Development of a method for analyzing and forecasting the state of multidimensional objects using a metaheuristic algorithm

Authors

DOI:

https://doi.org/10.15587/1729-4061.2024.313086

Keywords:

multidimensional objects, advanced genetic algorithm, artificial neural networks, swarm algorithms

Abstract

The object of the study is multidimensional objects. The problem solved in the study is to increase the efficiency of assessing the state of multidimensional objects, regardless of the number of dimensions of object state assessment. The subject of the study is the process of assessing the state of multidimensional objects using an advanced butterfly optimization algorithm (BOA), an advanced genetic algorithm and evolving artificial neural networks.

The originality of the study is as follows:

– the initial setting of butterfly agents (BA) on the plane of multidimensional objects is carried out taking into account the type of uncertainty using appropriate correction factors for the degree of awareness of nectar source locations (in our case, priority search directions);

– adjusting the initial BA velocity allows determining search priority;

– the fitness of BA nectar collection sites is determined, which reduces the time for assessing the state of multidimensional objects;

– the possibility of global restart of the algorithm, which allows the algorithm to go beyond the current optimum and improve the exploration ability, which reduces the time for assessing the state of multidimensional objects;

– the possibility of clarification at the stage of collecting nectar clusters due to ranking nectar sources by the level of stimulus intensity;

– improved ability to select the best BA in comparison with traditional selection using an advanced genetic algorithm.

The proposed method should be used to solve the problems of assessing the state of multidimensional objects under uncertainty and risks characterized by a high degree of complexity. The method showed a 14–16 % increase in the efficiency of assessing the state of multidimensional objects

Author Biographies

Aqeel Bahr Tarkhan, Al Taff University College

PhD, Lecturer

Department of Computer Technologies Engineering

Andrii Lebedynskyi, Kharkiv National Automobile and Highway University

PhD, Associate Professor

Department of Computer Systems

Yurii Dehtiar, National Aviation University

Senior Lecturer

Department of Intelligent Cybernetic Systems

Oleksandr Lytvynenko, Military Institute of Taras Shevchenko National University of Kyiv

PhD, Senior Researcher

Research Department

Research Center

Dmytro Minochkin, National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"

PhD, Associate Professor

Department of Telecommunications

Dmytro Petrukovych, Kharkiv National Automobile and Highway University

PhD, Associate Professor

Department of Metrology and Life Safety

Ihor Pimonov, Kharkiv National Automobile and Highway University

PhD, Associate Professor

Department of Operation, Testing, Service of Construction and Road Machines

Viktor Kosolapov, Kharkiv National Automobile and Highway University

PhD, Associate Professor

Department of Construction and Road-Building Machinery

Dmytro Honcharuk, The National University of Defense of Ukraine

Head of Research Laboratory

Research Laboratory of Informatization Project Management Problems

Center for Military and Strategic Studies

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Development of a method for analyzing and forecasting the state of multidimensional objects using a metaheuristic algorithm

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Published

2024-10-30

How to Cite

Tarkhan, A. B., Sova, O., Lebedynskyi, A., Dehtiar, Y., Lytvynenko, O., Minochkin, D., Petrukovych, D., Pimonov, I., Kosolapov, V., & Honcharuk, D. (2024). Development of a method for analyzing and forecasting the state of multidimensional objects using a metaheuristic algorithm. Eastern-European Journal of Enterprise Technologies, 5(3 (131), 41–47. https://doi.org/10.15587/1729-4061.2024.313086

Issue

Vol. 5 No. 3 (131) (2024): Control processes

Section

Control processes

License

Copyright (c) 2024 Aqeel Bahr Tarkhan, Oleg Sova, Andrii Lebedynskyi, Yurii Dehtiar, Oleksandr Lytvynenko, Dmytro Minochkin, Dmytro Petrukovych, Ihor Pimonov, Viktor Kosolapov, Dmytro Honcharuk

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