Development of a solution search method using an improved fish school algorithm

Authors

DOI:

https://doi.org/10.15587/1729-4061.2023.284315

Keywords:

multi-agent systems, decision support systems, complex processes, fish school algorithm

Abstract

The object of research is decision support systems. The subject of research is the decision-making process in management problems using the fish school (FSH) algorithm, an advanced genetic algorithm and evolving artificial neural networks.

A solution search method using an improved FSH algorithm is proposed. The study is based on the FSH algorithm for finding a solution on the object state. For training FSH, evolving artificial neural networks are used. The method has the following sequence of actions:

– input of initial data;

– processing of initial data taking into account the degree of uncertainty;

– checking the fitness function of the solution found;

– procedure of feeding fish agents (FA);

– instinctive-collective FA swimming;

– calculation of the center of school gravity;

– collective voluntary FA swimming;

– changing the FA swimming parameters;

– training of FA knowledge bases.

The originality of the proposed method lies in the arrangement of FA taking into account the uncertainty of the initial data, improved global and local search procedures taking into account the degree of noise of data about the state of the analysis object. The peculiarity of the proposed method is the use of an improved FA training procedure. The training procedure consists in learning 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 use of the method makes it possible to increase the efficiency of data processing at the level of 18–25 % due to the use of additional improved procedures. The proposed method should be used to solve the problems of evaluating complex and dynamic processes in the interest of solving national security problems

Author Biographies

Aqeel Bahr Tarkhan, Al-Taff University College

PhD, Lecturer of Department

Department of Computer Technologies Engineering

Yurii Zhuravskyi, Zhytomyr Military Institute named after S. P. Korolyov

Doctor of Technical Sciences, Senior Researcher, Head of Department

Department of Electrical Engineering and Electronics

Andrii Shyshatskyi, National Aviation University

PhD, Senior Researcher, Associate Professor

Department of Computerized Management Systems

Tetiana Pluhina, Kharkiv National Automobile and Highway University

PhD, Associate Professor

Department of Automation and Computer-Aided Technologies

Volodymyr Dudnyk, The National Defence University of Ukraine named after Ivan Cherniakhovskyi

PhD, Associate Professor

Department of Management of Troops (Forces) in Peace

Ihor Kiris, Scientific-Research Institute of Military Intelligence

Senior Researcher

Oleksii Nalapko, Central Scientifically-Research Institute of Armaments and Military Equipment of the Armed Forces of Ukraine

PhD, Senior Researcher

Scientific-Research Laboratory of Automation of Scientific Researches

Nadiia Protas, Poltava State Agrarian University

PhD, Associate Professor

Department of Information Systems and Technologies

Serhii Neronov, Kharkiv National Automobile and Highway University

Senior Lecturer

Department of Computer Technologies and Mechatronics

Vitaliy Nechyporuk, National Aviation University

PhD, Associate Professor

Department of Computerized Management Systems

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Development of a solution search method using an improved fish school algorithm

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Published

2023-08-31

How to Cite

Tarkhan, A. B., Zhuravskyi, Y., Shyshatskyi, A., Pluhina, T., Dudnyk, V., Kiris, I., Nalapko, O., Protas, N., Neronov, S., & Nechyporuk, V. (2023). Development of a solution search method using an improved fish school algorithm . Eastern-European Journal of Enterprise Technologies, 4(4 (124), 27–33. https://doi.org/10.15587/1729-4061.2023.284315

Issue

Vol. 4 No. 4 (124) (2023): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

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Copyright (c) 2023 Aqeel Bahr Tarkhan, Yurii Zhuravskyi, Andrii Shyshatskyi, Tetiana Pluhina, Volodymyr Dudnyk, Ihor Kiris, Oleksii Nalapko, Nadiia Protas, Serhii Neronov, Vitaliy Nechyporuk

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