//This function assumes the new fitnessvalues have just been updated
void SwarmOptimizer::tick(float sigma)
{
//First update the personalBest and friendBest
for(ParticleVector::iterator j = swarm.begin(); j!=swarm.end(); ++j)
{
//if there is an improvement...
if(j->fitness>=j->personalOptimum)
{
//update the personal best
j->personalOptimum=j->fitness;
j->personalBest=j->position;
//perhaps also update the friends best
if(j->fitness>j->friendOptimum)
{
j->friendOptimum=j->fitness;
j->friendBest=j->position;
}
//Broadcast this result to friends
for(int k=0;k<(int)j->friends.size();k++)
{
int f=j->friends[k];
if(j->fitness>swarm[f].friendOptimum)
{
swarm[f].friendOptimum=j->fitness;
swarm[f].friendBest=j->position;
}
}
}
}
//Then adjust the position and velocity vectors
for(ParticleVector::iterator j = swarm.begin(); j != swarm.end(); ++j)
{
/*
//screw it up
if(tickcount%100==0)
for(int i=0;i<ndims;i++)
j->velocity[i]=((rand()%1000)-500)/1000.0;
if(tickcount%500==0)
for(int i=0;i<ndims;i++)
j->position[i]+=(rand()%20)-10;
*/
//update velocity
for(int i=0;i<ndims;i++) {
j->velocity[i]*=config.omega*sigma;
j->velocity[i]+=UnitRandom() * config.phi1*sigma*(j->personalBest[i]-j->position[i]);
j->velocity[i]+=UnitRandom() * config.phi2*sigma*(j->friendBest[i]-j->position[i]);
j->velocity[i]+=(UnitRandom()-0.5f) * sigma * .01f;
}
//update position
for(int i=0;i<ndims;i++)
j->position[i] += j->velocity[i];
}
tickcount++;
}
float Random(float min, float max) { return min + UnitRandom() * (max-min); }
//-----------------------------------------------------------------------
Real Math::SymmetricRandom ()
{
return 2.0f * UnitRandom() - 1.0f;
}
//-----------------------------------------------------------------------
Real Math::RangeRandom (Real fLow, Real fHigh)
{
return (fHigh-fLow)*UnitRandom() + fLow;
}
