  A particle swarm optimizer (PSO) loosely based on the phenomena of
crystallization and a chaos factor which follows the complimentary error
function is described. The method features three phases: diffusion, directed
motion, and nucleation. During the diffusion phase random walk is the only
contributor to particle motion. As the algorithm progresses the contribution
from chaos decreases and movement toward global best locations is pursued until
convergence has occurred. The algorithm was found to be more robust to local
minima in multimodal test functions than a standard PSO algorithm and is
designed for problems which feature experimental precision.
