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


        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;


    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

    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 [].

    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

      -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.

      -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

	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

	  * 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.


    None... I hope.

    If any: A small script which yields the problem will probably be of



    Mario Roy for suggestions about efficiency.


    Strzelecki Lukasz <>


    AI::ParticleSwarmOptimization AI::ParticleSwarmOptimization::Pmap


    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.