AI-ParticleSwarmOptimization-Pmap
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{
"abstract" : "Particle Swarm Optimization (object oriented) with support for multi-core processing",
"author" : [
"\u0141ukasz Strzelecki <lukasz@strzeleccy.eu>"
],
"dynamic_config" : 0,
"generated_by" : "Dist::Zilla version 6.017, CPAN::Meta::Converter version 2.150010",
"license" : [
"lgpl_2_1"
],
"meta-spec" : {
"url" : "http://search.cpan.org/perldoc?CPAN::Meta::Spec",
---
abstract: 'Particle Swarm Optimization (object oriented) with support for multi-core processing'
author:
- 'Åukasz Strzelecki <lukasz@strzeleccy.eu>'
build_requires: {}
configure_requires:
ExtUtils::MakeMaker: '0'
dynamic_config: 0
generated_by: 'Dist::Zilla version 6.017, CPAN::Meta::Converter version 2.150010'
license: lgpl
meta-spec:
url: http://module-build.sourceforge.net/META-spec-v1.4.html
Makefile.PL view on Meta::CPAN
# This file was automatically generated by Dist::Zilla::Plugin::MakeMaker v6.017.
use strict;
use warnings;
use ExtUtils::MakeMaker;
my %WriteMakefileArgs = (
"ABSTRACT" => "Particle Swarm Optimization (object oriented) with support for multi-core processing",
"AUTHOR" => "\x{141}ukasz Strzelecki <lukasz\@strzeleccy.eu>",
"CONFIGURE_REQUIRES" => {
"ExtUtils::MakeMaker" => 0
},
"DISTNAME" => "AI-ParticleSwarmOptimization-Pmap",
"LICENSE" => "lgpl",
"NAME" => "AI::ParticleSwarmOptimization::Pmap",
"PREREQ_PM" => {
"AI::ParticleSwarmOptimization" => "1.006",
"List::Util" => 0,
NAME
AI::ParticleSwarmOptimization::Pmap - Particle Swarm Optimization
(object oriented) with support for multi-core processing
SYNOPSIS
use AI::ParticleSwarmOptimization::Pmap;
my $pso = AI::ParticleSwarmOptimization::Pmap->new (
-fitFunc => \&calcFit,
-dimensions => 3,
-iterations => 10,
-numParticles => 1000,
my ($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 $sum;
}
Description
This module is enhancement of on original AI::ParticleSwarmOptimization
to support multi-core processing with use of Pmap. Below you can find
original documentation of that module, but with one difference. There
is new parameter "-workers", which one can use to define of number of
parallel processes that will be used during computations.
The Particle Swarm Optimization technique uses communication of the
current best position found between a number of particles moving over a
hyper surface as a technique for locating the best location on the
surface (where 'best' is the minimum of some fitness function). For a
Wikipedia discussion of PSO see
http://en.wikipedia.org/wiki/Particle_swarm_optimization.
This pure Perl module is an implementation of the Particle Swarm
Optimization technique for finding minima of hyper surfaces. It
AI::ParticleSwarmOptimization provides the following public methods.
The parameter lists shown for the methods denote optional parameters by
showing them in [].
new (%parameters)
Create an optimization object. The following parameters may be used:
-workers: positive number, optional
The number of workers (processes), that will be used during
computations.
-dimensions: positive number, required
The number of dimensions of the hypersurface being searched.
-exitFit: number, optional
If provided -exitFit allows early termination of optimize if the
fitness value becomes equal or less than -exitFit.
init ()
Reinitialize the optimization. init () will be called during the
first call to optimize () if it hasn't already been called.
optimize ()
Runs the minimization optimization. Returns the fit value of the best
fit found. The best possible fit is negative infinity.
optimize () may be called repeatedly to continue the fitting process.
The fit processing on each subsequent call will continue from where
the last call left off.
getParticleState ()
Returns the vector of position
getBestParticles ([$n])
Takes an optional count.
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
lib/AI/ParticleSwarmOptimization/Pmap.pm view on Meta::CPAN
$prtcl;
} @{ $self->{ prtcls } };
}
#=======================================================================
1;
__END__
=head1 NAME
AI::ParticleSwarmOptimization::Pmap - Particle Swarm Optimization (object oriented) with support for multi-core processing
=head1 SYNOPSIS
use AI::ParticleSwarmOptimization::Pmap;
my $pso = AI::ParticleSwarmOptimization::Pmap->new (
-fitFunc => \&calcFit,
-dimensions => 3,
-iterations => 10,
-numParticles => 1000,
lib/AI/ParticleSwarmOptimization/Pmap.pm view on Meta::CPAN
my ($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
one can use to define of number of parallel processes that will be used during
computations.
The Particle Swarm Optimization technique uses communication of the current best
position found between a number of particles moving over a hyper surface as a
technique for locating the best location on the surface (where 'best' is the
minimum of some fitness function). For a Wikipedia discussion of PSO see
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
lib/AI/ParticleSwarmOptimization/Pmap.pm view on Meta::CPAN
=over 4
=item new (%parameters)
Create an optimization object. The following parameters may be used:
=over 4
=item I<-workers>: positive number, optional
The number of workers (processes), that will be used during computations.
=item I<-dimensions>: positive number, required
The number of dimensions of the hypersurface being searched.
=item I<-exitFit>: number, optional
If provided I<-exitFit> allows early termination of optimize if the
fitness value becomes equal or less than I<-exitFit>.
lib/AI/ParticleSwarmOptimization/Pmap.pm view on Meta::CPAN
=item B<init ()>
Reinitialize the optimization. B<init ()> will be called during the first call
to B<optimize ()> if it hasn't already been called.
=item B<optimize ()>
Runs the minimization optimization. Returns the fit value of the best fit
found. The best possible fit is negative infinity.
B<optimize ()> may be called repeatedly to continue the fitting process. The fit
processing on each subsequent call will continue from where the last call left
off.
=item B<getParticleState ()>
Returns the vector of position
=item B<getBestParticles ([$n])>
Takes an optional count.
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