To find approximate optimization solutions, many algorithms are used, seven of which are considered in this work: fuzzy sets, artificial neural networks, genetic algorithm, ant algorithm, particle swarm algorithm, DNA computation, and a new approach based on artificial immune systems (IIS). All these methods belong to the direction of "natural computing", ie. model certain biological processes, the algorithms of which nature has created for millions of years. It should be noted that the efficiency of one or another algorithm depends on the characteristics of the initial data of the problem, so it is impossible to unambiguously determine which of the algorithms is the most effective
1. E.D.Bychkov, R.Z.Salakhutdinov, V.V.Lendikrey, “Application of the theory of fuzzy (Fuzzy) sets in mathematical models of communication systems”. Research and materials: Supplement to the journal "Omsk Scientific Bulletin", Omsk: OGMA, 2000, 188 p.
2. J.J.Hopfield, D.W.Tank, “Neural” computation of decisions in optimization problems, Biological Cybernetics, vol. 52, no. 3, pp. 141-152, 1985.
3. J.H.Holland, Adaptation in natural and artificial systems. An introductory analysis with application to biology, control, and artificial intelligence. London: Bradford book edition, 1994, 211 p.
4. L.A.Zadeh, “Fuzzy sets: Information and control”, vol. 8, pp. 338-353, 1965.
5. R.A.Aliev, R.R.Aliev, The theory of intelligent systems. Baku: Publishing House "Chashyolgy", 2001. 720 p.
6. A.E.Altunin, M.V.Semukhin, Models and algorithms for decision making in fuzzy environment. Tyumen: Publishing House of the Tyumen State University, 2000, 352 р.
7. D.T.Muhamediyeva and N.A.Egamberdiyev, “Algorithm and the Program of Construction of the Fuzzy Logical Model”, 2019 International Conference on Information Science and Communications Technologies (ICISCT), Tashkent, 2019, pp. 1-4.
8. N.Egamberdiev, D.Mukhamedieva and U.Khasanov, “Presentation of preferences in multi-criterional tasks of decision-making”, IOP Conf. Series:Journal of Physics: Conference Series 1441, 2020.
9. T.F.Bekmuratov, D.T.Mukhamedieva, “A training algorithm of fuzzy inference system”, International scientific and technical journal “Chemical technology. Control and management, no. 3-4” and “Journal of Korea multmedia society” South Korea, Seoul – Uzbekistan,2015, 108-114 p.
10. D.K.Muhamediyeva, “Study parabolic type diffusion equations with double nonlinearity”, IOP Conf. Series:Journal of Physics: Conference Series, no. 1441, 2020, 012151. DOI: https://doi.org/10.1088/1742-6596/1441/1/012151.
11. D.T.Mukhamediyeva, N.A.Niyozmatova, “Application of Immune Algorithm for Constructing a Model of Fuzzy Inference”, American Journal of Mathematical and Computational Sciences 2016, no. 1(2), pp. 74-78. http://www.aascit.org/journal/ajmcs.
12. A.R.Marakhimov, I.H.Siddikov, A.Nasridinov, J.-Y.Byun, “A structural synthesis of information computer networks of automated control systems based on genetic algorithms”, Lecture Notes in Electrical Engineering, 2015.
"APPROACHES TO SOLVING PROBLEMS OF OPTIMIZATION OF SOLVING MONITORING PROBLEMS BASED ON NATURAL COMPUTING ALGORITHMS,"
Chemical Technology, Control and Management: Vol. 2020
, Article 23.
Available at: https://uzjournals.edu.uz/ijctcm/vol2020/iss5/23