The development of the method of evaluation of complex hierarchical systems based on improved alforitm of particle swarm

Authors

DOI:

https://doi.org/10.15587/2706-5448.2023.288055

Keywords:

complex hierarchical real-time systems, responsiveness, particle swarm, global and local optimization

Abstract

The scientific task, which is solved in the research, is to increase the efficiency of the evaluation of complex hierarchical real-time systems. Finding solutions to nonlinear optimization problems and especially global optimization problems is one of the most popular problems in computational mathematics. In applied problems, the objective function, as a rule, has a large number of variables, is not given in an analytical form and is calculated as some integral characteristic of a complex dynamic process. The development of effective methods, to a certain extent adaptive to the variability of the objective function, is especially relevant in connection with the development of computer technology and the possibility of using parallel computing systems. The conducted research was aimed at developing a method of evaluating complex hierarchical systems based on an improved particle swarm. At the same time, the object of research was complex hierarchical real-time systems. The subject of research is the functioning of real-time hierarchical systems.

The novelties of the proposed method consist in:

‒ creating a multi-level and interconnected description of complex systems of hierarchical real-time systems;

‒ increasing the efficiency of decision making while evaluating complex systems of hierarchical real-time systems;

‒ solving the problem of falling into global and local extremes while assessing the state of complex systems of hierarchical real-time systems;

‒ the possibilities of directed search by several individuals of the particles swarm in a given direction, taking into account the degree of uncertainty;

‒ the possibilities of re-analysis of the state of complex systems of hierarchical real-time systems;

‒ avoiding the problem of loops while visualizing the state of the national security system in real time.

It is advisable to implement the specified method in specialized software, which is used to analyze the state of complex systems of hierarchical real-time systems and make management decisions.

Author Biographies

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-Integrated Technologies

Ganna Plekhova, Kharkiv National Automobile and Highway University

PhD, Associate Professor

Department of Informatics and Applied Mathematics

Anzhela Binkovska, Kharkiv National Automobile and Highway University

PhD, Associate Professor

Department of Automation and Computer-Integrated Technologies

Sergii Pronin, Kharkiv National Automobile and Highway University

PhD, Associate Professor

Department of Computer Systems

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

Lecturer

Cyclic Commission of General Education Disciplines

Sergeant Military College

Oleksii Nalapko, Central Scientifically-Research Institute of Armaments and Military Equipments 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

Tetiana Pliushch, Kyiv National University of Construction and Architecture

Assistant

Department of Geoinformatics and Photogrammetry

Dmytro Burlak, Research Institute of Military Intelligence

Senior Researcher

References

  1. Shevchenko, A. I., Baranovskyi, S. V., Bilokobylskyi, O. V., Bodianskyi, Ye. V., Bomba, A. Ya. et al.; Shevchenko, A. I. (Ed.) (2023). Stratehiia rozvytku shtuchnoho intelektu v Ukraini. Kyiv: IPShI, 305.
  2. Shyshatskyi, A. V., Bashkyrov, O. M., Kostyna, O. M. (2015). Rozvytok intehrovanykh system zv’iazku ta peredachi danykh dlia potreb Zbroinykh Syl. Ozbroiennia ta viiskova tekhnika, 1 (5), 35–40.
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. Rotshtein, A. P. (1999). Intellektualnye tekhnologii identifikatcii: nechetkie mnozhestva, geneticheskie algoritmy, neironnye seti. Vinnitca: UNIVERSUM, 320.
  11. 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
  12. 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
  13. Gorelova, G. V. (2013). Kognitivnyi podkhod k imitatcionnomu modelirovaniiu slozhnykh sistem. Izvestiia IuFU. Tekhnicheskie nauki, 3, 239–250.
  14. Orouskhani, M., Orouskhani, Y., Mansouri, M., Teshnehlab, M. (2013). A Novel Cat Swarm Optimization Algorithm for Unconstrained Optimization Problems. International Journal of Information Technology and Computer Science, 5 (11), 32–41. doi: https://doi.org/10.5815/ijitcs.2013.11.04
The development of the method of evaluation of complex hierarchical systems based on improved alforitm of particle swarm

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Published

2023-09-29

How to Cite

Shyshatskyi, A., Pluhina, T., Plekhova, G., Binkovska, A., Pronin, S., Stasiuk, T., Nalapko, O., Protas, N., Pliushch, T., & Burlak, D. (2023). The development of the method of evaluation of complex hierarchical systems based on improved alforitm of particle swarm. Technology Audit and Production Reserves, 6(2(74), 15–19. https://doi.org/10.15587/2706-5448.2023.288055

Issue

Section

Systems and Control Processes