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=head1 DESCRIPTION OF ALGORITHM

  Particle Swarm Optimization is an optimization algorithm designed by 
  Russell Eberhart and James Kennedy from Purdue University.  The 
  algorithm itself is based off of the emergent behavior among societal 
  groups ranging from marching of ants, to flocking of birds, to 
  swarming of bees.

  PSO is a cooperative approach to optimization rather than an 
  evolutionary approach which kills off unsuccessful members of the 
  search team.  In the swarm framework each particle, is a relatively 
  unintelligent search agent.  It is in the collective sharing of 
  knowledge that solutions are found.  Each particle simply 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 dimensionality of the problem 
  hyperspace.  So, if you want to optimize three variables, a particle