Central force optimization: A new deterministic gradient-like optimization metaheuristic

This paper introduces central force optimization as a new, nature-inspired metaheuristic for multidimensional search and optimization based on the metaphor of gravitational kinematics. CFO is a “gradient-like” deterministic algorithm that explores a decision space by “flying” a group of “probes” whose trajectories are governed by equations analogous to the equations of gravitational motion in the physical universe. This paper suggests the possibility of creating a new “hyperspace directional derivative” using the Unit Step function to create positive-definite “masses” in “CFO space.” A simple CFO implementation is tested against several recognized benchmark functions with excellent results, suggesting that CFO merits further investigation.