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 analogoues 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 testd against several recognized benchmark functions with excellent results, suggesting that CFO merits further investigations.