Development of a solution search method using the improved emperor penguin algorithm

Authors

DOI:

https://doi.org/10.15587/1729-4061.2023.291008

Keywords:

multi-extremal functions, decision support systems, emperor penguin algorithm, optimization

Abstract

The objects of the study are decision support systems. The subject of the study is the decision-making process in management problems using the Emperor Penguin Algorithm (EPA), an advanced genetic algorithm and evolving artificial neural networks.

A solution search method using the improved EPA is proposed. The study is based on the EPA algorithm for finding a solution regarding the object state. Evolving artificial neural networks are used to train EPA, and an advanced genetic algorithm is used to select the best EPA. The method has the following sequence of actions:

– input of initial data;

– setting agents on the search plane;

– numbering EPA in the flock;

– setting the initial velocity of the EPA and thermal radiation of each EPA;

– calculation of the position of each EPA on the total search area and its cost;

– approach (attraction) of the EPA to another EPA;

– changing in the trajectory of EPA movement;

– selection of the best individuals from the EPA flock;

– ranking the obtained solutions and sorting them;

– training EPA knowledge bases;

– determining the amount of necessary computing resources for an intelligent decision support system.

The originality of the proposed method lies in setting EPA taking into account the uncertainty of the initial data, improved global and local search procedures taking into account the noise degree of data on the state of the analysis object. The method makes it possible to increase the efficiency of data processing at the level of 13–17 % 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 interests of solving national security problems

Author Biographies

Andrii Shyshatskyi, Kharkiv National Automobile and Highway University

PhD, Associate Professor, Senior Researcher

Department of Computer Systems

Oleksii Romanov, Scientific-Research Institute of Military Intelligence

PhD, Senior Researcher

Head

Oleh Shknai, Scientific-Research Institute of Military Intelligence

PhD, Senior Researcher, Leading Researcher

Research Department

Vitalina Babenko, Kharkiv National Automobile and Highway University

Doctor of Economic Scinces, Professor, Head of Department

Department of Computer Systems

Oleksandr Koshlan, The National Defence University of Ukraine

PhD, Senior Researcher, Head of Department

Scientific Department of General and Resource Planning

Tetiana Pluhina, Kharkiv National Automobile and Highway University

PhD, Associate Professor

Department of Automation and Computer-Aided Technologies

Alona Biletska, Central Scientifically-Research Institute of Armaments and Military Equipment of the Armed Forces of Ukraine

Researcher

Scientific-Research Laboratory of Automation of Scientific Researches

Tetiana Stasiuk, Military Institute of Telecommunications and Information Technologies named after Heroes of Kruty

Lecturer

Cyclic Commission of General Education Disciplines

Sergeant Military College

Svitlana Kashkevich, National Aviation University

Seniour Lecturer

Department of Computerized Management Systems

Vitalii Kryvosheiev, The National Defence University of Ukraine

PhD, Associate Professor

Department of Command and Control

State Military Management Institute

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Development of a solution search method using the improved emperor penguin algorithm

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Published

2023-12-28

How to Cite

Shyshatskyi, A., Romanov, O., Shknai, O., Babenko, V., Koshlan, O., Pluhina, T., Biletska, A., Stasiuk, T., Kashkevich, S., & Kryvosheiev, V. (2023). Development of a solution search method using the improved emperor penguin algorithm. Eastern-European Journal of Enterprise Technologies, 6(4 (126), 6–13. https://doi.org/10.15587/1729-4061.2023.291008

Issue

Vol. 6 No. 4 (126) (2023): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

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Copyright (c) 2023 Andrii Shyshatskyi, Oleksii Romanov, Oleh Shknai, Vitalina Babenko, Oleksandr Koshlan, Tetiana Pluhina, Alona Biletska, Tetiana Stasiuk, Svitlana Kashkevich, Vitalii Kryvosheiev

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