  Central Force Optimization is a deterministic metaheuristic for an
evolutionary algorithm that searches a decision space by flying probes whose
trajectories are computed using a gravitational metaphor. CFO benefits
substantially from the inclusion of a pseudorandom component (a numerical
sequence that is precisely known by specification or calculation but otherwise
arbitrary). The essential requirement is that the sequence is uncorrelated with
the decision space topology, so that its effect is to pseudorandomly distribute
probes throughout the landscape. While this process may appear to be similar to
the randomness in an inherently stochastic algorithm, it is in fact
fundamentally different because CFO remains deterministic at every step. Three
pseudorandom methods are discussed (initial probe distribution, repositioning
factor, and decision space adaptation). A sample problem is presented in detail
and summary data included for a 23-function benchmark suite. CFO's performance
is quite good compared to other highly developed, state-of-the-art algorithms.
Includes corrections 02-03-2010.
