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ASPECTS OF APPLYING THE METHOD OF COORDINATE DESCENT FOR THE SHEPHERD DOG BIO-INSPIRED ALGORITHM
Abstract
Further development of digital economy requires researching the limits of some algorithms applicability. These algorithms are based on the mechanisms obtained as a result of investigating the processes of ?alive? nature. Such algorithms were called the bio-inspired algorithms and are widely used in practice, for example, for solving the problems of multi-agent systems coordinated control. Application of coordinate descent, a type of gradient method ? extends the description of the existing shepherd dog algorithm. In the pasture, the actions of a sheep dog are a characteristic example when one agent forces many to move in the prescribed direction. Such approach is widely applicable in practice for crowd control, solving the problem of cleaning the environment and other engineering tasks. In spite of the fact that the heuristic algorithm of a shepherd dog is already described in scientific works, there is still a problem of defining the algorithm for the other agents? movement. From the authors? point of view, the selfish herd approach is formalized by the Gauss?Seidel method of coordinate descent, named similarly to the Gauss?Seidel iterative technique for solving a square system of linear equations.
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Strömbom D., Mann R. P., Wilson. A. M. Solving the shepherding problem: heuristics for herding autonomous interacting agents, Journal of the royal society interface, vol. 11/issue 100, pp 1-9, 2014.
Lee W., Kim D. Autonomous shepherding behaviors of multiple target steering robots, Sensors (Switzerland), vol. 17/issue 12, art. no. 2729, 2017.
Fujioka K., Hayashi S. Effective shepherding behaviours using multi-agent systems, IEEE Region 10 Annual International Conference, Proceedings/TENCON, Singapore, pp 3179-3182, 2017.
Yu L., Wang S., Lai K.K., Zhou L. Bio-inspired credit risk analysis: Computational intelligence with support vector machines, Springer-Verlag Berlin Heidelberg, 2008, p 244.
Odili J.B., Kahar M.N.M., Noraziah A. Swarm intelligence optimization algorithms: A review, Journal of Telecommunication, Electronic and Computer Engineering, vol. 10/issue 1-4, pp 139-142, 2018.
Chiroma H., Herawan T., Fister I., Jr., Fister I., Abdulkareem S., Shuib L., Hamza M.F., Saadi Y., Abubakar A. Bio-inspired computation: Recent development on the modifications of the cuckoo search algorithm, Applied Soft Computing Journal, vol. 61, pp 149-173, 2017.
Elkhechafi M., Benmamoun Z., Hachimi H., Amine A., Elkettani Y. Firefly Algorithm for Supply Chain Optimization, Lobachevskii Journal of Mathematics, vol. 39/issue 3, pp 355-367, 2018.
Fidanova S., Roeva O. Influence of ant colony optimization parameters on the algorithm performance, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10665 LNCS, pp 358-365, 2018.
Davidović T., Teodorović D., Šelmić M. Bee Colony Optimization part I: The algorithm overview, Yugoslav Journal of Operations Research, vol. 25/issue 1, pp 33- 56, 2015.
Hajihassani M., Jahed Armaghani D., Kalatehjari R. Applications of Particle Swarm Optimization in Geotechnical Engineering: A Comprehensive Review, Geotechnical and Geological Engineering, vol. 36/issue 2, pp 705-722, 2018.
King A.J., Wilson A.M., Wilshin S.D., Lowe J., Haddadi H., Hailes S., Morton A.J. Selfish-herd behaviour of sheep under threat, Current Biology, vol. 22/issue 14, pp R561-R562, 2012.
Caruso F., Ceparano M.C., Morgan J. Uniqueness of Nash equilibrium in continuous two-player weighted potential games, Journal of Mathematical Analysis and Applications, vol. 459/issue 2, pp 1208-1221, 2018.
Evdokimov I.V., Kulakov E.D. The application of the steepest descent method in a single bioinspired algorithm, Devices and Systems. Management, control, diagnostics, vol. 8, pp 10-13, 2017.
Go C.K., Lao B., Yoshimoto J., Ikeda K. A reinforcement learning approach to the shepherding task using SARSA, Proceedings of the International Joint Conference on Neural Networks, Canada, pp 3833-3836, 2016.
Altshuller G. 40 Principles: TRIZ Keys to Technical Innovation (Triztools), US, 1998, p 141.
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