AI-PSO

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Revision history for Perl extension AI::PSO.

0.86  Tue Nov 21 20:41:23 2006
    - updated documentation
    - added support for original RE & JK algorithm
    - abstracted initialization function

0.85  Wed Nov 15 22:30:47 2006
    - corrected the fitness function in the test
    - added perceptron c++ code that I wrote a long time ago ;)
    - added an example (pso_ann.pl) for training a simple feed-forward neural network
    - updated POD

0.82  Sat Nov 11 22:20:31 2006
    - fixed POD to correctly 'use AI::PSO'
    - fixed fitness function in PSO.t
	- added research paper to package
	- moved into a subversion repository
	- removed requirement for perl 5.8.8
	- removed printing of solution array in test

0.80  Sat Nov 11 14:22:27 2006

lib/AI/PSO.pm  view on Meta::CPAN

# so in N-dimensional hyperspace, the size of the position vector is N
# 
# A particle updates its position according the Euler integration equation for physical motion:
#   Xi(t) = Xi(t-1) + Vi(t)
#   The velocity portion of this contains the stochastic elements of PSO and is defined as:
#   Vi(t) = Vi(t-1)  +  P1*[pi - Xi(t-1)]  +  P2*[pg - Xi(t-1)]
#   where P1 and P2 add are two random values who's sum adds up to the PSO random range (4.0)
#   and pi is the individual's best location
#   and pg is the global (or neighborhoods) best position
#
#   The velocity vector is obviously updated before the position vector...
#
#
my @particles = ();
my $user_fitness_function;
my @solution = ();
#----------   END GLOBAL DATA STRUCTURES --------


#---------- BEGIN EXPORTED SUBROUTINES ----------

lib/AI/PSO.pm  view on Meta::CPAN

#
# initialize_particles
#    - sets up internal data structures
#    - initializes particle positions and velocities with an element of randomness
#
sub initialize_particles() {
    for(my $p = 0; $p < $numParticles; $p++) {
        $particles[$p]           = {};  # each particle is a hash of arrays with the array sizes being the dimensionality of the problem space
        $particles[$p]{nextPos}  = [];  # nextPos is the array of positions to move to on the next positional update
        $particles[$p]{bestPos}  = [];  # bestPos is the position of that has yielded the best fitness for this particle (it gets updated when a better fitness is found)
        $particles[$p]{currPos}  = [];  # currPos is the current position of this particle in the problem space
        $particles[$p]{velocity} = [];  # velocity ... come on ...

        for(my $d = 0; $d < $dimensions; $d++) {
            $particles[$p]{nextPos}[$d]  = &random($deltaMin, $deltaMax);
            $particles[$p]{currPos}[$d]  = &random($deltaMin, $deltaMax);
            $particles[$p]{bestPos}[$d]  = &random($deltaMin, $deltaMax);
            $particles[$p]{velocity}[$d] = &random($deltaMin, $deltaMax);
        }
    }

lib/AI/PSO.pm  view on Meta::CPAN

            } else {
	    	if($verbose == 1) {
			print "N:$iter:$p:$fitness\n"
		}
		if($verbose == 2) {
			&dump_particle($p);
		}
            }
        }

        ## at this point we've updated our position, but haven't reached the end of the search
        ## so we turn to our neighbors for help.
        ## (we see if they are doing any better than we are, 
        ##  and if so, we try to fly over closer to their position)

        for(my $p = 0; $p < $numParticles; $p++) {
            my $n = &get_index_of_best_fit_neighbor($p);
            my @meDelta = ();       # array of self position updates
            my @themDelta = ();     # array of neighbor position updates
            for(my $d = 0; $d < $dimensions; $d++) {
				if($useModifiedAlgorithm) { # this if shold be moved out much further, but i'm working on code refactoring first