The development of management methods based on bio-inspired algorithms
Keywords:
Optimization problems, dynamic objects, multiagent systems, genetic algorithms, reliability and adequacySynopsis
In this chapter of the research, management methods based on bio-inspired algorithms are proposed. The basis of the research is the theory of artificial intelligence. The method is aimed at solving optimization tasks, variable solutions are defined in such a way that complex dynamic objects work in their best point (mode) according to the optimization criteria determined. In the research, the authors proposed:
– management method using a bio-inspired algorithm;
– method of finding solutions using the improved flying squirrel algorithm;
– method of assessing the state of dynamic objects using the population algorithm.
Each of the methods was based on canonical optimization algorithms, but they were improved by the authors of the research.
The essence of the improvement of these methods, which is the scientific novelty of each of them:
– the initial position of the agents is determined taking into account the type of uncertainty due to the use of a correction factor for the degree of awareness of the state of the initial situation in relation to the object of analysis;
– the initial speed of each agent is taken into account, which makes it possible to research complex functions;
– the speed of movement is regulated according to the priority of finding a solution;
– using the procedure of deep learning of knowledge bases of algorithm agents due to learning both architecture and parameters of artificial neural networks;
– select the best individuals in the flock by using an improved genetic algorithm, which improves the reliability of the decisions made.
A limitation of the research is the need to have an initial condition database complex dynamic object, the need to take into account the time delay for collecting and proving information from sources of information extraction.
It is advisable to use the proposed approach to solve the tasks of evaluating complex and dynamic processes characterized by a high degree of complexity.
References
Shyshatskyi, A. V., Bashkyrov, O. M., Kostyna, O. M. (2015). Rozvytok intehrovanykh system zviazku ta peredachi danykh dlia potreb Zbroinykh Syl. Ozbroiennia ta viiskova tekhnika, 1 (5), 35–40.
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. https://doi.org/10.15587/1729-4061.2020.203301
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. https://doi.org/10.21303/2461-4262.2021.001940
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. https://doi.org/10.21303/2461-4262.2020.001353
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. https://doi.org/10.15587/1729-4061.2020.208554
Shyshatskyi, A., Zvieriev, O., Salnikova, O., Demchenko, Ye., Trotsko, O., Neroznak, Ye. (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. https://doi.org/10.30534/ijatcse/2020/206942020
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. https://doi.org/10.46338/ijetae0521_05
Shyshatskyi, A., Stasiuk, T., Odarushchenko, E., Berezanska, K., Demianenko, H. (2023). Method of assessing the state of hierarchical objects based on bio-inspired algorithms. Advanced Information Systems, 7 (3), 44–48. https://doi.org/10.20998/2522-9052.2023.3.06
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. https://doi.org/10.1016/j.ins.2019.01.079
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. https://doi.org/10.1016/j.autcon.2018.02.025
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. https://doi.org/10.1016/j.eswa.2018.11.023
Chen, H. (2018). Evaluation of Personalized Service Level for Library Information Management Based on Fuzzy Analytic Hierarchy Process. Procedia Computer Science, 131, 952–958. https://doi.org/10.1016/j.procs.2018.04.233
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. https://doi.org/10.1016/j.dss.2019.113114
Osman, A. M. S. (2019). A novel big data analytics framework for smart cities. Future Generation Computer Systems, 91, 620–633. https://doi.org/10.1016/j.future.2018.06.046
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. https://doi.org/10.1016/j.cirp.2019.04.001
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. https://doi.org/10.5194/isprsarchives-xl-2-w1-59-2013
Kosko, B. (1986). Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24 (1), 65–75. https://doi.org/10.1016/s0020-7373(86)80040-2
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. https://doi.org/10.15587/1729-4061.2022.268621
Maccarone, A. D., Brzorad, J. N., Stone, H. M. (2008). Characteristics And Energetics of Great Egret And Snowy Egret Foraging Flights. Waterbirds, 4, 541–549. https://doi.org/10.1675/1524-4695-31.4.541
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. https://doi.org/10.15587/1729-4061.2019.180197
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. https://doi.org/10.15587/1729-4061.2021.232718
Petrovska, I., Kuchuk, H. (2023). Adaptive resource allocation method for data processing and security in cloud environment. Advanced Information Systems, 7 (3), 67–73. https://doi.org/10.20998/2522-9052.2023.3.10
Braik, M., Ryalat, M. H., Al-Zoubi, H. (2021). A novel meta-heuristic algorithm for solving numerical optimization problems: Ali Baba and the forty thieves. Neural Computing and Applications, 34 (1), 409–455. https://doi.org/10.1007/s00521-021-06392-x
Khudov, H., Khizhnyak, I., Glukhov, S., Shamrai, N., Pavlii, V. (2024). The method for objects detection on satellite imagery based on the firefly algorithm. Advanced Information Systems, 8 (1), 5–11. https://doi.org/10.20998/2522-9052.2024.1.01
Poliarush, O., Krepych, S., Spivak, I. (2023). Hybrid approach for data filtering and machine learning inside content management system. Advanced Information Systems, 7 (4), 70–74. https://doi.org/10.20998/2522-9052.2023.4.09
Chalyi, S., Leshchynskyi, V. (2023). Possible evaluation of the correctness of explanations to the end user in an artificial intelligence system. Advanced Information Systems, 7 (4), 75–79. https://doi.org/10.20998/2522-9052.2023.4.10
Raskin, L., Sira, O. (2016). Method of solving fuzzy problems of mathematical programming. Eastern-European Journal of Enterprise Technologies, 5 (4 (83)), 23–28. https://doi.org/10.15587/1729-4061.2016.81292
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. https://doi.org/10.15587/1729-4061.2017.98750
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. https://doi.org/10.15587/1729-4061.2018.126578
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. https://doi.org/10.15587/1729-4061.2017.96321
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. https://doi.org/10.15587/1729-4061.2022.266009
Mahdi, Q. A., Zhyvotovskyi, R., Kravchenko, S., Borysov, I., Orlov, O., Panchenko, I. et al. (2021). Development of a method of structural-parametric assessment of the object state. Eastern-European Journal of Enterprise Technologies, 5 (4 (113)), 34–44. https://doi.org/10.15587/1729-4061.2021.240178
Koval, V., Nechyporuk, O., Shyshatskyi, A., Nalapko, O., Shknai, O., Zhyvylo, Y. et al. (2023). Improvement of the optimization method based on the cat pack algorithm. Eastern-European Journal of Enterprise Technologies, 1 (9 (121)), 41–48. https://doi.org/10.15587/1729-4061.2023.273786
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Pages
35-69
Published
August 16, 2024
Copyright (c) 2024 Svitlana Kashkevich, Andrii Shyshatskyi, Oksana Dmytriieva, Yevhen Zhyvylo, Ganna Plekhova, Serhii Neronov
Details about the available publication format: PDF
PDF
ISBN-13 (15)
978-617-8360-04-7
How to Cite
Kashkevich, S., Shyshatskyi, A., Dmytriieva, O., Neronov, S., Plekhova, G., & Zhyvylo, Y. (2024). The development of management methods based on bio-inspired algorithms. In A. Shyshatskyi (Ed.), INFORMATION AND CONTROL SYSTEMS: MODELLING AND OPTIMIZATIONS (pp. 35–69). Kharkiv: TECHNOLOGY CENTER PC. https://doi.org/10.15587/978-617-8360-04-7.ch2
