Methodical approach to assessing the state of hierarchical systems using a metaheuristic algorithm
DOI:
https://doi.org/10.15587/1729-4061.2024.311235Keywords:
complex hierarchical systems, genetic algorithm, artificial neural networks, swarm algorithmsAbstract
The 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 out taking 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.
The proposed methodical approach provides a 22–25 % increase in the efficiency of assessing the state of hierarchical systems by using additional advanced procedures. The proposed methodical approach should be used to solve the problems of assessing the state of complex hierarchical systems under uncertainty and risks characterized by a high degree of complexity.
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Copyright (c) 2024 Andrii Shyshatskyi, Svitlana Kashkevich, Igor Kyrychenko, Oleksiy Khakhlyuk, Volodymyr Kubrak, Andrii Kоval, Oleksandr Kоval, Nadiia Protas, Vitalii Stryhun, Ievgenii Kuzminov
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