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        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