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

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Abstract

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.

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  1. P.O. Box 1714, Harwich, MA, 02645, USA

    Richard A. Formato

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  1. Richard A. Formato
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Correspondence to Richard A. Formato.

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Formato, R.A. Central force optimization: A new deterministic gradient-like optimization metaheuristic. OPSEARCH 46, 25–51 (2009). https://doi.org/10.1007/s12597-009-0003-4

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