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

=head1 SYNOPSIS

    use AI::ParticleSwarmOptimization::MCE;

    my $pso = AI::ParticleSwarmOptimization::MCE->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 MCE. 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
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)

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>.

=item I<-fitFunc>: required

I<-fitFunc> is a reference to the fitness function used by the search. If extra
parameters need to be passed to the fitness function an array ref may be used
with the code ref as the first array element and parameters to be passed into
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::MCE->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

Determines what proportion of the previous velocity is carried forward to the
next iteration. Defaults to 0.9

See also I<-meWeight> and I<-themWeight>.

=item I<-iterations>: number, optional

Number of optimization iterations to perform. Defaults to 1000.

=item I<-meWeight>: number, optional