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lib/AI/ParticleSwarmOptimization/Pmap.pm  view on Meta::CPAN

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.

=item I<-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 I<-meWeight> and I<-themWeight>.

=item I<-iterations>: number, optional

Number of optimization iterations to perform. Defaults to 1000.

=item I<-meWeight>: number, optional

Coefficient determining the influence of the current local best position on the
next iterations velocity. Defaults to 0.5.

See also I<-inertia> and I<-themWeight>.

=item I<-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.

=item I<-numParticles>: positive number, optional

Number of particles in the swarm. Defaults to 10 times the number of dimensions.

=item I<-posMax>: number, optional

Maximum coordinate value for any dimension in the hyper space. Defaults to 100.

=item I<-posMin>: number, optional

Minimum coordinate value for any dimension in the hyper space. Defaults to
-I<-posMax> (if I<-posMax> is negative I<-posMin> should be set more negative).

=item I<-randSeed>: number, optional

Seed for the random number generator. Useful if you want to rerun an
optimization, perhaps for benchmarking or test purposes.

=item I<-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 -I<-posMax> - I<-posMin> is used for the initial
speed in each dimension of the velocity vector if a random start velocity is
used.

=item I<-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 I<-stallSpeed> is undefined but particles with a speed of 0 will be
repositioned.

=item I<-themWeight>: number, optional

Coefficient determining the influence of the neighbourhod best position on the
next iterations velocity. Defaults to 0.5.

See also I<-inertia> and I<-meWeight>.

=item I<-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).

=item I<-exitPlateauDP>: number, optional

Specify the number of decimal places to compare between the current fitness
function value and the mean of the previous I<-exitPlateauWindow> values.

Defaults to 10.

=item I<-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 (I<-iterations>).

=item I<-exitPlateauBurnin>: number, optional

Determines how many iterations to run before checking for plateaus.

Defaults to 50% of the number of iterations (I<-iterations>).

=item I<-verbose>: flags, optional

If set to a non-zero value I<-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: