SELECTION OF RANDOMNESS SOURCE FOR COMPUTER SIMULATION
DOI:
https://doi.org/10.18372/2310-5461.59.17944Keywords:
modeling, stochastic process, Xorshift generator, inverse function method, Pearson's chi-square test, numerical stream post-processingAbstract
The main purpose of evolutionary optimization is to find a combination of parameters (independent variables) that would help maximize or minimize the qualitative, quantitative, and probabilistic characteristics of the problem. Recently, integrated optimization methods have become very common, borrowing the basic principles of their work from wildlife. Researchers are experimenting with different types of representations, for example, evolutionary and genetic algorithms use selection methods and genetic operators. A large number of algorithms based on the swarm method are known.
The artificial bee colony is an optimization method that mimics the behavior of bees, a specific application of cluster intelligence, the main feature of which is that it does not need to understand specific information about the problem, you just need to optimize the problem. Comparing inferiority with the help of the local optimization behavior of each person with an artificial bee finally leads to the appearance in the group of a global optimal value with a higher rate of convergence.
The paper considers the method of solving the optimization problem based on modeling the behavior of the bee colony. Description of the model of the behavior of intelligence agents and forage agents, search mechanisms, and selection of positions in a given neighborhood. The general structure of the optimization process is given. Graphical results are also presented, which prove the possibility of the bee colony method to optimize the results, i.e. from all multiple sources of information, the bee colony method by optimization can significantly limit the number of information sources, identify a narrow range of sources that may be false information. Which in the future will allow you to more accurately identify sources with false information and block them.
References
Mark A. Pinsky, Samuel Karlin. An Introduction to Stochastic Modeling, Fourth Edition., Academic Press, 2010. URL: https://faculty.ksu.edu.sa/ sites/default/files/an_introd_to_stoch_modeling_4th_ed.pdf. (access date 15/07/2023)
Knuth, D. E. The Art of Computer Programming. Volume 2. Seminumerical Algorithms. 3rd edition / D. E. Knuth. – Boston, Mass, USA : Addison-Wesley, Longman Publishing, 1997. – 762 p. ISBN 0-201-89683-4. URL: https://www.pdfdrive. com/art-of-computer-programming-knuth-vol-v2-e57538699.html. (access date 18/07/2023)
Bruce Schneier. Applied Cryptography, Second Edition: Protocols, Algorthms, and Source Code in C. 1996. Wiley Computer Publishing, John Wiley & Sons, Inc. URL: https://dut.edu.ua/uploads/ l_1134_27449793.pdf (access date 15/07/2023)
Lemire Daniel. Fast Random Integer Generation in an Interval. ACM Transactions on Modeling and Computer SimulationVolume 29. Issue 1. Article No.: 3 pp 1–12 URL: https://arxiv.org/pdf/ 1805.10941.pdf (access date 15/07/2023)
Tin Ni Ni Kyaw, Akio Tsuneda. Generation of chaos-based random bit sequences with prescribed auto-correlations by post-processing using linear feedback shift registers. DOI: 10.1587/nolta.8.224
Mario Stipčević, True Random Number Generators. Open Problems in Mathematics and Computational Science, Open Problems in Mathematics and Computational Science 275–315) doi:10.1007/978-3-319-10683-0_12
J. von Neumann. Various techniques for use in connection with random digits. Applied Math Series, Notes by G. E. Forsythe, in National Bureau of Standards, Vol. 12, 36–38, 1951. URL: https://mcnp.lanl.gov/pdf_files/nbs_vonneumann.pdf. (access date 15/07/2023)
Trevisan L. Extractors and Pseudorandom Generators 1999. Journal of the ACM URL: http://theory.stanford.edu/~trevisan/pubs/extractor-full.pdf. (access date 25/07/2023)
Siew-Hwee Kwok, Yen-Ling Ee, Guanhan Chew, Kanghong Zheng, Khoongming Khoo, Chik-How Tan. A Comparison of Post-Processing Techniques for Biased Random Number Generators. WISTP 2011: Information Security Theory and Practice. Security and Privacy of Mobile Devices in Wireless Communication. PP. 175–190. URL: https://link.springer.com/content/pdf/10.1007/978-3-540-74619-5_9.pdf. (access date 15/07/2023)
Yurii Shcherbina, Nadiia Kazakova, Oleksii Fraze-Frazenko. Using the Xorshift generator to simulate stochastic processes. Processing, transmission and security of information – 5 December 2022. URL: https://www.engineerxxi. ath.eu/publikacja/processing-transmission-and-security-of-information-2022/.
George Marsaglia. Xorshift RNGs. 2003. DOI:10.18637/jss.v008.i14.
Laptiev, O., Sobchuk, V.,Subach, I., Barabash, A., Salanda, I. The Method of Detecting Radio Signals Using the Approximation of Spectral Function. CEUR Workshop Proceedings, 2022, 3384, pp. 52– 61.
Valentyn Sobchuk , Iryna Zelenska and Oleksandr Laptiev. Algorithm for solution of systems of singularly perturbed differential equations with a differential turning point. Bulletin of the Polish Academy of Sciences Technical Sciences, Vol.71, No 3, 2023, Article number: e145682. DOI: 10.24425/bpasts. 2023.145682.