AI-ParticleSwarmOptimization-MCE
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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);
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
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 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
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
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.
example/PSOTest-MultiCore.pl view on Meta::CPAN
return $sum;
}
#=======================================================================
++$|;
#-----------------------------------------------------------------------
#my $pso = AI::ParticleSwarmOptimization::Pmap->new( # Multi-core
my $pso = AI::ParticleSwarmOptimization::MCE->new( # Multi-core
#my $pso = AI::ParticleSwarmOptimization->new( # Single-core
-fitFunc => \&calcFit,
-dimensions => 10,
-iterations => 10,
-numParticles => 1000,
# only for many-core version # the best if == $#cores of your system
# selecting best value if undefined
-workers => 4,
);
my $beg = time;
$pso->init();
my $fitValue = $pso->optimize ();
my ( $best ) = $pso->getBestParticles (1);
my ( $fit, @values ) = $pso->getParticleBestPos ($best);
my $iters = $pso->getIterationCount();
printf "Fit %.4f at (%s) after %d iterations\n", $fit, join (', ', map {sprintf '%.4f', $_} @values), $iters;
warn "\nTime: ", time - $beg, "\n\n";
#=======================================================================
exit 0;
lib/AI/ParticleSwarmOptimization/MCE.pm view on Meta::CPAN
#-------------------------------------------------------------------
return $self;
}
#=======================================================================
sub _init_tpl {
my ( $self, $params ) = @_;
my $cln = clone( $params );
delete $cln->{ $_ } for qw(
-iterCount
-iterations
-numParticles
-workers
_pop
_tpl
_wrk
);
$self->_tpl( $cln );
lib/AI/ParticleSwarmOptimization/MCE.pm view on Meta::CPAN
AI::ParticleSwarmOptimization::MCE - Particle Swarm Optimization (object oriented) with support for multi-core processing
=head1 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);
lib/AI/ParticleSwarmOptimization/MCE.pm view on Meta::CPAN
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
lib/AI/ParticleSwarmOptimization/MCE.pm view on Meta::CPAN
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).
lib/AI/ParticleSwarmOptimization/MCE.pm view on Meta::CPAN
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
lib/AI/ParticleSwarmOptimization/MCE.pm view on Meta::CPAN
=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
None... I hope.
If any: A small script which yields the problem will probably be of help.
t/01_pso_multi.t view on Meta::CPAN
Test AI::ParticleSwarmOptimization::MCE
=cut
plan (tests => 1);
# Calculation tests.
my $pso = AI::ParticleSwarmOptimization::MCE->new (
-fitFunc => \&calcFit,
-dimensions => 10,
-iterations => 10,
-numParticles => 1000,
# only for many-core version # the best if == $#cores of your system
# selecting best value if undefined
-workers => 4,
);
$pso->init();
my $fitValue = $pso->optimize ();
( run in 1.704 second using v1.01-cache-2.11-cpan-71847e10f99 )