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,
# 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
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.
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:
-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.
-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 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::Pmap->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.
-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
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).
-exitPlateauBurnin: number, optional
Determines how many iterations to run before checking for plateaus.
Defaults to 50% of the number of iterations (-iterations).
-verbose: flags, optional
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:
* kLogBetter
prints particle details when its fit becomes bebtter than its
previous best.
* kLogStall
prints particle details when its velocity reaches 0 or falls
below the stall threshold.
* kLogIter
Shows the current iteration number.
* kLogDetail
Shows additional details for some of the other logging options.
* kLogIterDetail
Shorthand for kLogIter | kLogIterDetail
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.
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.
Returns a list containing the best $n particle numbers. If $n is not
specified only the best particle number is returned.
getParticleBestPos ($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
None... I hope.
If any: A small script which yields the problem will probably be of
help.
SEE ALSO
http://en.wikipedia.org/wiki/Particle_swarm_optimization
AUTHOR
Strzelecki Lukasz
SEE ALSO
AI::ParticleSwarmOptimization AI::ParticleSwarmOptimization::Pmap
COPYRIGHT
Copyright (c) Strzelecki Lukasz. All rights reserved. This program is
free software; you can redistribute it and/or modify it under the same
terms as Perl itself.