public double[] Solve(IProblem problem)
{
Random rnd = new Random();
Nest[] hosts = new Nest[NumberOfNests];
double[] bestGlobalPosition = new double[problem.Dimension];
// generate initial population of host nests
for (int i = 0; i < hosts.Length; i++)
{
double[] randomPosition = new double[problem.Dimension];
for (int j = 0; j < randomPosition.Length; j++)
{
randomPosition[j] = (problem.MaxX - problem.MinX) * rnd.NextDouble() + problem.MinX;
}
double error = problem.ErrorFunction(randomPosition);
hosts[i] = new Nest(randomPosition, error);
}
int currentIteration = 0;
while (currentIteration < MaxIterations)
{
for (int i = 0; i < hosts.Length; i++)
{
double[] newCoords = new double[problem.Dimension];
hosts[i].Coords.CopyTo(newCoords, 0);
for (int j = 0; j < problem.Dimension; j++)
{
newCoords[j] += LevyFlight(LevyExponent);
if (newCoords[j] < problem.MinX) // new position out of min-bound
{
newCoords[j] = problem.MinX;
}
if (newCoords[j] > problem.MaxX) // new position out of max-bound
{
newCoords[j] = problem.MaxX;
}
}
double error = problem.ErrorFunction(newCoords);
Nest newNest = new Nest(newCoords, error);
if (newNest.Error < hosts[i].Error)
{
newNest.Coords.CopyTo(hosts[i].Coords, 0);
hosts[i].Error = newNest.Error;
}
}
Array.Sort(hosts);
for (int i = hosts.Length - 1; i > 1; i--)
{
int abandon = rnd.Next(0, 100);
if (abandon < ProbabilityOfAbandon)
{
double[] randomPosition = new double[problem.Dimension];
for (int j = 0; j < randomPosition.Length; j++)
{
randomPosition[j] = (problem.MaxX - problem.MinX) * rnd.NextDouble() + problem.MinX;
}
double error = problem.ErrorFunction(randomPosition);
hosts[i] = new Nest(randomPosition, error);
}
}
Array.Sort(hosts);
hosts[0].Coords.CopyTo(bestGlobalPosition, 0);
if (hosts[0].Error < AcceptableError)
{
break;
}
currentIteration++;
}
return(bestGlobalPosition);
}
public double[] Solve(IProblem problem)
{
Random rnd = new Random();
Particle[] swarm = new Particle[NumberOfParticles];
double[] bestGlobalPosition = new double[problem.Dimension]; // best solution found by any particle in the swarm
double bestGlobalError = double.MaxValue;
// init swarm
for (int i = 0; i < swarm.Length; i++)
{
double[] randomPosition = new double[problem.Dimension];
for (int j = 0; j < randomPosition.Length; j++)
{
randomPosition[j] = (problem.MaxX - problem.MinX) * rnd.NextDouble() + problem.MinX;
}
double error = problem.ErrorFunction(randomPosition);
double[] randomVelocity = new double[problem.Dimension];
for (int j = 0; j < randomVelocity.Length; j++)
{
double low = problem.MinX * 0.1;
double high = problem.MaxX * 0.1;
randomVelocity[j] = (high - low) * rnd.NextDouble() + low;
}
swarm[i] = new Particle(randomPosition, error, randomVelocity, randomPosition, error);
if (swarm[i].Error < bestGlobalError)
{
bestGlobalError = swarm[i].Error;
swarm[i].Coords.CopyTo(bestGlobalPosition, 0);
}
}
double inertia = 0.729; // inertia weight
double localWeight = 1.49445; // local weight
double globalWeight = 1.49445; // global weight
double localRandom, globalRandom; // local and global weights randomizations
double probabilityOfDeath = 0.01;
int currentIteration = 0;
bool acceptableErrorReached = false;
double[] newVelocity = new double[problem.Dimension];
double[] newPosition = new double[problem.Dimension];
double newError;
while (currentIteration < MaxIterations && !acceptableErrorReached)
{
foreach (Particle currentParticle in swarm)
{
// calculate new velocity (displacement of the particle)
for (int i = 0; i < currentParticle.Velocity.Length; i++)
{
localRandom = rnd.NextDouble();
globalRandom = rnd.NextDouble();
newVelocity[i] = (inertia * currentParticle.Velocity[i]) +
(localWeight * localRandom * (currentParticle.BestPosition[i] - currentParticle.Coords[i])) +
(globalWeight * globalRandom * (bestGlobalPosition[i] - currentParticle.Coords[i]));
}
newVelocity.CopyTo(currentParticle.Velocity, 0);
// calculate new position after displacement
for (int i = 0; i < currentParticle.Coords.Length; i++)
{
newPosition[i] = currentParticle.Coords[i] + newVelocity[i];
if (newPosition[i] < problem.MinX) // new position out of min-bound
{
newPosition[i] = problem.MinX;
}
if (newPosition[i] > problem.MaxX) // new position out of max-bound
{
newPosition[i] = problem.MaxX;
}
}
newPosition.CopyTo(currentParticle.Coords, 0);
// evaluate the error with the new params
newError = problem.ErrorFunction(newPosition);
currentParticle.Error = newError;
if (newError < currentParticle.BestError) // is newPosition a local best
{
newPosition.CopyTo(currentParticle.BestPosition, 0);
currentParticle.BestError = newError;
}
if (newError < bestGlobalError) // is newPosition a global best
{
newPosition.CopyTo(bestGlobalPosition, 0);
bestGlobalError = newError;
}
if (currentParticle.Error < AcceptableError)
{
acceptableErrorReached = true;
break;
}
// particle died so replace it with another one with random starting position
double death = rnd.NextDouble();
if (death < probabilityOfDeath)
{
for (int i = 0; i < currentParticle.Coords.Length; i++)
{
currentParticle.Coords[i] = (problem.MaxX - problem.MinX) * rnd.NextDouble() + problem.MinX;
}
currentParticle.Error = problem.ErrorFunction(currentParticle.Coords);
currentParticle.Coords.CopyTo(currentParticle.BestPosition, 0);
currentParticle.BestError = currentParticle.Error;
if (currentParticle.Error < bestGlobalError) // randomly got a new global best
{
bestGlobalError = currentParticle.Error;
currentParticle.Coords.CopyTo(bestGlobalPosition, 0);
}
if (currentParticle.Error < AcceptableError)
{
acceptableErrorReached = true;
break;
}
}
}
currentIteration++;
}
// reached maxIterations or acceptableError
Console.WriteLine("Finished");
for (int i = 0; i < swarm.Length; i++)
{
Console.WriteLine("Particle: " + i);
Console.WriteLine(swarm[i].ToString()); // print the particles
}
double[] finalResult = new double[problem.Dimension];
bestGlobalPosition.CopyTo(finalResult, 0);
return(finalResult); // return the best global result
}
public double[] Solve(IProblem problem)
{
Random rnd = new Random();
Agent[] population = new Agent[PopulationSize];
double[] bestGlobalPosition = new double[problem.Dimension];
Array.Sort(population);
Group[] groups = new Group[GroupCount];
for (int i = 0; i < GroupCount; i++)
{
double[] randomPosition = new double[problem.Dimension];
for (int j = 0; j < randomPosition.Length; j++)
{
randomPosition[j] = (problem.MaxX - problem.MinX) * rnd.NextDouble() + problem.MinX;
}
double error = problem.ErrorFunction(randomPosition);
groups[i] = new Group(new Agent(randomPosition, error), PopulationSize / GroupCount);
}
int currentIteration = 0;
while (currentIteration < MaxIterations)
{
for (int i = 0; i < GroupCount; i++)
{
double minDistance = double.MaxValue;
double minError = double.MaxValue;
double minError2 = double.MaxValue;
double[] minCoords = new double[problem.Dimension];
double[] minCoords2 = new double[problem.Dimension];
for (int j = 0; j < GroupCount; j++)
{
if (i != j)
{
double distance = groups[i].Head.distanceTo(groups[j].Head);
if (minDistance > distance)
{
minDistance = distance;
}
}
}
for (int j = 1; j < groups[i].GroupSize; j++)
{
double[] randomPosition = new double[problem.Dimension];
for (int k = 0; k < randomPosition.Length; k++)
{
randomPosition[k] = 2 * minDistance * rnd.NextDouble() - minDistance + groups[i].Head.Coords[k];
}
double error = problem.ErrorFunction(randomPosition);
if (error < minError)
{
minError2 = minError;
minCoords.CopyTo(minCoords2, 0);
minError = error;
randomPosition.CopyTo(minCoords, 0);
}
else if (error < minError2)
{
minError2 = error;
randomPosition.CopyTo(minCoords2, 0);
}
}
if (minError < groups[i].Head.Error)
{
if (minError2 < groups[i].Head.Error)
{
minCoords2.CopyTo(groups[i].Tail.Coords, 0);
groups[i].Tail.Error = minError2;
}
else
{
groups[i].Head.Coords.CopyTo(groups[i].Tail.Coords, 0);
groups[i].Tail.Error = groups[i].Head.Error;
}
minCoords.CopyTo(groups[i].Head.Coords, 0);
groups[i].Head.Error = minError;
}
else
{
minCoords.CopyTo(groups[i].Tail.Coords, 0);
groups[i].Tail.Error = minError;
}
}
Array.Sort(groups);
groups[0].Head.Coords.CopyTo(bestGlobalPosition, 0);
if (groups[0].Head.Error < AcceptableError)
{
break;
}
int removedAgents = groups[GroupCount - 1].GroupSize * 4 / 10;
groups[GroupCount - 1].GroupSize -= removedAgents;
if (groups[GroupCount - 1].GroupSize < 2)
{
groups[0].Tail.Coords.CopyTo(groups[GroupCount - 1].Head.Coords, 0);
groups[GroupCount - 1].Head.Error = groups[0].Tail.Error;
groups[0].GroupSize = groups[0].GroupSize / 2 + groups[0].GroupSize % 2;
groups[GroupCount - 1].GroupSize = groups[0].GroupSize /= 2;
}
else
{
groups[0].GroupSize += removedAgents;
}
currentIteration++;
}
return(bestGlobalPosition);
}
