void Start()
{
_voronoiGen = _levelGenerator.GetComponent <VoronoiGenerator>();
_voronoiGen.Generate();
_voronoiGen.ScaleAllPolies(Scale);
OnGenerationComplete?.Invoke();
}
void RoomLogic()
{
Time.timeScale = 1;
SelectRooms();
foreach (Room room in m_rooms)
{
room.RemoveCollider();
}
m_isGenerationComplete = true;
if (OnGenerationComplete != null)
{
OnGenerationComplete.Invoke();
}
}
void Start()
{
chunks = new Dictionary <WorldPosition, Chunk>();
//chunks = new List<Chunk>();
for (int x = 0; x < 10; x++)
{
for (int y = 0; y < 1; y++)
{
for (int z = 0; z < 10; z++)
{
CreateChunk(x * 16, y * 16, z * 16);
}
}
}
if (OnGenerationComplete != null)
{
OnGenerationComplete.Invoke();
}
}
public void Run(int evaluations, double C_t, double N_max, double V_f, double D_max)
{
var scaleVector = CalculateScaleVector(C_t, UB_vector, LB_vector);
InducedMotion inducedMotion = new InducedMotion(KrillPopulation, FitnessFunction, evaluations, N_max);
ForagingMotion foragingMotion = new ForagingMotion(KrillPopulation, FitnessFunction, evaluations, V_f);
VirtualFood virtualFood = new VirtualFood(KrillPopulation, FitnessFunction, UB_vector, LB_vector);
PhysicalDiffusion physicalDiffusion = new PhysicalDiffusion(D_max, evaluations, scaleVector.Count);
// We evaluate each krill in the population
KrillPopulation.EvaluatePopulation(FitnessFunction);
for (int i = 1; i <= evaluations; i++)
{
var vf_position = virtualFood.CreateVirtualFood();
foreach (var krill in KrillPopulation.Population)
{
Vector <double> N_i = inducedMotion.GetInducedMotion(krill, i);
Vector <double> F_i = foragingMotion.GetForagingMotion(krill, i, vf_position);
Vector <double> D_i = physicalDiffusion.GetPhysicalDiffusion(i);
Vector <double> X_i = (F_i + N_i).Add(D_i); // EQUATION 1
X_i = X_i.PointwiseMultiply(scaleVector);
var newPosition = krill.Coordinates + X_i;
var bestKrillPosition = KrillPopulation.GetBestKrill().Coordinates;
krill.Coordinates = MathHelpers.FindLimits(newPosition, bestKrillPosition, LB_vector, UB_vector);
}
// We evaluate each krill in the population
KrillPopulation.EvaluatePopulation(FitnessFunction);
OnGenerationComplete.Invoke(KrillPopulation, new RunEventArgs(i));
}
OnRunComplete.Invoke(KrillPopulation, new RunEventArgs(evaluations));
}
public void Run(int maxEvaluations, double CR, double F)
{
Population.Evaluate(Parameters.FitnessFunction);
while (maxEvaluations > 0)
{
List <Individual> newGeneration = new List <Individual>();
foreach (var orginal in Population.Solutions)
{
// generate unique random numbers
List <int> randomValues = RandomGenerator.GenerateRandom(3, 0, Population.Solutions.Count);
int a = randomValues[0];
int b = randomValues[1];
int c = randomValues[2];
// choose random individuals (agents) from population
Individual individual1 = Population.Solutions[a];
Individual individual2 = Population.Solutions[b];
Individual individual3 = Population.Solutions[c];
int i = 0;
int R = RandomGenerator.Instance.Random.Next(Population.Solutions.Count);
Individual candidate = new Individual();
foreach (var orginalElement in orginal.Elements)
{
double ri = RandomGenerator.Instance.Random.NextDouble();
if (ri < CR || i == R)
{
// simple mutation
double newElement = individual1.Elements[i] + F * (individual2.Elements[i] - individual3.Elements[i]);
if (CheckIfWithinDomain(newElement, Parameters))
{
candidate.Elements.Add(newElement);
}
else
{
candidate.Elements.Add(orginalElement);
}
}
else
{
candidate.Elements.Add(orginalElement);
}
i++;
}
if (candidate.Evaluate(Parameters.FitnessFunction) < orginal.Evaluate(Parameters.FitnessFunction))
{
newGeneration.Add(candidate);
}
else
{
newGeneration.Add(orginal);
}
}
// switch populations
Population.Solutions = newGeneration;
maxEvaluations--;
OnGenerationComplete.Invoke(Population, new EventArgs());
}
OnRunComplete.Invoke(Population, new EventArgs());
}
