AI-ParticleSwarmOptimization-Pmap
view release on metacpan or search on metacpan
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
A range based on 1/100th of --posMax - -posMin is used for the
initial speed in each dimension of the velocity vector if a random
start velocity is used.
-stallSpeed: positive number, optional
Speed below which a particle is considered to be stalled and is
repositioned to a new random location with a new initial speed.
By default -stallSpeed is undefined but particles with a speed of 0
will be repositioned.
-themWeight: number, optional
Coefficient determining the influence of the neighbourhod best
position on the next iterations velocity. Defaults to 0.5.
See also -inertia and -meWeight.
-exitPlateau: boolean, optional
Set true to have the optimization check for plateaus (regions where
the fit hasn't improved much for a while) during the search. The
optimization ends when a suitable plateau is detected following the
burn in period.
Defaults to undefined (option disabled).
-exitPlateauDP: number, optional
Specify the number of decimal places to compare between the current
fitness function value and the mean of the previous
-exitPlateauWindow values.
Defaults to 10.
-exitPlateauWindow: number, optional
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 ) = $pso->getBestParticles (1);
lib/AI/ParticleSwarmOptimization/Pmap.pm view on Meta::CPAN
if ($self->_betterFit ($fit, $prtcl->{bestFit})) {
# Save position - best fit for this particle so far
$self->_saveBest ($prtcl, $fit, $iter);
}
my $ret;
if( defined $self->{exitFit} and $fit < $self->{exitFit} ){
$ret = $fit;
}elsif( !($self->{verbose} & AI::ParticleSwarmOptimization::kLogIterDetail) ){
$ret = undef;
}else{
printf "Part %3d fit %8.2f", $prtcl->{id}, $fit
if $self->{verbose} >= 2;
printf " (%s @ %s)",
join (', ', map {sprintf '%5.3f', $_} @{$prtcl->{velocity}}),
join (', ', map {sprintf '%5.2f', $_} @{$prtcl->{currPos}})
if $self->{verbose} & AI::ParticleSwarmOptimization::kLogDetail;
print "\n";
$ret = undef;
}
#---------------------------------------------------------------
[ $prtcl, $ret ]
} @{ $self->{ prtcls } };
@{ $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;
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
A range based on 1/100th of -I<-posMax> - I<-posMin> is used for the initial
speed in each dimension of the velocity vector if a random start velocity is
used.
=item I<-stallSpeed>: positive number, optional
Speed below which a particle is considered to be stalled and is repositioned to
a new random location with a new initial speed.
By default I<-stallSpeed> is undefined but particles with a speed of 0 will be
repositioned.
=item I<-themWeight>: number, optional
Coefficient determining the influence of the neighbourhod best position on the
next iterations velocity. Defaults to 0.5.
See also I<-inertia> and I<-meWeight>.
=item I<-exitPlateau>: boolean, optional
Set true to have the optimization check for plateaus (regions where the fit
hasn't improved much for a while) during the search. The optimization ends when
a suitable plateau is detected following the burn in period.
Defaults to undefined (option disabled).
=item I<-exitPlateauDP>: number, optional
Specify the number of decimal places to compare between the current fitness
function value and the mean of the previous I<-exitPlateauWindow> values.
Defaults to 10.
=item I<-exitPlateauWindow>: number, optional
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();
( run in 1.081 second using v1.01-cache-2.11-cpan-d80b1682f3f )