private void LogData(NsgaLog log, List <List <Individual> > fronts, int iteration)
{
log.FrontsNumberLog.Add(fronts.Count);
log.FirstFrontSizeLog.Add(fronts.First().Count);
var individuals = fronts.SelectMany(f => f).ToList();
var crowdingDistances = individuals.Select(ind => ind.CrowdingDistance).Where(dist => !double.IsInfinity(dist)).ToList();
if (crowdingDistances.Any())
{
log.CrowdingDistanceVar.Add(crowdingDistances.Variance());
log.CrowdingDistanceAvg.Add(crowdingDistances.Average());
}
else
{
log.CrowdingDistanceVar.Add(0);
log.CrowdingDistanceAvg.Add(0);
}
var feasibleRatio = (double)individuals.Where(i => i.IsFeasible).ToList().Count / individuals.Count;
log.FeasibleSolutions.Add(feasibleRatio);
var evaluiationTypes = individuals.First().Evaluations.Select(e => e.Type);
foreach (var evaluiationType in evaluiationTypes)
{
log.ObjectiveLogs.Add(GetFrontObjectiveLog(individuals, evaluiationType, iteration));
}
}
public NsgaResult Solve()
{
var log = new NsgaLog();
var watch = Stopwatch.StartNew();
var individuals = InitializeIndividuals();
_evaluator.Evaluate(individuals);
var fronts = _sorter.Sort(individuals).ToList();
for (var iteration = 0; iteration < _config.MaxIterations; iteration++)
{
AssignCrowdingDistance(fronts);
//Create Offspring
individuals.AddRange(CreateOffspring(individuals));
//sort
fronts = _sorter.Sort(individuals);
AssignCrowdingDistance(fronts);
//select next Gen
fronts = SelectNextGeneration(fronts);
individuals = fronts.SelectMany(f => f).ToList();
LogData(log, fronts, iteration);
Debug.WriteLine($"iteration {iteration}");
if (individuals.Count != _config.PopulationSize)
{
throw new ApplicationException($"Population size is {individuals.Count} instead of {_config.PopulationSize}");
}
}
_evaluator.Evaluate(individuals);
fronts = _sorter.Sort(individuals).ToList();
watch.Stop();
log.SolvingTime = (int)watch.ElapsedMilliseconds;
return(new NsgaResult
{
Log = log,
Fronts = fronts
});
}
