Development of a solution search method using an advanced flying squirrel algorithm

Authors

DOI:

https://doi.org/10.15587/1729-4061.2023.292574

Keywords:

decision support systems, global optimization, complex processes, bio-inspired algorithms

Abstract

The object of the study is decision support systems. The subject of the study is the decision-making process in management problems using the flying squirrel algorithm (FSA), an advanced genetic algorithm and evolving artificial neural networks.

A solution search method using an advanced FSA is proposed. The study is based on the FSA algorithm for finding a solution regarding the state of an object. Evolving artificial neural networks are used to train FSA, and an advanced genetic algorithm is used to select the best FSA. The method has the following sequence of actions. Input of initial data and setting agents on the search plane take place. After that, numbering FSA in the flock and setting the initial fitness function are carried out. Then, the quality of food in the FSA search area is determined, and the classification of trees (food sources) for FSA is carried out. The next step is the creation of new locations by FSA gliding, formation of the FSA action algorithm in the presence of a predator. After that, the FSA seasonal monitoring conditions are checked, the stop criterion is checked, and new FSA positions are generated taking into account the degree of data noise.

The originality of the proposed method lies in setting FSA taking into account the uncertainty of the initial data, advanced 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 21–25 % due to the use of additional advanced 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

Oleg Sova, The National University of Defense of Ukraine

Doctor of Technical Science, Professor, Deputy Head of Scientific Center

Scientific Center for Building Integrity and Preventing Corruption in the Security and Defense Sector

Oleksandr Zhuk, The National University of Defense of Ukraine

Doctor of Technical Sciences, Associate Professor, Head of Department

Department of Communication Technologies and Cyber Protection

Oksana Petruchenko, Scientific-Research Institute of Military Intelligence

PhD, Assosiate Professor, Senior Researcher

Research Department

Yurii Artabaiev, Research Center for Trophy and Perspective Weapons and Military Equipment

PhD, Head of Department

Research Department of Combat Crews

Oleksandr Trotsko, Military Institute of Telecommunications and Information Technologies named after Heroes of Kruty

PhD, Associate Professor, Head of Department

Department of Automated Control Systems

Olena Shaposhnikova, Kharkiv National Automobile and Highway University

PhD, Associate Professor

Department of Computer Systems

Ruslan Boikо, The National University of Defense of Ukraine

PhD, Senior Researcher, Head of Department

Center for Military and Strategic Studies

Yevhenii Redziuk, Military Institute of Telecommunications and Information Technologies named after Heroes of Kruty

PhD, Associate Professor

Department of Computer Information Technology

Viacheslav Shmyhol, Military Institute of Telecommunications and Information Technologies named after Heroes of Kruty

Senior Lecturer

Department of Combined Arms Disciplines

Vira Velychko, Military Institute of Telecommunications and Information Technologies named after Heroes of Kruty

Lecturer

Department of Automated Control Systems

References

  1. Bashkyrov, O. M., Kostyna, O. M., Shyshatskyi, A. V. (2015). Rozvytok intehrovanykh system zviazku ta peredachi danykh dlia potreb Zbroinykh Syl. Ozbroiennia ta viyskova tekhnika, 1, 35–39. Available at: http://nbuv.gov.ua/UJRN/ovt_2015_1_7
  2. Dudnyk, V., Sinenko, Y., Matsyk, M., Demchenko, Y., Zhyvotovskyi, R., Repilo, I. et al. (2020). Development of a method for training artificial neural networks for intelligent decision support systems. Eastern-European Journal of Enterprise Technologies, 3 (2 (105)), 37–47. doi: https://doi.org/10.15587/1729-4061.2020.203301
  3. Sova, O., Shyshatskyi, A., Salnikova, O., Zhuk, O., Trotsko, O., Hrokholskyi, Y. (2021). Development of a method for assessment and forecasting of the radio electronic environment. EUREKA: Physics and Engineering, 4, 30–40. doi: https://doi.org/10.21303/2461-4262.2021.001940
  4. Pievtsov, H., Turinskyi, O., Zhyvotovskyi, R., Sova, O., Zvieriev, O., Lanetskii, B., Shyshatskyi, A. (2020). Development of an advanced method of finding solutions for neuro-fuzzy expert systems of analysis of the radioelectronic situation. EUREKA: Physics and Engineering, 4, 78–89. doi: https://doi.org/10.21303/2461-4262.2020.001353
  5. Zuiev, P., Zhyvotovskyi, R., Zvieriev, O., Hatsenko, S., Kuprii, V., Nakonechnyi, O. et al. (2020). Development of complex methodology of processing heterogeneous data in intelligent decision support systems. Eastern-European Journal of Enterprise Technologies, 4 (9 (106)), 14–23. doi: https://doi.org/10.15587/1729-4061.2020.208554
  6. Shyshatskyi, A. (2020). Complex Methods of Processing Different Data in Intellectual Systems for Decision Support System. International Journal of Advanced Trends in Computer Science and Engineering, 9 (4), 5583–5590. doi: https://doi.org/10.30534/ijatcse/2020/206942020
  7. Yeromina, N., Kurban, V., Mykus, S., Peredrii, O., Voloshchenko, O., Kosenko, V. et al. (2021). The Creation of the Database for Mobile Robots Navigation under the Conditions of Flexible Change of Flight Assignment. International Journal of Emerging Technology and Advanced Engineering, 11 (5), 37–44. doi: https://doi.org/10.46338/ijetae0521_05
  8. Rotshteyn, A. P. (1999). Intellektual'nye tekhnologii identifikatsii: nechetkie mnozhestva, geneticheskie algoritmy, neyronnye seti. Vinnitsa: “UNIVERSUM”, 320.
  9. Ko, Y.-C., Fujita, H. (2019). An evidential analytics for buried information in big data samples: Case study of semiconductor manufacturing. Information Sciences, 486, 190–203. doi: https://doi.org/10.1016/j.ins.2019.01.079
  10. Ramaji, I. J., Memari, A. M. (2018). Interpretation of structural analytical models from the coordination view in building information models. Automation in Construction, 90, 117–133. doi: https://doi.org/10.1016/j.autcon.2018.02.025
  11. Pérez-González, C. J., Colebrook, M., Roda-García, J. L., Rosa-Remedios, C. B. (2019). Developing a data analytics platform to support decision making in emergency and security management. Expert Systems with Applications, 120, 167–184. doi: https://doi.org/10.1016/j.eswa.2018.11.023
  12. Chen, H. (2018). Evaluation of Personalized Service Level for Library Information Management Based on Fuzzy Analytic Hierarchy Process. Procedia Computer Science, 131, 952–958. doi: https://doi.org/10.1016/j.procs.2018.04.233
  13. Chan, H. K., Sun, X., Chung, S.-H. (2019). When should fuzzy analytic hierarchy process be used instead of analytic hierarchy process? Decision Support Systems, 125, 113114. doi: https://doi.org/10.1016/j.dss.2019.113114
  14. Osman, A. M. S. (2019). A novel big data analytics framework for smart cities. Future Generation Computer Systems, 91, 620–633. doi: https://doi.org/10.1016/j.future.2018.06.046
  15. Gödri, I., Kardos, C., Pfeiffer, A., Váncza, J. (2019). Data analytics-based decision support workflow for high-mix low-volume production systems. CIRP Annals, 68 (1), 471–474. doi: https://doi.org/10.1016/j.cirp.2019.04.001
  16. Harding, J. L. (2013). Data quality in the integration and analysis of data from multiple sources: some research challenges. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-2/W1, 59–63. doi: https://doi.org/10.5194/isprsarchives-xl-2-w1-59-2013
  17. Kosko, B. (1986). Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24 (1), 65–75. doi: https://doi.org/10.1016/s0020-7373(86)80040-2
  18. Koval, M., Sova, O., Shyshatskyi, A., Artabaiev, Y., Garashchuk, N., Yivzhenko, Y. et al. (2022). Improving the method for increasing the efficiency of decision-making based on bio-inspired algorithms. Eastern-European Journal of Enterprise Technologies, 6 (4 (120)), 6–13. doi: https://doi.org/10.15587/1729-4061.2022.268621
  19. Jain, M., Singh, V., Rani, A. (2019). A novel nature-inspired algorithm for optimization: Squirrel search algorithm. Swarm and Evolutionary Computation, 44, 148–175. doi: https://doi.org/10.1016/j.swevo.2018.02.013
  20. Koshlan, A., Salnikova, O., Chekhovska, M., Zhyvotovskyi, R., Prokopenko, Y., Hurskyi, T. et al. (2019). Development of an algorithm for complex processing of geospatial data in the special-purpose geoinformation system in conditions of diversity and uncertainty of data. Eastern-European Journal of Enterprise Technologies, 5 (9 (101)), 35–45. doi: https://doi.org/10.15587/1729-4061.2019.180197
  21. Mahdi, Q. A., Shyshatskyi, A., Prokopenko, Y., Ivakhnenko, T., Kupriyenko, D., Golian, V. et al. (2021). Development of estimation and forecasting method in intelligent decision support systems. Eastern-European Journal of Enterprise Technologies, 3 (9 (111)), 51–62. doi: https://doi.org/10.15587/1729-4061.2021.232718
  22. Gorokhovatsky, V., Stiahlyk, N., Tsarevska, V. (2021). Combination method of accelerated metric data search in image classification problems. Advanced Information Systems, 5 (3), 5–12. doi: https://doi.org/10.20998/2522-9052.2021.3.01
  23. Levashenko, V., Liashenko, O., Kuchuk, H. (2020). Building Decision Support Systems based on Fuzzy Data. Advanced Information Systems, 4 (4), 48–56. doi: https://doi.org/10.20998/2522-9052.2020.4.07
  24. Meleshko, Y., Drieiev, O., Drieieva, H. (2020). Method of identification bot profiles based on neural networks in recommendation systems. Advanced Information Systems, 4 (2), 24–28. doi: https://doi.org/10.20998/2522-9052.2020.2.05
  25. Kuchuk, N., Merlak, V., Skorodelov, V. (2020). A method of reducing access time to poorly structured data. Advanced Information Systems, 4 (1), 97–102. doi: https://doi.org/10.20998/2522-9052.2020.1.14
  26. Shyshatskyi, A., Tiurnikov, M., Suhak, S., Bondar, O., Melnyk, A., Bokhno, T., Lyashenko, A. (2020). Method of assessment of the efficiency of the communication of operational troop grouping system. Advanced Information Systems, 4 (1), 107–112. doi: https://doi.org/10.20998/2522-9052.2020.1.16
  27. Raskin, L., Sira, O. (2016). Method of solving fuzzy problems of mathematical programming. Eastern-European Journal of Enterprise Technologies, 5 (4 (83)), 23–28. doi: https://doi.org/10.15587/1729-4061.2016.81292
  28. Lytvyn, V., Vysotska, V., Pukach, P., Brodyak, O., Ugryn, D. (2017). Development of a method for determining the keywords in the slavic language texts based on the technology of web mining. Eastern-European Journal of Enterprise Technologies, 2 (2 (86)), 14–23. doi: https://doi.org/10.15587/1729-4061.2017.98750
  29. Stepanenko, A., Oliinyk, A., Deineha, L., Zaiko, T. (2018). Development of the method for decomposition of superpositions of unknown pulsed signals using the second­order adaptive spectral analysis. Eastern-European Journal of Enterprise Technologies, 2 (9 (92)), 48–54. doi: https://doi.org/10.15587/1729-4061.2018.126578
  30. Gorbenko, I., Ponomar, V. (2017). Examining a possibility to use and the benefits of post-quantum algorithms dependent on the conditions of their application. Eastern-European Journal of Enterprise Technologies, 2 (9 (86)), 21–32. doi: https://doi.org/10.15587/1729-4061.2017.96321
  31. Koval, M., Sova, O., Orlov, O., Shyshatskyi, A., Artabaiev, Y., Shknai, O. et al. (2022). Improvement of complex resource management of special-purpose communication systems. Eastern-European Journal of Enterprise Technologies, 5 (9 (119)), 34–44. doi: https://doi.org/10.15587/1729-4061.2022.266009
  32. Wang, L., Shi, Y., Liu, S. (2015). An improved fruit fly optimization algorithm and its application to joint replenishment problems. Expert Systems with Applications, 42 (9), 4310–4323. doi: https://doi.org/10.1016/j.eswa.2015.01.048
  33. Yuan, X., Dai, X., Zhao, J., He, Q. (2014). On a novel multi-swarm fruit fly optimization algorithm and its application. Applied Mathematics and Computation, 233, 260–271. doi: https://doi.org/10.1016/j.amc.2014.02.005
Development of a solution search method using an advanced flying squirrel algorithm

Downloads

Published

2023-12-28

How to Cite

Sova, O., Zhuk, O., Petruchenko, O., Artabaiev, Y., Trotsko, O., Shaposhnikova, O., Boikо R., Redziuk, Y., Shmyhol, V., & Velychko, V. (2023). Development of a solution search method using an advanced flying squirrel algorithm. Eastern-European Journal of Enterprise Technologies, 6(4 (126), 14–22. https://doi.org/10.15587/1729-4061.2023.292574

Issue

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

Section

Mathematics and Cybernetics - applied aspects

License

Copyright (c) 2023 Oleg Sova, Oleksandr Zhuk, Oksana Petruchenko, Yurii Artabaiev, Oleksandr Trotsko, Olena Shaposhnikova, Ruslan Boikо, Yevhenii Redziuk, Viacheslav Shmyhol, Vira Velychko

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.

A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.