public Chromosome Run(IProblem problem, Parameters parameters)
{
Parameters = parameters;
chromosomeFactory = parameters.ChromosomeFactory;
heuristics = parameters.Heuristics;
var population = Initialization(problem, parameters);
int counter = 0;
var watch = System.Diagnostics.Stopwatch.StartNew();
watch.Start();
bool conv = false;
Chromosome theBestOne = population[0], localBest;
do
{
population = Evolve(problem, parameters, population);
population = Improve(problem, parameters, population);
if (conv = IsConvergent(population, parameters))
{
population = Restart(problem, population, parameters);
}
localBest = GetTheBestChromosome(population);
theBestOne = theBestOne.CompareTo(localBest) < 0 ? theBestOne : localBest;
} while (!StopCondition(++counter, parameters));
//Chromosome theBestSolution = GetTheBestChromosome(population);
Console.WriteLine("\nThe best solution: " + theBestOne.ToString());
return(theBestOne);
}
/// <summary>
/// Runs the memetic algorithm to solve the problem.
/// </summary>
/// <param name="problem">The problem.</param>
/// <param name="parameters">The parameters.</param>
/// <returns>The best found solution.</returns>
public async Task <Chromosome> Run(IProblem problem, Parameters parameters, Action <string, Chromosome, int, float, long, int> callback, string id, bool genetic)
{
Parameters = parameters;
chromosomeFactory = parameters.ChromosomeFactory;
heuristics = parameters.Heuristics;
Population population = new Population(parameters.PopulationSize);
Chromosome solution = null;
int index = 0;
while (index < parameters.PopulationSize)
{
solution = parameters.ChromosomeFactory.RandomSolution(parameters.GeneCount, problem);
if (!population.Contains(solution))
{
population[index] = solution;
++index;
}
}
var watch = System.Diagnostics.Stopwatch.StartNew();
watch.Start();
Chromosome theBestOne = population[0], localBest;
int iteration = 0, restarts = 0;
await Task.Run(() =>
{
do
{
if (source.IsCancellationRequested)
{
break;
}
population = Evolve(problem, parameters, population);
if (IsConvergent(population, parameters))
{
population = Restart(problem, population, parameters);
++restarts;
}
localBest = GetTheBestChromosome(population);
theBestOne = theBestOne.CompareTo(localBest) < 0 ? theBestOne : localBest;
callback(id, theBestOne, iteration, (theBestOne as Solution).TotalDistance(),
watch.ElapsedMilliseconds, restarts);
} while (!StopCondition(++iteration, parameters));
}, source.Token);
source.Dispose();
source = new CancellationTokenSource();
return(theBestOne);
}
public Task <Chromosome> Run(IProblem problem, Parameters parameters, Action <string, Chromosome, int, float, long, int> callback, string id)
{
chromosomeFactory = parameters.ChromosomeFactory;
heuristics = parameters.Heuristics;
var population = Initialization(problem, parameters);
int iteration = 0, restarts = 0;
var watch = System.Diagnostics.Stopwatch.StartNew();
watch.Start();
Chromosome theBestOne = population[0], localBest;
var result = Task.Run(() =>
{
do
{
if (source.IsCancellationRequested)
{
break;
}
population = Evolve(problem, parameters, population);
population = Improve(problem, parameters, population);
if (IsConvergent(population, parameters))
{
population = Restart(problem, population, parameters);
++restarts;
}
localBest = GetTheBestChromosome(population);
theBestOne = theBestOne.CompareTo(localBest) < 0 ? theBestOne : localBest;
callback(id, theBestOne, iteration, (theBestOne as Solution).TotalDistance(),
watch.ElapsedMilliseconds, restarts);
} while (!StopCondition(++iteration, parameters));
return(theBestOne);
}, source.Token);
//Chromosome theBestSolution = GetTheBestChromosome(population);
source.Dispose();
source = new CancellationTokenSource();
return(result);
}
/// <summary>
/// Creates a new fitness landscape.
/// </summary>
/// <param name="problem">The problem instance.</param>
/// <param name="chromosomeFactory">A chromosome factory.</param>
/// <param name="fitnessFunction">The fitness function.</param>
public Landscape(IProblem problem, AbstractChromosomeFactory chromosomeFactory, FitnessFunction fitnessFunction)
{
Problem = problem;
ChromosomeFactory = chromosomeFactory;
FitnessFunction = fitnessFunction;
}
public void SetUp()
{
reader = new VrptwProblemReader();
factory = new SolutionFactory();
fitness = new FitnessFunction(5000, 1);
}
