• Yurii Shcherbyna National University “Odesa Law Academy”, Odesa, Ukraine
  • Nadiia Kazakova Odessa State Environmental University, Odesa, Ukraine
  • Oleksii Fraze-Frazenko Odessa State Environmental University, Odesa, Ukraine
  • Oleksandr Laptiev Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
  • Andrii Sobchuk State University of Telecommunications, Ukraine



modeling, stochastic process, Xorshift generator, inverse function method, Pearson's chi-square test, numerical stream post-processing


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.

Author Biographies

Yurii Shcherbyna, National University “Odesa Law Academy”, Odesa, Ukraine

Candidate of Technical Sciences, Associate Professor, , associate professor of department of Odesa Law Academy National University

Nadiia Kazakova, Odessa State Environmental University, Odesa, Ukraine

Doctor of technical sciences, professor, head of department of information technology

Oleksii Fraze-Frazenko, Odessa State Environmental University, Odesa, Ukraine

Candidate of Technical Sciences, Associate Professor, associate professor of department of Information Technology

Oleksandr Laptiev, Taras Shevchenko National University of Kyiv, Kyiv, Ukraine

Doctor of technical sciences, senior researcher, Associate Professor the Department of Cyber Security and Information Protection,Faculty of Information Technology

Andrii Sobchuk , State University of Telecommunications, Ukraine

Associate Professor of the Department of Information and Cyber Security

Educational and Scientific Institute of Information Protection


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Information technology, cybersecurity