Algorithm of solving corroding construction optimization problems based on flexible tolerance method

Authors

  • Дмитрий Гегемонович Зеленцов Ukrainian State University of Chemical Technology, Pr. Gagarina, 8, Dnipropetrovsk, Ukraine, 49005, Ukraine https://orcid.org/0000-0002-5785-9858
  • Ольга Ростиславовна Денисюк Ukrainian State University of Chemical Technology, Pr. Gagarina, 8, Dnipropetrovsk, Ukraine, 49005, Ukraine https://orcid.org/0000-0002-9818-5298

DOI:

https://doi.org/10.15587/2312-8372.2016.66650

Keywords:

corrosion, discrete optimization, flexible tolerance method, neural networks, genetic algorithm

Abstract

In current paper authors formulated a new problem of hinged-rod constructions optimal design, which considers physicochemical processes in construction elements that cause reduction of their bearing capacity and assumes that solution is obtained with a given accuracy. The search for an optimal solution is made on discrete non-metrical space of varied parameters.

Actuality of the problem of corroding constructions optimal design is determined by the requirements for their high reliability and minimal consumption of materials. Utilization of existing algorithms for optimal design of such constructions allows to achieve required solution accuracy only with high computational cost. The paper describes creation of effective algorithm based on flexible tolerance method which allows to obtain solution with given accuracy and, therefore, to ensure required level of reliability of designed construction. Optimization algorithm uses neural network module of computational error control, which allows to change parameters of numerical solution of differential equation system modeling the influence of aggressive environment while solving the optimization problem. It allows to reduce computational cost at initial stages of search for the solution and to ensure required solution accuracy in the vicinity of the extremum.

Analysis of results of numerical experiments allows to make a conclusion about high performance of optimization algorithm while ensuring given accuracy of problem solution. Utilization of developed algorithm will allow to solve the problems of  corroding hinged-rod constructions optimal design.

Author Biographies

Дмитрий Гегемонович Зеленцов, Ukrainian State University of Chemical Technology, Pr. Gagarina, 8, Dnipropetrovsk, Ukraine, 49005

Doctor of Technical Science, Professor, Head of Department

Department of Information Systems

Ольга Ростиславовна Денисюк, Ukrainian State University of Chemical Technology, Pr. Gagarina, 8, Dnipropetrovsk, Ukraine, 49005

PhD student

Department of Information Systems

References

  1. Haug, E. J., Arora, J. S. (1979). Applied optimal design: mechanical and structural systems. John Wiley & Sons, 506.
  2. Zelentsov, D. G., Filatov, G. V. (2002). Obzor issledovanii po primeneniiu metodov nelineinogo matematicheskogo programmirovaniia k optimal'nomu proektirovaniiu konstruktsii, vzaimodeistvuiushchih s agressivnoi sredoi. Voprosy himii i himicheskoi tehnologii, 4, 108–115.
  3. Zelentsov, D. G., Naumenko, N. Yu. (2005). Adaptatsiia metoda skol'ziashchego dopuska k zadacham optimizatsii korrodiruiushchih konstruktsii. Systemni tekhnolohii, 2 (37), 48–56.
  4. Zelentsov, D. G., Novikova, L. V., Naumenko, N. Yu. (2012). Algoritm upravleniia tochnost'iu chislennogo resheniia nekotoryh klassov sistem differentsial'nyh uravnenii. Systemni tekhnolohii, 5 (82), 71–79.
  5. Zelentsov, D. G., Korotkaya, L. I. (2011). Neural networks as the change facility of the method of the sliding admission. Eastern-European Journal Of Enterprise Technologies, 4(4(52)), 21–24. Available: http://journals.uran.ua/eejet/article/view/1384
  6. Coello, C. A. C. (1994). Discrete optimization of trusses using genetic algorithms. EXPERSYS-94. The Sixth International Conference on Artificial Intelligence and Expert Systems Applications, 331–336.
  7. Wu, S.-J., Chow, P.-T. (1995, September). Steady-state genetic algorithms for discrete optimization of trusses. Computers & Structures, Vol. 56, № 6, 979–991. doi:10.1016/0045-7949(94)00551-d
  8. Rajeev, S., Krishnamoorthy, C. S. (1992, May). Discrete Optimization of Structures Using Genetic Algorithms. Journal of Structural Engineering, Vol. 118, № 5, 1233–1250. doi:10.1061/(asce)0733-9445(1992)118:5(1233)
  9. Gutkowski, W. (1997). Discrete structural optimization. International Centre for Mechanical Sciences, Vol. 373. Springer, 250. doi:10.1007/978-3-7091-2754-4
  10. Himmelblau, D. M. (1972). Applied nonlinear programming. McGraw-Hill Companies, 498.
  11. Robert, C. (1999). The Essence of Neural Networks. Prentice Hall Europe, 232.
  12. Ashlock, D. (2006). Evolutionary Computation for Modeling and Optimization. New York: Springer, 572. doi:10.1007/0-387-31909-3

Published

2016-03-29

How to Cite

Зеленцов, Д. Г., & Денисюк, О. Р. (2016). Algorithm of solving corroding construction optimization problems based on flexible tolerance method. Technology Audit and Production Reserves, 2(2(28), 51–57. https://doi.org/10.15587/2312-8372.2016.66650

Issue

Section

Mathematical Modeling: Original Research