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lib/AI/PSO.pm  view on Meta::CPAN

my $meMin      = 'null';               # 'individuality' minimum random weight (this should really be between 0, 1)
my $meMax      = 'null';               # 'individuality' maximum random weight (this should really be between 0, 1)
my $themWeight = 'null';               # 'social' weighting constant (higher weight (than individual) means trust group more, self less)
my $themMin    = 'null';               # 'social' minimum random weight (this should really be between 0, 1)
my $themMax    = 'null';               # 'social' maximum random weight (this should really be between 0, 1)

my $psoRandomRange = 'null';           # PSO::.86 new variable to support original unmodified algorithm
my $useModifiedAlgorithm = 'null';

#-#-# user/debug parameters #-#-#
my $verbose    = 0;                    # This one defaults for obvious reasons...

#NOTE: $meWeight and $themWeight should really add up to a constant value.  
#      Swarm Intelligence defines a 'pso random range' constant and then computes two random numbers
#      within this range by first getting a random number and then subtracting it from the range.
#      e.g. 
#           $randomRange = 4.0
#           $meWeight   = random(0, $randomRange);
#           $themWeight = $randomRange - $meWeight.
#
#

lib/AI/PSO.pm  view on Meta::CPAN

}



#
# initialize_neighbors
# NOTE: I made this a separate subroutine so that different topologies of neighbors can be created and used instead of this.
# NOTE: This subroutine is currently not used because we access neighbors by index to the particle array rather than storing their references
# 
#  - adds a neighbor array to the particle hash data structure
#  - sets the neighbor based on the default neighbor hash function
#
sub initialize_neighbors() {
    for(my $p = 0; $p < $numParticles; $p++) {
        for(my $n = 0; $n < $numNeighbors; $n++) {
            $particles[$p]{neighbor}[$n] = $particles[&get_index_of_neighbor($p, $n)];
        }
    }
}

lib/AI/PSO.pm  view on Meta::CPAN

  make my code nicely formatted and good looking :)).

=item pso_optimize()

  Runs the particle swarm optimization algorithm.  This consists of 
  running iterations of search and many calls to the fitness function 
  you registered with pso_register_fitness_function()

=item pso_get_solution_array()

  By default, pso_optimize() will print out to STDERR the first 
  solution, or the best solution so far if the max iterations were 
  reached.  This function will simply return an array of the winning 
  (or best so far) position of the entire swarm system.  It is an 
  array of floats to be used how you wish (like weights in a 
  neural network!).

=back