AI::ParticleSwarmOptimization - Particle Swarm Optimization (object oriented)
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; }
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.
AI::ParticleSwarmOptimization provides the following public methods. The parameter lists shown for the methods denote optional parameters by showing them in [].
Create an optimization object. The following parameters may be used:
The number of dimensions of the hypersurface being searched.
If provided -exitFit allows early termination of optimize if the fitness value becomes equal or less than -exitFit.
-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->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.
Determines what proportion of the previous velocity is carried forward to the next iteration. Defaults to 0.9
See also -meWeight and -themWeight.
Number of optimization iterations to perform. Defaults to 1000.
Coefficient determining the influence of the current local best position on the next iterations velocity. Defaults to 0.5.
See also -inertia and -themWeight.
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.
Number of particles in the swarm. Defaults to 10 times the number of dimensions.
Maximum coordinate value for any dimension in the hyper space. Defaults to 100.
Minimum coordinate value for any dimension in the hyper space. Defaults to --posMax (if -posMax is negative -posMin should be set more negative).
Seed for the random number generator. Useful if you want to rerun an optimization, perhaps for benchmarking or test purposes.
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.
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.
Coefficient determining the influence of the neighbourhod best position on the next iterations velocity. Defaults to 0.5.
See also -inertia and -meWeight.
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).
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.
Specify the size of the window used to calculate the mean for comparison to the current output of the fitness function. Correlates to the minimum size of a plateau needed to end the optimization.
Defaults to 10% of the number of iterations (-iterations).
Determines how many iterations to run before checking for plateaus.
Defaults to 50% of the number of iterations (-iterations).
If set to a non-zero value -verbose determines the level of diagnostic print reporting that is generated during optimization.
The following constants may be bitwise ored together to set logging options:
prints particle details when its fit becomes bebtter than its previous best.
prints particle details when its velocity reaches 0 or falls below the stall threshold.
Shows the current iteration number.
Shows additional details for some of the other logging options.
Shorthand for kLogIter | kLogIterDetail
Set or change optimization parameters. See -new above for a description of the parameters that may be supplied.
Reinitialize the optimization. init () will be called during the first call to optimize () if it hasn't already been called.
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.
Returns the vector of position
Takes an optional count.
Returns a list containing the best $n particle numbers. If $n is not specified only the best particle number is returned.
Returns a list containing the best value of the fit and the vector of its point in hyper space.
my ($fit, @vector) = $pso->getParticleBestPos (3)
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.
Please report any bugs or feature requests to bug-AI-ParticleSwarmOptimization at rt.cpan.org
, or through the web interface at http://rt.cpan.org/NoAuth/ReportBug.html?Queue=AI-ParticleSwarmOptimization. I will be notified, and then you'll automatically be notified of progress on your bug as I make changes.
This module is supported by the author through CPAN. The following links may be of assistance:
http://rt.cpan.org/NoAuth/Bugs.html?Dist=AI-ParticleSwarmOptimization
http://en.wikipedia.org/wiki/Particle_swarm_optimization
This module is an evolution of the AI::PSO module created by Kyle Schlansker.
Plateau management code added in version 1.004 contributed by Kevin Balbi.
Peter Jaquiery CPAN ID: GRANDPA grandpa@cpan.org
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.