AI-ParticleSwarmOptimization-Pmap

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README  view on Meta::CPAN

        use AI::ParticleSwarmOptimization::Pmap;
    
        my $pso = AI::ParticleSwarmOptimization::Pmap->new (
            -fitFunc        => \&calcFit,
            -dimensions     => 3,
            -iterations     => 10,
            -numParticles   => 1000,
            
            # only for many-core version # the best if == $#cores of your system
            # selecting best value if undefined
            -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;
        }

Description

    This module is enhancement of on original AI::ParticleSwarmOptimization
    to support multi-core processing with use of Pmap. Below you can find
    original documentation of that module, but with one difference. There

README  view on Meta::CPAN

    This pure Perl module is an implementation of the Particle Swarm
    Optimization technique for finding minima of hyper surfaces. It
    presents an object oriented interface that facilitates easy
    configuration of the optimization parameters and (in principle) allows
    the creation of derived classes to reimplement all aspects of the
    optimization engine (a future version will describe the replaceable
    engine components).

    This implementation allows communication of a local best point between
    a selected number of neighbours. It does not support a single global
    best position that is known to all particles in the swarm.

Methods

    AI::ParticleSwarmOptimization provides the following public methods.
    The parameter lists shown for the methods denote optional parameters by
    showing them in [].

    new (%parameters)

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

#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 $sum;
}
#=======================================================================
++$|;
#-----------------------------------------------------------------------
#my $pso = AI::ParticleSwarmOptimization->new(			# Single-core
#my $pso = AI::ParticleSwarmOptimization::MCE->new(		# Multi-core	
my $pso = AI::ParticleSwarmOptimization::Pmap->new(		# Multi-core	
    -fitFunc    	=> \&calcFit,
    -dimensions 	=> 10,
    -iterations 	=> 10,
    -numParticles	=> 1000,
    
    # only for many-core version # the best if == $#cores of your system
    # selecting best value if undefined
    -workers		=> 4,							
);


my $beg = time;

$pso->init();

my $fitValue         = $pso->optimize ();
my ( $best )         = $pso->getBestParticles (1);

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

    use AI::ParticleSwarmOptimization::Pmap;

    my $pso = AI::ParticleSwarmOptimization::Pmap->new (
        -fitFunc        => \&calcFit,
        -dimensions     => 3,
        -iterations     => 10,
        -numParticles   => 1000,
        
        # only for many-core version # the best if == $#cores of your system
        # selecting best value if undefined
        -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;
    }

=head1 Description

This module is enhancement of on original AI::ParticleSwarmOptimization to support 
multi-core processing with use of Pmap. Below you can find original documentation
of that module, but with one difference. There is new parameter "-workers", which

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

http://en.wikipedia.org/wiki/Particle_swarm_optimization.

This pure Perl module is an implementation of the Particle Swarm Optimization
technique for finding minima of hyper surfaces. It presents an object oriented
interface that facilitates easy configuration of the optimization parameters and
(in principle) allows the creation of derived classes to reimplement all aspects
of the optimization engine (a future version will describe the replaceable
engine components).

This implementation allows communication of a local best point between a
selected number of neighbours. It does not support a single global best position
that is known to all particles in the swarm.

=head1 Methods

AI::ParticleSwarmOptimization provides the following public methods. The parameter lists shown
for the methods denote optional parameters by showing them in [].

=over 4

=item new (%parameters)

t/01_pso_multi.t  view on Meta::CPAN

plan (tests => 1);

# Calculation tests.
my $pso = AI::ParticleSwarmOptimization::Pmap->new (
    -fitFunc        => \&calcFit,
    -dimensions     => 10,
    -iterations     => 10,
    -numParticles   => 1000,

    # only for many-core version # the best if == $#cores of your system
    # selecting best value if undefined
    -workers        => 4,
);

$pso->init();

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