AI-ParticleSwarmOptimization-Pmap

 view release on metacpan or  search on metacpan

README  view on Meta::CPAN

            -workers                => 4,                                                   
        );
        
        my $fitValue       = $pso->optimize ();
        my ($best)         = $pso->getBestParticles (1);
        my ($fit, @values) = $pso->getParticleBestPos ($best);
    
        printf "Fit %.4f at (%s)\n",
            $fit, join ', ', map {sprintf '%.4f', $_} @values;
    
        sub calcFit {
            my @values = @_;
            my $offset = int (-@values / 2);
            my $sum;
            
            select( undef, undef, undef, 0.01 );    # Simulation of heavy processing...
        
            $sum += ($_ - $offset++) ** 2 for @values;
            return $sum;
        }

README  view on Meta::CPAN

	elements. User provided parameters are passed as the first
	parameters to the fitness function when it is called:

            my $pso = AI::ParticleSwarmOptimization::Pmap->new(
                -fitFunc    => [\&calcFit, $context],
                -dimensions => 3,
            );
        
            ...
        
            sub calcFit {
                my ($context, @values) = @_;
                ...
                return $fitness;
            }

	In addition to any user provided parameters the list of values
	representing the current particle position in the hyperspace is
	passed in. There is one value per hyperspace dimension.

      -inertia: positive or zero number, optional

example/PSOTest-MultiCore.pl  view on Meta::CPAN

use strict;
use warnings;
use lib '../lib/'; 
#-----------------------------------------------------------------------
#use AI::ParticleSwarmOptimization;
#use AI::ParticleSwarmOptimization::MCE;
use AI::ParticleSwarmOptimization::Pmap;
use Data::Dumper; $::Data::Dumper::Sortkeys = 1;
#=======================================================================
sub calcFit {
    my @values = @_;
    my $offset = int (-@values / 2);
    my $sum;

	select( undef, undef, undef, 0.01 );	# Simulation of heavy processing...

    $sum += ($_ - $offset++) ** 2 for @values;
    return $sum;
}
#=======================================================================

lib/AI/ParticleSwarmOptimization/Pmap.pm  view on Meta::CPAN

$AI::ParticleSwarmOptimization::Pmap::VERSION = '1.008';
use strict;
use warnings;
use base qw( 
	AI::ParticleSwarmOptimization
);
#-----------------------------------------------------------------------
use List::Util 					qw( min );
use Parallel::parallel_map;
#=======================================================================
sub _initParticles {
    my ($self) = @_;

	@{ $self->{ prtcls } } = map {
		my %prt = ( id => $_ );
		$self->_initParticle( \%prt );
		\%prt;
	} 0 .. $self->{ numParticles } - 1;
}
#=======================================================================
# Thanks Mario :-)
my $_pid = $$;
my $_srn = 0;
#-----------------------------------------------------------------------
sub _randInRange {
    my ($self, $min, $max) = @_;
    if( $$ != $_pid and not $_srn ){
        $self->{ rndGen }->set_seed( abs( $$ ) );
        $_srn = 1;
    }
    return $min + $self->{ rndGen }->rand( $max - $min );
}
#=======================================================================
sub _moveParticles {
    my ($self, $iter) = @_;

    print "Iter $iter\n" if $self->{verbose} & AI::ParticleSwarmOptimization::kLogIter;

	my @lst = grep { defined $_ } parallel_map {
		my $prtcl = $_;
		#---------------------------------------------------------------
		@{$prtcl->{currPos}} = @{$prtcl->{nextPos}};
        $prtcl->{currFit} = $prtcl->{nextFit};

lib/AI/ParticleSwarmOptimization/Pmap.pm  view on Meta::CPAN

	@{ $self->{ prtcls } } = map { $_->[ 0 ] } @lst;
	
	$self->{ bestBest } = min map { $_->{ bestFit } } @{ $self->{ prtcls } };
	
	my @fit = map { $_->[ 1 ] } grep { defined $_->[ 1 ] } @lst;

    return scalar( @fit ) ? ( sort { $a <=> $b } @fit )[ 0 ] : undef;
}
#=======================================================================
sub _updateVelocities {
    my ($self, $iter) = @_;

	@{ $self->{ prtcls } } = parallel_map {
		my $prtcl = $_;
		#---------------------------------------------------------------
		my $bestN = $self->{prtcls}[$self->_getBestNeighbour ($prtcl)];
        my $velSq;

        for my $d (0 .. $self->{dimensions} - 1) {
            my $meFactor =

lib/AI/ParticleSwarmOptimization/Pmap.pm  view on Meta::CPAN

        -workers		=> 4,							
    );
    
    my $fitValue       = $pso->optimize ();
    my ($best)         = $pso->getBestParticles (1);
    my ($fit, @values) = $pso->getParticleBestPos ($best);

    printf "Fit %.4f at (%s)\n",
        $fit, join ', ', map {sprintf '%.4f', $_} @values;

    sub calcFit {
        my @values = @_;
        my $offset = int (-@values / 2);
        my $sum;
        
        select( undef, undef, undef, 0.01 );    # Simulation of heavy processing...
    
        $sum += ($_ - $offset++) ** 2 for @values;
        return $sum;
    }

lib/AI/ParticleSwarmOptimization/Pmap.pm  view on Meta::CPAN

the fitness function as following elements. User provided parameters are passed
as the first parameters to the fitness function when it is called:

    my $pso = AI::ParticleSwarmOptimization::Pmap->new(
        -fitFunc    => [\&calcFit, $context],
        -dimensions => 3,
    );

    ...

    sub calcFit {
        my ($context, @values) = @_;
        ...
        return $fitness;
    }

In addition to any user provided parameters the list of values representing the
current particle position in the hyperspace is passed in. There is one value per
hyperspace dimension.

=item I<-inertia>: positive or zero number, optional

t/01_pso_multi.t  view on Meta::CPAN

my $fitValue         = $pso->optimize ();
my ( $best )         = $pso->getBestParticles (1);
my ( $fit, @values ) = $pso->getParticleBestPos ($best);
my $iters            = $pso->getIterationCount();


ok ( $fit > 0, 'Computations');


sub calcFit {
    my @values = @_;
    my $offset = int (-@values / 2);
    my $sum;

    $sum += ($_ - $offset++)**2 for @values;
    return $sum;
}