AI-PSO

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lib/AI/PSO.pm  view on Meta::CPAN

#---------- BEGIN GLOBAL PARAMETERS ------------

#-#-# search parameters #-#-#
my $numParticles  = 'null';            # This is the number of particles that actually search the problem hyperspace
my $numNeighbors  = 'null';            # This is the number of neighboring particles that each particle shares information with
                                       # which must obviously be less than the number of particles and greater than 0.
                                         # TODO: write code to preconstruct different topologies.  Such as fully connected, ring, star etc.
                                         #       Currently, neighbors are chosen by a simple hash function.  
                                         #       It would be fun (no theoretical benefit that I know of) to play with different topologies.
my $maxIterations = 'null';            # This is the maximum number of optimization iterations before exiting if the fitness goal is never reached.
my $exitFitness   = 'null';            # this is the exit criteria.  It must be a value between 0 and 1.
my $dimensions    = 'null';            # this is the number of variables the user is optimizing


#-#-# pso position parameters #-#-#
my $deltaMin       = 'null';           # This is the minimum scalar position change value when searching
my $deltaMax       = 'null';           # This is the maximum scalar position change value when searching

#-#-# my 'how much do I trust myself verses my neighbors' parameters #-#-#
my $meWeight   = 'null';               # 'individuality' weighting constant (higher weight (than group) means trust individual more, neighbors less)

lib/AI/PSO.pm  view on Meta::CPAN

					# do the PSO position and velocity updates
					$particles[$p]{velocity}[$d] = &clamp_velocity($delta);
					$particles[$p]{nextPos}[$d] = $particles[$p]{currPos}[$d] + $particles[$p]{velocity}[$d];
				}
            }
        }

    }

    #
    # at this point we have exceeded the maximum number of iterations, so let's at least print out the best result so far
    #
    print STDERR "MAX ITERATIONS REACHED WITHOUT MEETING EXIT CRITERION...printing best solution\n";
    my $bestFit = -1;
    my $bestPartIndex = -1;
    for(my $p = 0; $p < $numParticles; $p++) {
    	my $endFit = &compute_fitness(@{$particles[$p]{bestPos}});
	if($endFit >= $bestFit) {
		$bestFit = $endFit;
		$bestPartIndex = $p;
	}

lib/AI/PSO.pm  view on Meta::CPAN

AI::PSO - Module for running the Particle Swarm Optimization algorithm

=head1 SYNOPSIS

  use AI::PSO;

  my %params = (
      numParticles   => 4,     # total number of particles involved in search 
      numNeighbors   => 3,     # number of particles with which each particle will share its progress
      maxIterations  => 1000,  # maximum number of iterations before exiting with no solution found
      dimensions     => 4,     # number of parameters you want to optimize
      deltaMin       => -4.0,  # minimum change in velocity during PSO update
      deltaMax       =>  4.0,  # maximum change in velocity during PSO update
      meWeight       => 2.0,   # 'individuality' weighting constant (higher means more individuality)
      meMin          => 0.0,   # 'individuality' minimum random weight
      meMax          => 1.0,   # 'individuality' maximum random weight
      themWeight     => 2.0,   # 'social' weighting constant (higher means trust group more)
      themMin        => 0.0,   # 'social' minimum random weight 
      themMax        => 1.0,   # 'social' maximum random weight
      exitFitness    => 0.9,   # minimum fitness to achieve before exiting

lib/AI/PSO.pm  view on Meta::CPAN

  input-output pairs...pso_ann.pl is a _very_ simple example.  Also note 
  that the neural network exmaple requires g++.  Type 'make run' in the 
  examples/NeuralNetwork directory to run the example.  Lastly, the 
  neural network c++ code is in a very different coding style.  I did 
  indeed write this, but it was many years ago when I was striving to 
  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



=head1 EXAMPLES