Development of an evaluation method using a combined cat swarm optimization algorithm

Authors

DOI:

https://doi.org/10.15587/1729-4061.2024.305363

Keywords:

mentor cats, combined approach, genetic algorithm, global and local optimization

Abstract

The object of the study is complex dynamic objects with a hierarchical structure. The problem solved in the study is to increase the efficiency of decision-making while ensuring the given reliability. The subject of the study is the process of decision-making in management problems using an improved cat swarm optimization algorithm (CSO), an improved genetic algorithm and evolving artificial neural networks.

The proposed method, due to additional and improved procedures, allows you:

– to take into account the type of uncertainty of the initial data for setting CA for the local search procedure;

– to implement adaptive strategies for finding food sources by CA;

– to take into account the experience of the most authoritative CA while conducting local and global search;

– to take into account the available computing resources of the state analysis system of complex dynamic objects and determine their required amount for involvement;

– to take into account the CA search priority;

– to determine the best CA using an improved genetic algorithm;

– to conduct training of knowledge bases, which is carried out by training the synaptic weights of the artificial neural network, the type and parameters of the membership function, and architecture of individual elements and the architecture of the artificial neural network as a whole;

– to avoid the local extremum problem by using the jump procedure.

The proposed method was tested on the example of solving the problem of determining the composition of an operational group of troops (forces) and elements of its operational structure. An example of using the method showed an increase in the efficiency of data processing at the level of 14–19 % by using additional improved procedures.

The proposed approach should be used to solve the problems of evaluating complex and dynamic processes characterized by a high degree of complexity

Author Biographies

Aqeel Bahr Tarkhan, Al-Taff University College

PhD, Lecturer

Department of Computer Technologies Engineering

Heorhii Kuchuk, National Technical University “Kharkiv Polytechnic Institute”

Doctor of Technical Sciences, Professor

Department of Computer Engineering and Programming

Iraida Stanovska, Odesа Polytechnic National University

Doctor of Technical Sciences, Professor

Department of Advanced Mathematics and Systems Modelling

Vira Golian, Kharkiv National University of Radio Electronics

PhD, Associate Professor

Department of Software Engineering

Nataliia Golian, Kharkiv National University of Radio Electronics

PhD, Associate Professor

Department of Software Engineering

Oksana Zharova, Odesа Polytechnic National University

PhD, Associate Professor

Department of Advanced Mathematics and Systems Modelling

Yevhen Kryzhanivskyi, State Enterprise “State Kyiv Design Bureau “LUCH”

PhD, Head of Sector

Andrii Liubarets, State Enterprise “State Kyiv Design Bureau “LUCH”

PhD, Head of Department

Igor Zvershkhovskyi, State Enterprise “State Kyiv Design Bureau “LUCH”

PhD, Chief Designer - Head of Department

Artem Fysiuk, Military Institute of Telecommunications and Information Technologies named after Heroes of Kruty

Senior Lecturer

Department of Combat Use of Communication Units

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Development of an evaluation method using a combined cat swarm optimization algorithm

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Published

2024-06-28

How to Cite

Tarkhan, A. B., Kuchuk, H., Stanovska, I., Golian, V., Golian, N., Zharova, O., Kryzhanivskyi, Y., Liubarets, A., Zvershkhovskyi, I., & Fysiuk, A. (2024). Development of an evaluation method using a combined cat swarm optimization algorithm. Eastern-European Journal of Enterprise Technologies, 3(4 (129), 55–63. https://doi.org/10.15587/1729-4061.2024.305363

Issue

Vol. 3 No. 4 (129) (2024): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

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Copyright (c) 2024 Aqeel Bahr Tarkhan, Heorhii Kuchuk, Iraida Stanovska, Vira Golian, Nataliia Golian, Oksana Zharovа, Yevhen Kryzhanivskyi, Andrii Liubarets, Igor Zvershkhovskyi, Artem Fysiuk

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