public EmitterBase(EmitterActionType emitterType, Vector2 position, Parameters parameters)
{
IsActive = true;
EmitterType = emitterType;
Position = position;
Parameters = parameters;
}
public Parameters(Parameters parent)
{
neighborhoodRadius = parent.neighborhoodRadius;
normalSpeed = parent.normalSpeed;
maxSpeed = parent.maxSpeed;
c1 = parent.c1;
c2 = parent.c2;
c3 = parent.c3;
c4 = parent.c4;
c5 = parent.c5;
}
public Individual(int id, double x, double y, double dx, double dy, double dx2, double dy2, Parameters g, EmitterActionType emitterType, bool isMobile)
{
ID = id;
X = x;
Y = y;
Dx = Dx2 = dx;
Dy = Dy2 = dy;
Genome = g;
EmitterType = emitterType;
IsMobile = isMobile;
color = Genome.getDisplayColor(emitterType);
rankInXOrder = 0;
rankInYOrder = 0;
}
public Individual(int id, double x, double y, double dx, double dy, Parameters g, EmitterActionType emitterType, bool isMobile)
: this(id, x, y, dx, dy, dx, dy, g, emitterType, isMobile)
{
}
public Individual(int id, double x, double y, double dx, double dy, Parameters g)
: this(id, x, y, dx, dy, g, EmitterActionType.Still, true)
{
}
public Individual(int id, double x, double y, double dx, double dy, Parameters g)
: this(id, x, y, dx, dy, g, EmitterActionType.Still, true)
{
}
public Individual(int id, double x, double y, double dx, double dy, double dx2, double dy2, Parameters g, EmitterActionType emitterType, bool isMobile)
{
ID = id;
X = x;
Y = y;
Dx = Dx2 = dx;
Dy = Dy2 = dy;
Genome = g;
EmitterType = emitterType;
IsMobile = isMobile;
color = Genome.getDisplayColor(emitterType);
rankInXOrder = 0;
rankInYOrder = 0;
}
public Individual(int id, double x, double y, double dx, double dy, Parameters g, EmitterActionType emitterType, bool isMobile)
: this(id, x, y, dx, dy, dx, dy, g, emitterType, isMobile)
{
}
public void stepSimulation(List<Individual> temporaryIndividuals, int weightOfTemporaries)
{
int numberOfSwarm = swarmInBirthOrder.Count();
for (int i = 0; i < numberOfSwarm; i++)
{
currentInd = swarmInBirthOrder[i];
param = currentInd.Genome;
tempX = currentInd.X;
tempY = currentInd.Y;
neighborhoodRadiusSquared = param.getNeighborhoodRadius()
* param.getNeighborhoodRadius();
List<Individual> neighbors = new List<Individual>();
// Detecting neighbors using sorted lists
minX = tempX - param.getNeighborhoodRadius();
maxX = tempX + param.getNeighborhoodRadius();
minY = tempY - param.getNeighborhoodRadius();
maxY = tempY + param.getNeighborhoodRadius();
minRankInXOrder = currentInd.RankInXOrder;
maxRankInXOrder = currentInd.RankInXOrder;
minRankInYOrder = currentInd.RankInYOrder;
maxRankInYOrder = currentInd.RankInYOrder;
//TODO: Make this faster
for (int j = currentInd.RankInXOrder - 1; j >= 0; j--)
{
if (swarmInXOrder[j].X >= minX)
minRankInXOrder = j;
else
break;
}
for (int j = currentInd.RankInXOrder + 1; j < numberOfSwarm; j++)
{
if (swarmInXOrder[j].X <= maxX)
maxRankInXOrder = j;
else
break;
}
for (int j = currentInd.RankInYOrder - 1; j >= 0; j--)
{
if (swarmInYOrder[j].Y >= minY)
minRankInYOrder = j;
else
break;
}
for (int j = currentInd.RankInYOrder + 1; j < numberOfSwarm; j++)
{
if (swarmInYOrder[j].Y <= maxY)
maxRankInYOrder = j;
else
break;
}
if (maxRankInXOrder - minRankInXOrder < maxRankInYOrder
- minRankInYOrder)
{
for (int j = minRankInXOrder; j < maxRankInXOrder; j++)
{
tempSwarm2 = swarmInXOrder[j];
if (currentInd != tempSwarm2)
if (tempSwarm2.RankInYOrder >= minRankInYOrder
&& tempSwarm2.RankInYOrder <= maxRankInYOrder)
{
if ((tempSwarm2.X - currentInd.X)
* (tempSwarm2.X - currentInd.X)
+ (tempSwarm2.Y - currentInd.Y)
* (tempSwarm2.Y - currentInd.Y) < neighborhoodRadiusSquared)
neighbors.Add(tempSwarm2);
}
}
}
else
{
for (int j = minRankInYOrder; j < maxRankInYOrder; j++)
{
tempSwarm2 = swarmInYOrder[j];
if (currentInd != tempSwarm2)
if (tempSwarm2.RankInXOrder >= minRankInXOrder
&& tempSwarm2.RankInXOrder <= maxRankInXOrder)
{
if ((tempSwarm2.X - currentInd.X)
* (tempSwarm2.X - currentInd.X)
+ (tempSwarm2.Y - currentInd.Y)
* (tempSwarm2.Y - currentInd.Y) < neighborhoodRadiusSquared)
neighbors.Add(tempSwarm2);
}
}
}
for (int k = 0; k < temporaryIndividuals.Count; k++)
{
if ((temporaryIndividuals[k].X - currentInd.X)
* (temporaryIndividuals[k].X - currentInd.X)
+ (temporaryIndividuals[k].Y - currentInd.Y)
* (temporaryIndividuals[k].Y - currentInd.Y) < neighborhoodRadiusSquared)
for (int j = 0; j < weightOfTemporaries; j++)
neighbors.Add(temporaryIndividuals[k]);
}
// simulating the behavior of swarm agents
int n = neighbors.Count();
if (n == 0)
{
tempAx = rand.NextDouble() - 0.5;
tempAy = rand.NextDouble() - 0.5;
}
else
{
localCenterX = 0;
localCenterY = 0;
localDX = 0;
localDY = 0;
for (int j = 0; j < n; j++)
{
tempSwarm2 = neighbors[j];
localCenterX += tempSwarm2.X;
localCenterY += tempSwarm2.Y;
localDX += tempSwarm2.Dx;
localDY += tempSwarm2.Dy;
}
localCenterX /= n;
localCenterY /= n;
localDX /= n;
localDY /= n;
tempAx = tempAy = 0;
tempAx += (localCenterX - tempX) * param.getC1();
tempAy += (localCenterY - tempY) * param.getC1();
tempAx += (localDX - currentInd.Dx) * param.getC2();
tempAy += (localDY - currentInd.Dy) * param.getC2();
for (int j = 0; j < n; j++)
{
tempSwarm2 = neighbors[j];
tempX2 = tempSwarm2.X;
tempY2 = tempSwarm2.Y;
double d = (tempX - tempX2) * (tempX - tempX2) + (tempY - tempY2)
* (tempY - tempY2);
if (d == 0)
d = 0.001;
tempAx += (tempX - tempX2) / d * param.getC3();
tempAy += (tempY - tempY2) / d * param.getC3();
}
//rand = new Random();
//if (rand.NextDouble() < param.getC4())
//{
// tempAx += rand.NextDouble() * 10 - 5;
// tempAy += rand.NextDouble() * 10 - 5;
//}
}
currentInd.accelerate(tempAx, tempAy, param.getMaxSpeed());
tempDX = currentInd.Dx2;
tempDY = currentInd.Dy2;
double f = Math.Sqrt(tempDX * tempDX + tempDY * tempDY);
if (f == 0)
f = 0.001;
currentInd.accelerate(tempDX * (param.getNormalSpeed() - f) / f
* param.getC5(),
tempDY * (param.getNormalSpeed() - f) / f * param.getC5(),
param.getMaxSpeed());
};
updateInternalState();
}
public void stepSimulation(List <Individual> temporaryIndividuals, int weightOfTemporaries)
{
int numberOfSwarm = swarmInBirthOrder.Count();
for (int i = 0; i < numberOfSwarm; i++)
{
currentInd = swarmInBirthOrder[i];
param = currentInd.Genome;
tempX = currentInd.X;
tempY = currentInd.Y;
neighborhoodRadiusSquared = param.getNeighborhoodRadius()
* param.getNeighborhoodRadius();
List <Individual> neighbors = new List <Individual>();
// Detecting neighbors using sorted lists
minX = tempX - param.getNeighborhoodRadius();
maxX = tempX + param.getNeighborhoodRadius();
minY = tempY - param.getNeighborhoodRadius();
maxY = tempY + param.getNeighborhoodRadius();
minRankInXOrder = currentInd.RankInXOrder;
maxRankInXOrder = currentInd.RankInXOrder;
minRankInYOrder = currentInd.RankInYOrder;
maxRankInYOrder = currentInd.RankInYOrder;
//TODO: Make this faster
for (int j = currentInd.RankInXOrder - 1; j >= 0; j--)
{
if (swarmInXOrder[j].X >= minX)
{
minRankInXOrder = j;
}
else
{
break;
}
}
for (int j = currentInd.RankInXOrder + 1; j < numberOfSwarm; j++)
{
if (swarmInXOrder[j].X <= maxX)
{
maxRankInXOrder = j;
}
else
{
break;
}
}
for (int j = currentInd.RankInYOrder - 1; j >= 0; j--)
{
if (swarmInYOrder[j].Y >= minY)
{
minRankInYOrder = j;
}
else
{
break;
}
}
for (int j = currentInd.RankInYOrder + 1; j < numberOfSwarm; j++)
{
if (swarmInYOrder[j].Y <= maxY)
{
maxRankInYOrder = j;
}
else
{
break;
}
}
if (maxRankInXOrder - minRankInXOrder < maxRankInYOrder
- minRankInYOrder)
{
for (int j = minRankInXOrder; j < maxRankInXOrder; j++)
{
tempSwarm2 = swarmInXOrder[j];
if (currentInd != tempSwarm2)
{
if (tempSwarm2.RankInYOrder >= minRankInYOrder &&
tempSwarm2.RankInYOrder <= maxRankInYOrder)
{
if ((tempSwarm2.X - currentInd.X)
* (tempSwarm2.X - currentInd.X)
+ (tempSwarm2.Y - currentInd.Y)
* (tempSwarm2.Y - currentInd.Y) < neighborhoodRadiusSquared)
{
neighbors.Add(tempSwarm2);
}
}
}
}
}
else
{
for (int j = minRankInYOrder; j < maxRankInYOrder; j++)
{
tempSwarm2 = swarmInYOrder[j];
if (currentInd != tempSwarm2)
{
if (tempSwarm2.RankInXOrder >= minRankInXOrder &&
tempSwarm2.RankInXOrder <= maxRankInXOrder)
{
if ((tempSwarm2.X - currentInd.X)
* (tempSwarm2.X - currentInd.X)
+ (tempSwarm2.Y - currentInd.Y)
* (tempSwarm2.Y - currentInd.Y) < neighborhoodRadiusSquared)
{
neighbors.Add(tempSwarm2);
}
}
}
}
}
for (int k = 0; k < temporaryIndividuals.Count; k++)
{
if ((temporaryIndividuals[k].X - currentInd.X)
* (temporaryIndividuals[k].X - currentInd.X)
+ (temporaryIndividuals[k].Y - currentInd.Y)
* (temporaryIndividuals[k].Y - currentInd.Y) < neighborhoodRadiusSquared)
{
for (int j = 0; j < weightOfTemporaries; j++)
{
neighbors.Add(temporaryIndividuals[k]);
}
}
}
// simulating the behavior of swarm agents
int n = neighbors.Count();
if (n == 0)
{
tempAx = rand.NextDouble() - 0.5;
tempAy = rand.NextDouble() - 0.5;
}
else
{
localCenterX = 0;
localCenterY = 0;
localDX = 0;
localDY = 0;
for (int j = 0; j < n; j++)
{
tempSwarm2 = neighbors[j];
localCenterX += tempSwarm2.X;
localCenterY += tempSwarm2.Y;
localDX += tempSwarm2.Dx;
localDY += tempSwarm2.Dy;
}
localCenterX /= n;
localCenterY /= n;
localDX /= n;
localDY /= n;
tempAx = tempAy = 0;
tempAx += (localCenterX - tempX) * param.getC1();
tempAy += (localCenterY - tempY) * param.getC1();
tempAx += (localDX - currentInd.Dx) * param.getC2();
tempAy += (localDY - currentInd.Dy) * param.getC2();
for (int j = 0; j < n; j++)
{
tempSwarm2 = neighbors[j];
tempX2 = tempSwarm2.X;
tempY2 = tempSwarm2.Y;
double d = (tempX - tempX2) * (tempX - tempX2) + (tempY - tempY2)
* (tempY - tempY2);
if (d == 0)
{
d = 0.001;
}
tempAx += (tempX - tempX2) / d * param.getC3();
tempAy += (tempY - tempY2) / d * param.getC3();
}
//rand = new Random();
//if (rand.NextDouble() < param.getC4())
//{
// tempAx += rand.NextDouble() * 10 - 5;
// tempAy += rand.NextDouble() * 10 - 5;
//}
}
currentInd.accelerate(tempAx, tempAy, param.getMaxSpeed());
tempDX = currentInd.Dx2;
tempDY = currentInd.Dy2;
double f = Math.Sqrt(tempDX * tempDX + tempDY * tempDY);
if (f == 0)
{
f = 0.001;
}
currentInd.accelerate(tempDX * (param.getNormalSpeed() - f) / f
* param.getC5(),
tempDY * (param.getNormalSpeed() - f) / f * param.getC5(),
param.getMaxSpeed());
}
;
updateInternalState();
}
