The method of synthesizing scalar functions of functions for vibor for bagatocriterial tasks, priinyattya rishen, in the case of unaccounted

  • Артем Вячеславович Безлюбченко Национальный аэрокосмический университет им. Н. Е. Жуковского «Харьковский авиационный институт»
  • Евгений Сергеевич Меняйлов Национальный аэрокосмический университет им. Н. Е. Жуковского «Харьковский авиационный институт» https://orcid.org/0000-0002-9440-8378
  • Михаил Леонидович Угрюмов Харковский национальный университет имени В. Н. Каразина https://orcid.org/0000-0003-0902-2735
  • Катерина Михайловна Угрюмова Национальный аэрокосмический университет им. Н. Е. Жуковского «Харьковский авиационный институт» https://orcid.org/0000-0003-0043-2121
  • Сергей Викторович Черныш Национальный аэрокосмический университет им. Н. Е. Жуковского «Харьковский авиационный институт» https://orcid.org/0000-0002-1750-5158
Keywords: stochastic programming; computational mathematics; numerical analysis and programming (computer mathematics); memetic algorithm

Abstract

The problem of the definition of decision selection criteria (objective functions) and sought-for quantities estimation are considered in the multi-objective problems under a priori uncertain data. The types of decision selection criteria scalar convolution are obtained for the multi-objective problems of the development of robust meta-models, mathematical models identification, optimization and decision making. A model and a method for the synthesis of solutions of multicriteria problems of stochastic optimization with mixed conditions (MV-problems) are considered. A computational method for the synthesis of solutions of problems of this class is developed, based on a memetic algorithm. Examples of the implementation of the proposed method for solving test problems in deterministic and stochastic formulations are presented. Application of the proposed developments provides an effective robust estimation of the sought values for the parametric uncertainty of the input data and a reduction in the information complexity of the method for synthesizing quasisolutions.

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References

D. B. Yudin, Computational methods of decision theory: monograph. Moscow : Phys.-Mat. Lit., 1989. 320 p.

I. N. Egorov, Optimization of Gas Turbine Engine Elements by Probability Criteria. ASME 1993 International Gas Turbine and Aeroengine Congress and Exposition, Cincinnati, Ohio, USA, May 24–27, 1993, 8 p.

A. E. Gelfand, Model choice: A minimum posterior predictive loss approach. Biometrika. 1998, Vol. 85, Issue 1, 1 March, 11p.

A. A. Giunta, Perspectives on optimization under uncertainty: algorithms and applications.10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Albany, New York, 30 August -1 September, 2004, 10 р.

T. Gneiting, Strictly Proper Scoring Rules, Prediction, and Estimation. American Statistical Association Journal of the American Statistical Association,Vol. 102, No. 477, March 2007, pp. 359 – 378.

Yu. M. Volin and G. M. Ostrovsky, Multi-criteria optimization of technological processes in conditions of uncertainty, Avtomat. and Telemekh. 2007. No. 3, pp. 523–538

E. V. Lysenko, V. P. Ponomarenko and V. P Pisklakova, A systemological analysis of the problem of decision making under conditions of multicriteriality and uncertainty. ACS and automation devices. 2008. №145. pp.104-109.

V. I. Levin., Modeling of optimization problems in the conditions of interval uncertainty. Izvestiya Penza State Pedagogical University named after V.G. Belinsky. Physics and Mathematics. 2011. No. 26. pp.589-595.

T. Erfani and S. V. Utyuzhnikov, Control of robust design in multiobjective optimization under uncertainties. Structural and Multidisciplinary Optimization, February 2012, Vol. 45, Is. 2, pp. 247–256.

G. S. Veresnikov, L. A. Pankova and V. A. Pronina, Multi-criteria optimization in problems of preliminary aerodynamic projection under uncertainty. Journal IPU RAS. 2014. №2. p.161-163.

V.I. Levin, Optimization under uncertainty by the method of determinism. Journal. Manage technical systems. 2015. №4. pp. 104 - 112.

L. Brevault, M. Balesdent, Berend N. and R. Le Riche, Multi-level hierarchical MDO formulation with functional coupling satisfaction under uncertainty, application to sounding rocket design. 11th World Congress on Structural and Multidisciplinary Optimisation, Sydney Australia, 7 -12, June 2015, 6 p.

S. Lee, D. Rhee and K. Yee, Optimal Arrangement of the Film Cooling Holes Considering the Manufacturing Tolerance for High Pressure Turbine Nozzle. ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition, Seoul, South Korea, June 13–17, 2016, 10 p.

A. A. Tronchuk and E. M. Ugryumova, Mathematical models and an evolutionary method for solving stochastic optimization problems. Bulletin of Kharkiv National University. Zbіrnik naukovih Prats. Seriya: “Mathematical modeliwania. Informacion technology. Automation system management. 2012. Vipusk 19 (No. 1015). pp. 292-305.

Ievgen Meniailov, Olexandr Khustochka, Kateryna Ugryumova, Sergey Сhernysh, Sergiy Yepifanov and Mykhaylo Ugryumov, Mathematical Models and Methods of Effective Estimation in Multi-Objective Optimization Problems under Uncertainties. Advances in Structural and Multidisciplinary Optimization: Proceedings of the 12th World Congress of Structural and Multidisciplinary Optimization (WCSMO12) / By Axel Schumacher (05th - 09th, June 2017, Braunschweig, Germany). SpringerLink, 2018. 2115 p. (ISBN: 978-331-967-987-7) (Paper No. 0011, P.411-427)

O. N. Granichin and B. T. Polyak. Randomized Optimization and Estimation Algorithms with Almost Random Interference: study guide. A. V. Nazin Ed. Moscow : Science, 2003. 291 p.

Yu. E. Egorova and A.V. Yazenin, A stochastic quasigradient method for solving problems of probabilistic-probabilistic optimization of one class. Bulletin of Tver State University. Series: Applied Mathematics. 2014. No. 4. pp. 57–70.

A. G. Isavnin and M. R. Khamidullin, The solution of a number of economic problems by the algorithms of the method of penalty functions with incomplete minimization of auxiliary functions. Economic analysis: theory and practice. 2012. №20. pp. 62–66.

G. M. Ulitin and S. N. Tsarenko, The averaging method in problems of longitudinal impact of rods of variable cross section. Bulletin of the South Ural State University. Series: Mathematics. Mechanics. Physics. 2016 № 1. pp. 43–48.

V. E. Cnityuk, Aspects of evolutionary modeling in optimization problems. Artificial Intelligence. 2005. No. 4. pp. 284-291.

V. M. Kureichik, Modified genetic operators. Bulletin of the South Ural State University. Series: Mathematics. Mechanics. Physics. 2009. №1. pp. 7–14.

D. A. Karaboga, Simple and Global Optimization Algorithm for Engineering Problems: Dierential Evolution Algorithm. Turk J Elec Engin, VOL.12, NO.1, 2004, pp. 53–60.

A. V. Panteleev and I. F. Dmitrakov, Application of the method of differential evolution for optimization of parameters of aerospace systems. Electronic journal "Proceedings of the MAI". 2010. Issue number 37. 10 p.

D. S. Chivilikhin, Evolutionary strategies with an adaptive parameter based on the properties of the landscape of the fitness function. Proceedings of the scientific conference on computer science. 2013. pp. 525–531.

M. K. Sakharov and A. P. Karpenko, Memetic algorithms for solving the global nonlinear optimization problem. Overview. Science and education: a scientific publication MSTU. N.E. Bauman. 2015. №12. pp. 119–142.

V. V. Baranyuk and O. S. Smirnova, Detailing the ontological model using swarm algorithms based on the behavior of insects and animals. International Journal of Open Information Technologies scholar. 2015. № 12. Volume 3. pp. 18–27.

I. A. Khodashinsky, I. V. Gorbunov and P. A. Dudin, Algorithms of the ant and bee colony for training fuzzy systems. Reports of Tomsk State University of Control Systems and Radioelectronics. 2009. № 2 (20). pp. 157–161.

B. K. Lebedev and V. B Lebedev, Placement on the basis of the bee colony method. Proceedings of the Southern Federal University. Technical science. 2010. No. 12 (20). pp. 12–20.

Yu. O. Chernyshev, G. V. Grigoriev and N. N. Ventsov, Artificial immune systems: an overview and current state. Software products and systems. 2014. №4 (108). pp. 136–142.

A. V Panteleev. and D. V. Metlitskaya, Application of the method of artificial immune systems in the search for conditional extremum of functions. Scientific Bulletin of the Moscow State Technical University of Civil Aviation. 2012. № 184. Pp. 54–61.

A. P. Karpenko. Modern algorithms of search optimization. Algorithms inspired by nature: study guide. Moscow: Moscow. State Technical University Publishing House N.E. Bauman, 2014. 446s.

A. N. Tikhonov and V. Ya Arsenin. Methods for solving incorrect tasks: a tutorial. Moscow : Science, phys.-mat. lit., 1986. 288 p.

Published
2018-10-29
How to Cite
Безлюбченко, А. В., Меняйлов, Е. С., Угрюмов, М. Л., Угрюмова, К. М., & Черныш, С. В. (2018). The method of synthesizing scalar functions of functions for vibor for bagatocriterial tasks, priinyattya rishen, in the case of unaccounted. Bulletin of V.N. Karazin Kharkiv National University, Series «Mathematical Modeling. Information Technology. Automated Control Systems», 39(3), 14-25. Retrieved from https://periodicals.karazin.ua/mia/article/view/11643
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