NAME

PSO - Perl module for running the Particle Swarm Optimization algorithm

SYNOPSIS

  use AI::PSO;

  my %params = (
      numParticles   => 4,      # total number of particles involved in search (there is a trade-off between cooperation and time here if the fitness function takes a while...)
      numNeighbors   => 3,      # number of particles that each particle will share its progress with (degree of cooperation)
      maxIterations  => 1000,   # maximum number of iterations before exiting with no solution found
      dimensions     => 4,      # this must be the number of parameters you want to optimize (it will also be the size of the array passed to your fitness function)
      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 weight (than group) means trust individual more, neighbors less)
      meMin          => 0.0,    # 'individuality' minimum random weight (this should really be between 0, 1)
      meMax          => 1.0,    # 'individuality' maximum random weight (this should really be between 0, 1)
      themWeight     => 2.0,    # 'social' weighting constant (higher weight (than individual) means trust group more, self less)
      themMin        => 0.0,    # 'social' minimum random weight (this should really be between 0, 1)
      themMax        => 1.0,    # 'social' maximum random weight (this should really be between 0, 1)
      exitFitness    => 1.0,    # minimum fitness to achieve before exiting (if maxIterations is reached before, then program will exit with no solution)
      verbose        => 0,      # 0 prints solution, 1 prints particle:fitness at each iteration, 2 dumps each particle (+1)
  );

  sub custom_fitness_function(@input) { 
        # this is a callback function.  @input will be passed to this, you do not need to worry about setting it...
        # ... do something with @input which is an array of floats
        # return a value in [0,1] with 0 being the worst and 1 being the best
  }

  pso_set_params(\%params);
  pso_register_fitness_function('custom_fitness_function');
  pso_optimize();
  my @solutionArray = pso_get_solution_array();

General Guidelines

I suggest that meWeight and themWeight add up up to 4.0
If you have a large search space, increasing deltaMin and deltaMax and delta max can help cover more area Conversely, if you have a small search space, then decreasing them will fine tune the search.
You really should set meMin and themMin to 0, and meMin and themMin to 1 unless you know what you are doing.
I've personally found that using a global (fully connected) topology where each particle is neighbors with all other particles (numNeighbors == numParticles - 1) converges more quickly. However, this will drastically increase the number of calls to your fitness function. So, if your fitness function is the bottleneck, then you should tune this value for the appropriate time/accuracy trade-off.
The number of particles also increases cooperation and search space coverage at the expense of compute. Typical applications should suffice using 20-40 particles.
NOTE: I force people to define all parameters, but guidelines 1-3 are standard and pretty safe.

DESCRIPTION

  It is a cooperative approach to optimization.  Instead of an evolutionary 
  approach which kills off unsuccessful members of the search team, each 
  particle in PSO shares its information with its neighboring particles.
  So, if one particle is not doing to well (has a low fitness), then it looks 
  to its neighbors for help and tries to be more like them while still 
  maintaining a sense of individuality.

  A particle is defined by its position and velocity.  The parameters a user 
  wants to optimize define the dimension of the problem hyperspace.  So, if 
  you want to optimize three variables, a particle will be three dimensional 
  and will have 3 position values, 3 velocity values etc.

  Particles fly around the problem hyperspace looking for local/global maxima.  
  At each position, a particle computes its fitness.  If it does not meet the 
  exit criteria then it gets information from neighboring particles about how well 
  they are doing.  If a neighboring particle is doing better, then the current 
  particle tries to move closer to its neighbor by adjusting its weights.  The 
  velocity controls how quickly a particle changes location in the problem 
  hyperspace.  There are also some stochastic weights involved in the positional 
  updates so that each particle is truly independent and can take its own search 
  path while still incorporating good information from other particles.

  Solution convergence is quite fast once one particle becomes close to a local 
  maxima.  Having more particles active means there is more of a chance that you 
  will not be stuck in a local maxima.  Often times different neighborhoods 
  (when not configured in a global neighborhood fashion) will converge to different 
  maxima.  It is quite interesting to watch graphically.

  The algorithm implemented in this module is taken from the book Swarm Intelligence 
  by Russell Eberhart and James Kennedy.  I highly suggest you read the book if you 
  are interested in this sort of thing.  There are a few minor implementation changes 
  I have made, but the heart of the algorithm is as stated in the book.

EXPORTED FUNCTIONS

pso_set_params(%config_hash)

  Sets the particle swarm configuration parameters to use for the search.

pso_register_fitness_function()


  Sets the user defined fitness function to call.  The fitness function should 
  return a value between 0 and 1.  Users may want to look into the sigmoid 
  function [1 / (1+e^(-x))] and it's variants to implement this.
  Also, you may want to take a look at either t/PSO.t for the simple test or 
  examples/NeuralNetwork/pso_ann.pl for an example on how to train a simple 3-layer 
  feed forward neural network.  (Note that a real training application would have a 
  real dataset with many 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 neuraal 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 :)).

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()

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!).

EXAMPLES

examples/NeuralNet/pso_ann.pl =item * t/PSO.t

AUTHOR

W. Kyle Schlansker kylesch@gmail.com

COPYRIGHT AND LICENSE

This code is released under the Mozilla Public License Version 1.1. A copy of this license may be found along with this module or at: http://www.mozilla.org/MPL/MPL-1.1.txt

PSO - Perl module for running the Particle Swarm Optimization algorithm