AlgParticleSwarmOptimization.cpp

#include "AlgParticleSwarmOptimization.h"
 
 
ParticleSwarmOptimization::ParticleSwarmOptimization()
{
    // define algorithm name
    name_ = "ParticleSwarmOptimization";
 
    areGenotypesAdded_ = false;
 
    // create selection operators needed
    // in this case, SelBestOp
    selBestOp = static_cast<SelectionOperatorP> (new SelBestOp);
}
 
 
// register algorithm parameters
void ParticleSwarmOptimization::registerParameters(StateP state)
{
    registerParameter(state, "weightType", (voidP) new InertiaWeightType(CONSTANT), ECF::INT,
        "weight type update: 0 - constant, 1 - time dependant (based on max generations)");
    registerParameter(state, "weight", (voidP) new double(0.8), ECF::DOUBLE,
        "initial inertia weight (either constant or time dependant)");
    registerParameter(state, "maxVelocity", (voidP) new double(10.0), ECF::DOUBLE,
        "max particle velocity");
    registerParameter(state, "bounded", (voidP) new uint(0), ECF::UINT, 
        "should the algorithm respect the bounds defined in the genotype or not (default: 0)");
}
 
 
bool ParticleSwarmOptimization::initialize(StateP state)
{
    // initialize all operators
    selBestOp->initialize(state);
 
    voidP weightType = getParameterValue(state, "weightType");
    m_weightType = *((InertiaWeightType*) weightType.get());
 
    voidP weight = getParameterValue(state, "weight");
    m_weight = *((double*) weight.get());
 
    voidP maxV = getParameterValue(state, "maxVelocity");
    m_maxV = *((double*) maxV.get());
 
    // test if inertia weight type is time variant and if so, check if max iterations specified
    if(m_weightType == TIME_VARIANT) {
        if(state->getRegistry()->isModified("term.maxgen")) {
            // read maxgen parameter
            m_maxIter = *(boost::static_pointer_cast<int>( state->getRegistry()->getEntry("term.maxgen") ));
        }
        else {
            ECF_LOG_ERROR(state, "Error: term.maxgen has to be specified in order to use time variant inertia eight in PSO algorithm");
            throw("");
        }
    }
 
    // algorithm accepts a single FloatingPoint or Binary genotype 
    // or a genotype derived from the abstract RealValueGenotype class
    GenotypeP activeGenotype = state->getGenotypes()[0];
    RealValueGenotypeP rv = boost::dynamic_pointer_cast<RealValueGenotype> (activeGenotype);
    if(!rv || state->getGenotypes().size() > 1) {
        ECF_LOG_ERROR(state, "Error: PSO algorithm accepts only a RealValueGenotype derived genotype! (FloatingPoint or Binary)");
        throw ("");
    }
 
    voidP sptr = state->getGenotypes()[0]->getParameterValue(state, "dimension");
    uint numDimension = *((uint*) sptr.get());
 
    voidP bounded = getParameterValue(state, "bounded");
    bounded_ = *((bool*) bounded.get());
 
    sptr = state->getGenotypes()[0]->getParameterValue(state, "lbound");
    lbound_ = *((double*) sptr.get());
 
    sptr = state->getGenotypes()[0]->getParameterValue(state, "ubound");
    ubound_ = *((double*) sptr.get());
 
    // batch run check
    if(areGenotypesAdded_)
        return true;
 
    FloatingPointP flpoint[4];
    for(uint iGen = 1; iGen < 4; iGen++) {
 
        flpoint[iGen] = (FloatingPointP) new FloatingPoint::FloatingPoint;
        state->setGenotype(flpoint[iGen]);
 
        if(iGen == 3)
            flpoint[iGen]->setParameterValue(state, "dimension", (voidP) new uint(1));
        else
            flpoint[iGen]->setParameterValue(state, "dimension", (voidP) new uint(numDimension));
 
        // other parameters are proprietary (ignored by the algorithm)
        flpoint[iGen]->setParameterValue(state, "lbound", (voidP) new double(0));
        flpoint[iGen]->setParameterValue(state, "ubound", (voidP) new double(1));
    }
    ECF_LOG(state, 1, "PSO algorithm: added 3 FloatingPoint genotypes (particle velocity, best-so-far postition, best-so-far fitness value)");
 
    // mark adding of genotypes
    areGenotypesAdded_ = true;
 
    return true;
}
 
 
bool ParticleSwarmOptimization::advanceGeneration(StateP state, DemeP deme)
{
//       a) For each particle:
//          1) If the fitness value is better than the best fitness value (pBest) in  history
//          2) Set current value as the new pBest
//          End
//       b) For each particle:
//          1) Find, in the particle neighborhood, the particle with the best fitness
//          2) Calculate particle velocity according to the velocity equation (1)
//          3) Apply the velocity constriction
//          4) Update particle position according to the position equation (2)
//          5) Apply the position constriction
//          6) Calculate fitness value
//          End
 
    // a)
    for( uint i = 0; i < deme->getSize(); i++ ) { // for each particle 
        IndividualP particle = deme->at(i);
 
        // the whole point of this section is to compare fitness and pbest
        FloatingPointP flp = boost::static_pointer_cast<FloatingPoint::FloatingPoint> (particle->getGenotype(3));
        double &particlePbestFitness = flp->realValue[0];
        double fitness = particle->fitness->getValue();
 
        flp = boost::static_pointer_cast<FloatingPoint::FloatingPoint> (particle->getGenotype(0));
        std::vector< double > &positions = flp->realValue;
 
        flp = boost::static_pointer_cast<FloatingPoint::FloatingPoint> (particle->getGenotype(2));
        std::vector< double > &pbestx = flp->realValue;
 
        // set particle pbestx-es
        if( /*iter == 0 ||*/ fitness < particlePbestFitness ) { // minimize error
            particlePbestFitness = fitness;
 
            // set pbestx-es
            for( uint j = 0;j<pbestx.size();j++ ) {
                pbestx[j] = positions[j];
            }
        }
        // NOTE store best particle index?
    }
 
    // b)
    for( uint i = 0; i < deme->getSize(); i++ ) { // for each particle
        IndividualP particle = deme->at(i);
 
        IndividualP bestParticle = selBestOp->select( *deme );
 
        FloatingPointP flp = boost::static_pointer_cast<FloatingPoint::FloatingPoint> (particle->getGenotype(0));
        std::vector< double > &positions = flp->realValue;
 
        flp = boost::static_pointer_cast<FloatingPoint::FloatingPoint> (particle->getGenotype(1));
        std::vector< double > &velocities = flp->realValue;
 
        flp = boost::static_pointer_cast<FloatingPoint::FloatingPoint> (particle->getGenotype(2));
        std::vector< double > &pbestx = flp->realValue;
 
 
        double weight_up;
 
        switch( m_weightType )
        {
            //time variant weight, linear from weight to 0.4
            case TIME_VARIANT:
            weight_up = ( m_weight - 0.4 ) * ( m_maxIter - state->getGenerationNo() ) / m_maxIter + 0.4;
            break;
 
            // constant inertia weight
            case CONSTANT:
            default:
            weight_up = m_weight;
            break;
        }
        // calculate particle velocity according to the velocity equation (1)
        flp = boost::static_pointer_cast<FloatingPoint::FloatingPoint> (bestParticle->getGenotype(2));
        std::vector< double > &bestParticlesPbestx = flp->realValue;
        for( uint j = 0; j < velocities.size(); j++ ) {
            double velocity;
 
            velocity = weight_up * velocities[j] +
               2 * (rand()/(float)RAND_MAX) * (pbestx[j] - positions[j]) +
               2 * (rand()/(float)RAND_MAX) * (bestParticlesPbestx[j] - positions[j]);
            if( velocity > m_maxV ) velocity = m_maxV;
            velocities[j] = velocity;
 
            positions[j] += velocities[j];
            // TODO apply position constriction
 
            // check for bounds
            if(bounded_) {
                if(positions[j] < lbound_)
                    positions[j] = lbound_;
                if(positions[j] > ubound_)
                    positions[j] = ubound_;
            }
        }
 
        // determine new particle fitness
        evaluate( particle );
    }
 
    return true;
}