| Autor: |
Shyshatskyi, Andrii, Dmytriieva, Oksana, Lytvynenko, Oleksandr, Borysov, Ihor, Vakulenko, Yuliia, Mukashev, Temerbay, Mordovtsev, Oleksandr, Kashkevich, Svitlana, Lyashenko, Anna, Velychko, Vira |
| Předmět: |
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| Zdroj: |
Eastern-European Journal of Enterprise Technologies; 2024, Vol. 129 Issue 4, p44-54, 11p |
| Abstrakt: |
The object of the study is complex dynamic objects. The subject of the study is the decision-making process in the problems of managing complex dynamic objects. A method of assessing the state of dynamic objects using a combined swarm algorithm is proposed. The research is based on a combined swarm algorithm - for finding a solution to the state of dynamic objects with a hierarchical structure. To train the individuals of the combined swarm algorithm (CSA), evolving artificial neural networks are used, and to select the best in the combined swarm algorithm, an improved genetic algorithm is used. The originality of the method is: – in taking into account the type of uncertainty and noise of data during the operation of the combined swarm algorithm due to the use of appropriate correction factors; – in the implementation of adaptive strategies for the search for food sources due to setting appropriate search priorities; – in taking into account the presence of a predator while choosing food sources by the flock agents of the combined swarm algorithm, which allows excluding unwanted search areas; – in the additional consideration of the available computing resources of the state analysis system of complex dynamic objects while determining the maximum permissible parameters of the combined swarm algorithm; – in the possibility of changing the search area and speed of movement by separate individuals of the flock of the combined swarm algorithm; – in determining the best individuals of the flock of the combined swarm algorithm using an improved genetic algorithm; – in training knowledge bases, carried out by training the synaptic weights of the artificial neural network, the type and parameters of the membership function, the architecture of individual elements and the architecture of the artificial neural network as a whole. The method makes it possible to increase the efficiency of data processing at the level of 14–20 % by using additional improved procedures. The proposed method should be used to solve problems of evaluating complex dynamic objects. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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