Development of a methodical approach to assessing the state of hierarchical systems using a meta-heuristic approach

Authors

DOI:

https://doi.org/10.15587/1729-4061.2024.309030

Keywords:

hierarchical systems, artificial neural networks, swarm algorithms, optimization, multimodal functions

Abstract

Optimization is a complex process of defining a set of solutions for a wide range of problems, including management decision-making.

One of the approaches to increasing the efficiency of solving optimization problems is metaheuristic algorithms. The problem solved in the study is to increase decision-making efficiency in the problems of assessing the state of hierarchical systems while ensuring a given reliability, regardless of its hierarchy. The object of the study is hierarchical systems. The subject of the study is the decision-making process in management problems using an advanced Tasmanian devil algorithm (TDA) and evolving artificial neural networks. A methodical approach using a metaheuristic algorithm is proposed. For TDA training, evolving artificial neural networks are used.

The originality of the proposed method lies in setting TDA taking into account the uncertainty of the initial data, improved global and local search procedures. Also, the originality of the study lies in determining TDA feeding locations, which allows prioritizing the search in a given direction. The next original element of the study is the possibility of choosing a TDA hunting strategy, which allows a rational use of available system computing resources. Another original element of the study is determining the initial velocity of each TDA. This makes it possible to optimize the speed of exploration by each TDA in a specific direction. Using the methodical approach provides a 14–17 % increase in data processing efficiency by using additional improved procedures. The proposed methodical approach should be used to solve the problems of evaluating hierarchical systems

Author Biographies

Illia Dmytriiev, Kharkiv National Automobile and Highway University

Doctor of Economic Sciences, Professor

Department of Management

Nina Kuchuk, National Technical University “Kharkiv Polytechnic Institute”

Doctor of Technical Sciences, Professor

Department of Computer Engineering and Programming

Oleksandr Stanovskyi, Odesа Polytechnic National University

Doctor of Technical Sciences, Professor

Department of Integrated Management Technologies

Oleksandr Yefymenko, Kharkiv National Automobile and Highway University

PhD, Professor, Associate Professor

Department of Construction and Road Vehicles

Ganna Plekhova, Kharkiv National Automobile and Highway University

PhD, Associate Professor

Department of Informatics and Applied Mathematics

Yuliia Vakulenko, Poltava State Agrarian University

PhD, Associate Professor

Department of Information Systems and Technologies

Nadiia Protas, Poltava State Agrarian University

PhD, Associate Professor

Department of Information Systems and Technologies

Larisa Degtyareva, Poltava State Agrarian University

PhD, Associate Professor

Department of Information Systems and Technologies

Nataliia Apenko, National Aviation University

PhD, Associate Professor

Department of Computerized Control Systems

Maksym Sainog, State Enterprise “State Kyiv Design Bureau “LUCH”

PhD

Chief Designer – Head of Department

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Development of a methodical approach to assessing the state of hierarchical systems using a meta-heuristic approach

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Published

2024-08-30

How to Cite

Dmytriiev, I., Kuchuk, N., Stanovskyi, O., Yefymenko, O., Plekhova, G., Vakulenko, Y., Protas, N., Degtyareva, L., Apenko, N., & Sainog, M. (2024). Development of a methodical approach to assessing the state of hierarchical systems using a meta-heuristic approach. Eastern-European Journal of Enterprise Technologies, 4(4 (130), 6–14. https://doi.org/10.15587/1729-4061.2024.309030

Issue

Vol. 4 No. 4 (130) (2024): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

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Copyright (c) 2024 Illia Dmytriiev, Nina Kuchuk, Oleksandr Stanovskyi, Oleksandr Yefymenko, Ganna Plekhova, Yuliia Vakulenko, Nadiia Protas, Larisa Degtyareva, Nataliia Apenko, Maksym Sainog

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