A.F. Ulasen’1, I.L. Zhbanov2, A.V. Klyuev3, E.G. Andrianova4, S.G. Daeva5, N.S. Nikolaeva6

1–3 Army Air Defense Military Academy (Smolensk, Russia)
 4,5 MIREA – Russian Technological University (Moscow, Russia)
 6 Bauman Moscow State Technical University (Moscow, Russia)
 

The problem considered in the article is the lack of sufficient justification for the choice of stochastic optimization methods for solving extreme problems. The purpose of the work is to increase the efficiency of decision support systems for the mutual placement of objects. The paper examines the patterns reflecting the effectiveness of automated decision-making in the implementation of global optimization processes, based on a genetic algorithm and uncontrolled random search, in conditions of a priori unknown information about the studied functionality. The result of the work is a methodology for evaluating the effectiveness of stochastic algorithms for searching for a global extremum and recommendations for choosing an optimization method depending on the specifics of modeling conditions and the shape of the studied functionals.

Ulasen’ A.F., Zhbanov I.L., Klyuev A.V., Andrianova E.G., Daeva S.G., Nikolaeva N.S. Methodology for evaluating the effectiveness of stochastic search algorithms global extremum. Science Intensive Technologies. 2022. V. 23. № 4. P. 46−55. DOI: https://doi.org/10.18127/j19998465-202204-06 (in Russian)

1. Bakalo M.A., Kurejchik V.V. Modificirovannyj algoritm razmeshcheniya metodom parnyh perestanovok. Izv. TRTU. Tematicheskij vypusk «Intellektual'nye SAPR». 2007. S. 77–84 (in Russian).
2. Sobol' I.M., Statnikov R.B. Vybor optimal'nyh parametrov v zadachah so mnogimi kriteriyami. M.: Drofa, 2006 (in Russian).
3. Karpenko A.P. Sovremennye algoritmy poiskovoj optimizacii. Algoritmy vdohnovlennye prirodoj. M.: Izd-vo MGTU im. N.E. Baumana. 2014. 446 s. (in Russian)
4. Dorigo M., Socha K. Ant colony optimization for continuous domains. European Journal of Operational Research. 2008. V. 185.
   P. 1155–1173 (in Russian).
5. Batishchev D.I. Geneticheskie algoritmy resheniya ekstremal'nyh zadach. Voronezh. 1995. 65 s. (in Russian).
6. Kurejchik V.V., Sorokoletov P.V. Konceptual'naya model' predstavleniya reshenij v geneticheskih algoritmah. Izv. YUFU. Tekhnicheskie nauki. 2008. № 9 (86). S. 7–12 (in Russian).
7. Gol'dshtejn A.L. Mnogokriterial'nyj geneticheskij algoritm. Vestnik PNIPU. Elektrotekhnika, informacionnye tekhnologii, sistemy upravleniya. 2013. № 8. S. 14–22 (in Russian).
8. Panteleev A.V., Dmitrakov I.F. Analiz sravnitel'noj effektivnosti metoda imitacii otzhiga dlya poiska global'nogo ekstremuma funkcij mnogih peremennyh. Nauchnyj Vestnik MGTU GA. 2009. Vyp. 145(8). C. 26–31 (in Russian).
9. Saharov M.K., Karpenko A.P., Ivan'kov I.F. Issledovanie effektivnosti algoritma evolyucii razuma v zadache global'noj optimizacii. Internet-zhurnal «NAKOVEDENIE» 2016. T. 8. № 5 (in Russian).
10. Buslenko M.P., Golenko D.I., Sobol' I.M. Sragovich V.G. Metod statisticheskih ispytanij. Pod redakciej YU.A. SHrejdera.. Gosudarstvennoe izdatel'stvo fiziko-matematicheskoj literatury. – Moskva. 1962. S. 332 (in Russian).