THE METHOD OF SELF-ORGANIZATION OF INFORMATION NETWORKS IN THE CONDITIONS OF THE COMPLEX INFLUENCE OF DESTABILIZING FACTORS

The experience of modern military conflicts of the past decades and the experience of the Russian-Ukrainian war, requires a fundamental revision of approaches regarding the order of organizing the interaction of information networks and their individual components. Traditional approaches to organization require significant time to organize the interaction of information network elements and also depend significantly on the experience and personal qualities of administrators who operate them. That is why, in this research, the authors proposed a method of organizing information networks under the influence of destabilizing factors. The method of self-organization of information networks consists of the following sequence of actions: input of initial data, display of individuals of the combined flock on the search area, numbering of individuals in the flock of the combined algorithm; determination of the initial speed of individuals of the flock of the combined algorithm; preliminary assessment of the search (feeding) area by individuals of the combined flock; classification of food sources for agents of a combined flock; procedure for optimizing a flock of hawk agents; implementation of the coot herd optimization algorithm; combining individual optimization algorithms into a mixed one; checking the presence of predator agents of the combined flock; escape and fight with predators of combined pack agents; checking the stop criterion; training of knowledge bases of combined swarm agents, determination of the amount of necessary computing resources, intelligent decision making support system. The work of the specified method was modeled on the example of the self-organization of the information network of the operational group of troops (forces). The specified example showed an increase in the efficiency of data processing at the level of 12-17% due to the use of additional improved procedures of adding correction factors for uncertainty and noise of data, selection of combined swarm agents, crossing of different types of swarm optimization approaches, as well as training of combined swarm agents.