Pseudorandomness in Central Force Optimization

Formato, Richard A. (2013) Pseudorandomness in Central Force Optimization. British Journal of Mathematics & Computer Science, 3 (3). pp. 241-264. ISSN 2231-0851

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Abstract

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.

Item Type: Article
Subjects: Science Global Plos > Mathematical Science
Depositing User: Unnamed user with email support@science.globalplos.com
Date Deposited: 03 Jul 2023 05:06
Last Modified: 15 Jan 2024 04:29
URI: http://ebooks.manu2sent.com/id/eprint/1207

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