AI-ParticleSwarmOptimization-MCE

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README  view on Meta::CPAN

            
            # 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;

README  view on Meta::CPAN

      -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

	  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.

example/PSOTest-MultiCore.pl  view on Meta::CPAN

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 \@chk;
}
#=======================================================================
sub _updateVelocities {
    my ( $self, $iter ) = @_;

	#-------------------------------------------------------------------
    print "Iter $iter\n" if $self->{verbose} & AI::ParticleSwarmOptimization::kLogIter;

	my $tpl = $self->_tpl;

	my @lst = mce_map {
		#- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
		my $ary = $_;
		#- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
		my $arg = clone( $tpl );
		$arg->{ -numParticles } = 1;
		

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

	$self->{ bestBest } = min grep { defined $_ } map { $_->[ 1 ] } @lst;
	
	#-------------------------------------------------------------------
	return;
}
#=======================================================================
sub _moveParticles {
    my ( $self, $iter ) = @_;

	#-------------------------------------------------------------------
    print "Iter $iter\n" if $self->{verbose} & AI::ParticleSwarmOptimization::kLogIter;

	my $tpl = $self->_tpl;

	my @lst = mce_map {
		#- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
		my $ary = $_;
		#- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
		my $arg = clone( $tpl );
		$arg->{ -numParticles } = 1;
		

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

        
        # 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;

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

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.