true, false);
Population <List <int>, GeneticProblem, Fitness> population =
new Population <List <int>, GeneticProblem, Fitness>(true);
while (population.Count < _population_size)
{
// generate new.
Individual <List <int>, GeneticProblem, Fitness> new_individual =
_generation_operation.Generate(solver);
// add to population.
population.Add(new_individual);
}
// select each individual once.
Population <List <int>, GeneticProblem, Fitness> new_population =
new Population <List <int>, GeneticProblem, Fitness>(true);
Individual <List <int>, GeneticProblem, Fitness> best = null;
int stagnation = 0;
while (stagnation < _stagnation)
{
while (new_population.Count < _population_size)
{
// select an individual and the next one.
int idx = OsmSharp.Math.Random.StaticRandomGenerator.Get().Generate(population.Count);
Individual <List <int>, GeneticProblem, Fitness> individual1 = population[idx];
Individual <List <int>, GeneticProblem, Fitness> individual2 = null;
if (idx == population.Count - 1)
{
individual2 = population[0];
}
else
{
individual2 = population[idx + 1];
}
population.RemoveAt(idx);
Individual <List <int>, GeneticProblem, Fitness> new_individual = _cross_over_operation.CrossOver(solver,
individual1, individual2);
new_individual.CalculateFitness(solver.Problem, solver.FitnessCalculator);
if (new_individual.Fitness.CompareTo(
individual1.Fitness) < 0)
{
new_population.Add(new_individual);
}
else
{
new_population.Add(individual1);
}
}
population = new_population;
population.Sort(solver, solver.FitnessCalculator);
new_population = new Population <List <int>, GeneticProblem, Fitness>(true);
if (best == null ||
best.Fitness.CompareTo(population[0].Fitness) > 0)
{
stagnation = 0;
best = population[0];
}
else
{
stagnation++;
}
// report progress.
OsmSharp.Logging.Log.TraceEvent("EdgeAssemblyCrossOverSolver", Logging.TraceEventType.Information,
string.Format("Solving using EAX: Stagnation {0}.", stagnation));
}
var result = new List <int>(best.Genomes);
result.Insert(0, 0);
return(DynamicAsymmetricRoute.CreateFrom(result));
}
