Shyshatskyi, A.Kashkevich, S.Kyrychenko, I.,Khakhlyuk, O.Kubrak, V.Kоval A.Kоval O.Protas, N. M.Stryhun, V.Kuzminov, I.Протас, Надія Михайлівна2026-11-132026-11-132024-10-30Methodical approach to assessing the state of hierarchical systems using a metaheuristic algorithm / A. Shyshatskyi et al. Eastern-European Journal of Enterprise Technologies. 2024. №5 (4 (131)). Pp. 82–88. DOI: https://doi.org/10.15587/1729-4061.2024.3112351729-3774UDC 004.81https://dspace.pdau.edu.ua/handle/123456789/22698The object of the study is hierarchical systems. The subject of the study is the process of assessing the state of hierarchical systems using the advanced antlion algorithm (ALA), an advanced genetic algorithm and evolving artificial neural networks. The problem solved in the study is to increase the efficiency of assessing the state of hierarchical systems, regardless of the system hierarchy level. The originality of the study is that: – the initial setting of ALA is carried outtaking into account the type of uncertainty using appropriate correction factors for the degree of awareness of anthill location (priority search directions); – the initial velocity of each ALA is taken into account, which allows determining the priority of search by each ALA in the specified search direction; – the fitness of ALA hunting locations is determined, which reduces the time for assessing the state of the hierarchical system; – the use of the procedure of global restart of the algorithm, which allows the algorithm to go beyond the current optimum and improve the exploration ability of the algorithm, which reduces the time for assessing the state of hierarchical systems; – the possibility of clarifying the choice of an anthill at the hunting stage due to ranking anthills by the level of ant pheromone; – improved ability to select the best ALA in comparison with random selection using an advanced genetic algorithm, which improves the reliability of assessing the state of complex hierarchical systems.encomplex hierarchical systemsgenetic algorithmartificial neural networksswarm algorithmsArticle