Abstract
This paper presents Central Force Optimization, a novel, nature inspired, deterministic search metaheuristic for constrained multi-dimensional optimization. CFO is based on the metaphor of gravitational kinematics. Equations are presented for the positions and accelerations experienced by “probes” that “fly” through the decision space by analogy to masses moving under the influence of gravity. In the physical universe, probe satellites become trapped in close orbits around highly gravitating masses. In the CFO analogy, “mass” corresponds to a user-defined function of the value of an objective function to be maximized. CFO is a simple algorithm that is easily implemented in a compact computer program. A typical CFO implementation is applied to several test functions. CFO exhibits very good performance, suggesting that it merits further study.
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References
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Formato, R.A. (2008). Central Force Optimization: A New Nature Inspired Computational Framework for Multidimensional Search and Optimization. In: Krasnogor, N., Nicosia, G., Pavone, M., Pelta, D. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2007). Studies in Computational Intelligence, vol 129. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78987-1_21
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DOI: https://doi.org/10.1007/978-3-540-78987-1_21
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