protected override List <Individual.Individual> InitRandomPopulation(int amountOfIndividuals)
{
var population = new List <Individual.Individual>();
// If there is an odd amount of teams, add a break event to every round.
bool addBreakRound = _amountOfTeams % 2 == 1;
int eventsPerRound = addBreakRound ? _amountOfTeams / 2 + 1 : _amountOfTeams / 2;
int regularEventsPerRound = addBreakRound ? eventsPerRound - 1 : eventsPerRound;
Console.WriteLine($"-> Creating {amountOfIndividuals} schedules with {eventsPerRound} events per round.");
for (int i = 0; i < amountOfIndividuals; i++)
{
population.Add(BallStarsSchedule.Random(
_amountOfTeams, eventsPerRound, regularEventsPerRound, _amountOfRounds, _matchPool, addBreakRound,
_avgPlayersPerTeam, _predefinedMatchUps
));
}
return(population);
}
public void RunSimulatedAnnealing(double initialTemperature = 10, double alpha = 0.9999)
{
// Start off with a randomised schedule for an even amount of teams
int eventsPerRound = _amountOfTeams / 2;
BallStarsSchedule currentSchedule = BallStarsSchedule.Random(
_amountOfTeams, eventsPerRound, eventsPerRound, _amountOfRounds, _matchPool, false,
_avgPlayersPerTeam, _predefinedMatchUps
);
BallStarsSchedule bestSchedule = currentSchedule;
double bestFitness = currentSchedule.Evaluate();
double currentScheduleFitness = bestFitness;
double minimumTemp = 0.000000001;
double temperature = initialTemperature;
int iters = 0;
while (iters < 100000)
{
if (iters % 1000 == 0)
{
Console.WriteLine($"Running iteration {iters}.");
}
// Pick a neighbour by cloning and mutating
BallStarsSchedule neighbour = currentSchedule.Clone();
neighbour.LocalSearchMutate(_matchPool);
neighbour.Evaluate();
// Always accept better solutions
if (neighbour.Fitness < currentScheduleFitness)
{
// Update currently tracking schedule and fitness
currentScheduleFitness = neighbour.Fitness;
currentSchedule = neighbour;
// Update best found schedule and fitness if they improved
if (currentScheduleFitness < bestFitness)
{
bestFitness = currentScheduleFitness;
bestSchedule = currentSchedule.Clone();
Console.WriteLine($"New best fitness: {bestFitness} (found in iteration {iters})");
}
}
else
{
// Accept worse solutions often when starting out, but not as much near termination
double diff = neighbour.Fitness - currentSchedule.Fitness;
double acceptanceProb = 1 / (1 + Math.Exp(diff / temperature));
if (Globals.Rand.NextDouble() < acceptanceProb)
{
currentSchedule = neighbour;
currentScheduleFitness = neighbour.Fitness;
}
}
temperature *= alpha;
iters++;
// TODO: Maybe add a temperature reset to get out of a local minimum
}
Console.WriteLine($"Algorithm finished. The best schedule is as follows:\n{bestSchedule}");
}
