view release on metacpan or  search on metacpan

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

use constant kLogBetter     => 1;
use constant kLogStall      => 2;
use constant kLogIter       => 4;
use constant kLogDetail     => 8;
use constant kLogIterDetail => (kLogIter | kLogDetail);

sub new {
    my ($class, %params) = @_;
    my $self = bless {}, $class;

    $self->setParams (%params);
    return $self;
}


sub setParams {
    my ($self, %params) = @_;

    if (defined $params{-fitFunc}) {
        # Process required parameters - -fitFunc and -dimensions
        if ('ARRAY' eq ref $params{-fitFunc}) {
            ($self->{fitFunc}, @{$self->{fitParams}}) = @{$params{-fitFunc}};
        } else {
            $self->{fitFunc} = $params{-fitFunc};
        }

        $self->{fitParams} ||= [];
    }

    $self->{prtcls} = []    # Need to reinit if num dimensions changed
        if defined $params{-dimensions}
            and defined $self->{dimensions}
            and $params{-dimensions} != $self->{dimensions};

    $self->{$_} = $params{"-$_"} for grep {exists $params{"-$_"}} qw/
        dimensions
        exitFit
        exitPlateau
        exitPlateauDP
        exitPlateauWindow
        exitPlateauBurnin
        inertia
        iterations
        meWeight
        numNeighbors
        numParticles
        posMax
        posMin
        randSeed
        randStartVelocity
        stallSpeed
        themWeight
        verbose
        /;

    die "-dimensions must be greater than 0\n"
        if exists $params{-dimensions} && $params{-dimensions} <= 0;

    if (defined $self->{verbose} and 'ARRAY' eq ref $self->{verbose}) {
        my @log = map {lc} @{$self->{verbose}};
        my %logTypes = (
            better => kLogBetter,
            stall  => kLogStall,
            iter   => kLogIter,
            detail => kLogDetail,
        );

        $self->{verbose} = 0;
        exists $logTypes{$_} and $self->{verbose} |= $logTypes{$_} for @log;
    }

    $self->{numParticles} ||= $self->{dimensions} * 10
        if defined $self->{dimensions};
    $self->{numNeighbors} ||= int sqrt $self->{numParticles}
        if defined $self->{numParticles};
    $self->{iterations}        ||= 1000;
    $self->{exitPlateauDP}     ||= 10;
    $self->{exitPlateauWindow} ||= $self->{iterations} * 0.1;
    $self->{exitPlateauBurnin} ||= $self->{iterations} * 0.5;
    $self->{posMax} = 100 unless defined $self->{posMax};
    $self->{posMin} = -$self->{posMax} unless defined $self->{posMin};
    $self->{meWeight}   ||= 0.5;
    $self->{themWeight} ||= 0.5;
    $self->{inertia}    ||= 0.9;
    $self->{verbose}    ||= 0;

    return 1;
}


sub init {
    my ($self) = @_;

    die "-fitFunc must be set before init or optimize is called"
        unless $self->{fitFunc} and 'CODE' eq ref $self->{fitFunc};
    die
        "-dimensions must be set to 1 or greater before init or optimize is called"
        unless $self->{dimensions} and $self->{dimensions} >= 1;

    my $seed =
        int (exists $self->{randSeed} ? $self->{randSeed} : rand (0xffffffff));

    $self->{rndGen} = Math::Random::MT->new ($seed);
    $self->{usedRandSeed} = $seed;

    $self->{prtcls}         = [];
    $self->{bestBest}       = undef;
    $self->{bestBestByIter} = undef;
    $self->{bestsMean}      = 0;
    $self->_initParticles ();
    $self->{iterCount} = 0;

    # Normalise weights.
    my $totalWeight =
        $self->{inertia} + $self->{themWeight} + $self->{meWeight};

    $self->{inertia}    /= $totalWeight;
    $self->{meWeight}   /= $totalWeight;
    $self->{themWeight} /= $totalWeight;

    die "-posMax must be greater than -posMin"
        unless $self->{posMax} > $self->{posMin};
    $self->{$_} > 0 or die "-$_ must be greater then 0" for qw/numParticles/;

    $self->{deltaMax} = ($self->{posMax} - $self->{posMin}) / 100.0;

    return 1;
}

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

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:

=over 4

=item * kLogBetter

prints particle details when its fit becomes bebtter than its previous best.

=item * kLogStall

prints particle details when its velocity reaches 0 or falls below the stall
threshold.

=item * kLogIter

Shows the current iteration number.

=item * kLogDetail

Shows additional details for some of the other logging options.

=item * kLogIterDetail

Shorthand for C<kLogIter | kLogIterDetail>

=back

=back

=item B<setParams (%parameters)>

Set or change optimization parameters. See I<-new> above for a description of
the parameters that may be supplied.

=item B<init ()>

Reinitialize the optimization. B<init ()> will be called during the first call
to B<optimize ()> if it hasn't already been called.

=item B<optimize ()>

Runs the minimization optimization. Returns the fit value of the best fit
found. The best possible fit is negative infinity.

B<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.

=item B<getParticleState ()>

Returns the vector of position

=item B<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.

=item B<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)

=item B<getIterationCount ()>

Return the number of iterations performed. This may be useful when the
I<-exitFit> criteria has been met or where multiple calls to I<optimize> have
been made.

=back

=head1 BUGS

Please report any bugs or feature requests to C<bug-AI-ParticleSwarmOptimization
at rt.cpan.org>, or through the web interface at
L<http://rt.cpan.org/NoAuth/ReportBug.html?Queue=AI-ParticleSwarmOptimization>.
I will be notified, and then you'll automatically be notified of progress on