Pseudorandomness in Central Force Optimization

TL;DR

Central Force Optimization (CFO) is a deterministic metaheuristic that uses pseudorandom components to effectively explore decision spaces, achieving competitive performance compared to state-of-the-art algorithms.

Contribution

This paper introduces the use of pseudorandom sequences in CFO, detailing three methods and demonstrating improved performance on benchmark problems.

Findings

CFO with pseudorandom components performs well on benchmark suites.

Pseudorandom methods improve probe distribution and search effectiveness.

CFO remains deterministic despite pseudorandom elements.

Abstract

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.