/ 
AI-ParticleSwarmOptimization-1.003
River stage one • 2 direct dependents • 2 total dependents
/ AI::ParticleSwarmOptimization

NAME

AI::ParticleSwarmOptimization - Particle Swarm Optimization (object oriented)

SYNOPSIS

use AI::ParticleSwarmOptimization;

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

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

Description

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).

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)

Create an optimization object. The following parameters may be used:

-dimensions: positive number, required

The number of dimensions of the hypersurface being searched.

-exitFit: number, optional

If provided -exitFit specifies allows an early termination of optimize if the fitness value becomes equal or less than -exitFit.

-fitFunc: required

-fitFunc is a reference to the fitness function used by the search. If extra parameters need to be passed to the fitness function and 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->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

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

See also -meWeight and -themWeight.

-iterations: number, optional

Number of optimization iterations to perform. Defaults to 1000.

-meWeight: number, optional

Coefficient determining the influence of the current local best position on the next iterations velocity. Defaults to 0.5.

See also -inertia and -themWeight.

-numNeighbors: positive number, optional

Number of local particles considered to be part of the neighbourhood of the current particle. Defaults to the square root of the total number of particles.

-numParticles: positive number, optional

Number of particles in the swarm. Defaults to 10 times the number of dimensions.

-posMax: number, optional

Maximum coordinate value for any dimension in the hyper space. Defaults to 100.

-posMin: number, optional

Minimum coordinate value for any dimension in the hyper space. Defaults to --posMax (if -posMax is negative -posMin should be set more negative).

-randSeed: number, optional

Seed for the random number generator. Useful if you want to rerun an optimization, perhaps for benchmarking or test purposes.

-randStartVelocity: boolean, optional

Set true to initialize particles with a random velocity. Otherwise particle velocity is set to 0 on initalization.

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.

-verbose: number, optional

If set to a non-zero value -verbose determines the level of diagnostic print reporting that is generated during optimization.

setParams (%parameters)

Set or change optimization parameters. See -new above for a description of the parameters that may be supplied.

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.

getParticleState ()

Returns the vector of position

getBestParticles ([$n])

Takes an optional count.

Returns a list containing the best $n prtcl numbers. If $n is not specified only the best prtcl number is returned.

getParticleBestPos ($particleNum)

Returns a list containing the best value of the fit and the vector of its point in hyper space.

my ($fit, @vector) = $pso->getParticleBestPos (3)
getIterationCount ()

Return the number of iterations performed. This may be useful when the -exitFit criteria has been met or where multiple calls to optimize have been made.

BUGS

Please report any bugs or feature requests to bug-AI-PSO-OO at rt.cpan.org, or through the web interface at http://rt.cpan.org/NoAuth/ReportBug.html?Queue=AI-PSO-OO. I will be notified, and then you'll automatically be notified of progress on your bug as I make changes.

SUPPORT

This module is supported by the author through CPAN. The following links may be of assistance:

SEE ALSO

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

ACKNOWLEDGEMENTS

This module is an evolution of the AI::PSO module created by Kyle Schlansker.

AUTHOR

Peter Jaquiery
CPAN ID: GRANDPA
grandpa@cpan.org

COPYRIGHT AND LICENSE

This program is free software; you can redistribute it and/or modify it under the same terms as Perl itself.

The full text of the license can be found in the LICENSE file included with this module.