Pseudorandomness in Central Force Optimization
Journal of Advances in Mathematics and Computer Science · pp. 241–264 · Published 29 Apr 2013
10.9734/BJMCS/2013/3381Abstract
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 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.
Cited by 38
Min Li, Fei Liang, Jie Liu · 2018 14th International Conference on Computational Intelligence and Security (CIS) · 2018
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· Introduction to Nature-Inspired Optimization · 2017
Seyed Hamed Javadi, Seyed Hamid Zahiri · IETE Journal of Research · 2018
Hamed Zandevakili, Esmat Rashedi, Ali Mahani · Soft Computing · 2017
Bo Xing, Wen-Jing Gao · Intelligent Systems Reference Library · 2013
Korany Ragab Mahmoud · IET Microwaves, Antennas & Propagation · 2016
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Robert C. Green · 2017 IEEE Symposium Series on Computational Intelligence (SSCI) · 2017
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Showing 14 of 38 known citations — external sources report more than can currently be individually listed.
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