Застосування нейронних мереж в задачах маршрутизації БПЛА: Fìz.-mat. model. ìnf. tehnol. 2021, 33:73-77

• Дата: 2021
• Автор: Ogurtsov, Maksym
• Формат: Стаття
• Мова: Українська
• Опубліковано: Інститут прикладних проблем механіки і математики ім. Я. С. Підстригача НАН України 2021
• Теми: нейронні мережі; комбінаторна оптимізація; маршрутизація; безпілотні авіаційні комплекси; глибоке навчання
• Онлайн доступ: https://www.fmmit.lviv.ua/index.php/fmmit/article/view/205
• Назва журналу: Physico-mathematical modeling and informational technologies

The paper presents an overview of approaches to the neural networks’ usage in combinatorial optimization problems and other problems that arise when using unmanned aircraft vehicles. It has been determined that the neural networks usage (including the deep learning networks) is possible in almost all types of combinatorial optimization problems, in particular, in routing problems (traveling salesman problem, vehicle routing problem in various versions, etc.) and other similar combinatorial optimization problems that arise when using unmanned aerial systems. Recurrent neural networks with nonparametric normalized exponential functions of supervised learning may be used successfully to solve combinatorial optimization problems. References Naseem, A., Shah, S. T. H., Khan, S. A., Malik, A. W. (2017). Decision support system for optimum decision making process in threat evaluation and weapon assignment: Current status, challenges and future directions. Annual reviews in control, 43, 169-187. DOI https://doi.org/10.1016/j.arcontrol.2017.03.003 Hulianytskyi, L. F., Riasna, I. I., Butenko S., Pardalos P. M., Shylo V. (2017). Formalization and classification of combinatorial optimization problems In: Optimization Methods and Applications. Cham: Springer International Publishing, 239–250. DOI https://doi.org/10.1007/978-3-319-68640-0_11 Bengio, Y., Lodi, A., Prouvost, A. (2020). Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon. – European Journal of Operational Research, 290(2), 405-421. DOI https://doi.org/10.1016/j.ejor.2020.07.063 Bello, I. (2016). Neural combinatorial optimization with reinforcement learning. – arXiv preprint arXiv:1611.09940. Braekers, K., Ramaekers, K., Nieuwenh, I. V. (2016). The Vehicle Routing Problem: State of the Art Classification and Review. Computers & Industrial Engineering, 99, 300–313. DOI https://doi.org/10.1016/j.cie.2015.12.007 Horbulin, V. P., Hulianytskyi, L. F. Sergienko, I. V. (2019). Formulations and mathematical models of the optimizing routes problems for aircraft with dynamic depots. Control systems and computers, 1(3), 3-10. DOI https://doi.org/10.15407/usim.2019.01.003 Horbulin, V. P., Hulianytskyi, L. F. Sergienko, I. V. (2020). Optimization of UAV Team Routes in the Presence of Alternative and Dynamic Depots. Cybern Syst Anal., 56(2), 195–203. DOI https://doi.org/10.1007/s10559-020-00235-8 Li, Z., Chen, Q., Koltun, V. (2018). Combinatorial optimization with graph convolutional networks and guided tree search. – Advances in Neural Information Processing Systems, 539-548. Bessonov, A. A. (2012). Multicriteria neuroevolutionary optimization of nonlinear functions. Information processing systems, 9(107), 5–10. Hulianytskyi, L. F. Kotkova, А. А. (2020). To the classi cation of vehicles routing problems. Scientific Bulletin of Uzhhorod University. Mathematics and Informatics Series, 1(36), 73-84. Hopfield, J. J., Tank, D. W. (1985). ”Neural” computation of decisions in optimization problems. Biological Cybernetics, 52(3), 141–152. Wilson, G. V., Pawley, G. S. (1988). On the stability of the travelling salesman problem algorithm of hopfield and tank. Biological Cybernetics, 58(1), 63–70. DOI https://doi.org/10.1007/bf00363956 Goodfellow, I., Bengio, Y., Courville, A. (2016). Deep Learning. Cambridge : MIT press.