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NAME

    AI::ParticleSwarmOptimization::MCE - Particle Swarm Optimization
    (object oriented) with support for multi-core processing

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;
        }

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.

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::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.

      -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.

README  view on Meta::CPAN

      -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

THANKS

    Mario Roy for suggestions about efficiency.

AUTHOR

    Strzelecki Lukasz <lukasz@strzeleccy.eu>

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.