HYBRID METHOD FOR SOLVING THE PROBLEM OF PLACEMENT OF DIGITAL COMPUTER DEVICES
Abstract
The problem of placing elements of digital computing technology is considered in the article.
The analysis of the current state of research on this topic is carried out, the relevance of the
problem under consideration is noted. The importance of developing new effective methods for
solving such problems are highlighted. The place of the placement problem in the general cycle ofthe design stage is shown. The importance of a high-quality solution to the placement problem
from the point of view of the successful implementation of subsequent design stages is noted. The
importance of minimizing connection delays in the design process of large-scale devices is noted.
A review and analysis of various models and criteria for evaluating the solution to the placement
problem is carried out. It was emphasized that the most important criterion is the length of the
joints, it has a significant impact on the technologies used in the design. A complex mathematical
formulation of the problem of placing elements of digital computing equipment has been completed.
Perspective approaches to solving design problems are analyzed, hybrid methods and models
for solving complex multicriteria optimization and design problems are described. The principles
of operation and the model of a fuzzy logic controller are described. The description of the used
fuzzy control scheme is given. The functions of various blocks of a fuzzy logic controller are determined.
The structure of a multilayer neural network that implements the Gaussian function is
proposed. The interaction of blocks of a fuzzy genetic algorithm is described. A model of a hybrid
algorithm for solving the placement problem is proposed. The control parameters of the fuzzy
logic controller are determined. The proposed hybrid algorithm is implemented as an application
program. A series of computational experiments to determine the effectiveness of the developed
algorithm and select the optimal values of the control parameters were carried out.
References
design automation. CRC Press, New York, USA, 2009.
2. Shervani N. Algorithms for VLSI physical design automation. USA, Kluwer Academy Publisher,
1995, 538 p.
3. Cohoon J.P., Karro J., Lienig J. Evolutionary Algorithms for the Physical Design of VLSI
Circuits. Advances in Evolutionary Computing: Theory and Applications, Ghosh, A., Tsutsui,
S. (eds.). Springer Verlag, London, 2003, pp. 683-712.
4. Gladkov L.A., Kureychik V.V., Kureychik V.M. Diskretnaya matematika [Discrete Mathematics].
Moscow: Fizmatlit, 2014.
5. Prangishvili I.V. Sistemnyy podkhod i obshchesistmenye zakonomernosti [A systematic approach
and system-wide patterns]. Moscow: SINTEG, 2000.
6. Yarushkina N.G. Osnovy teorii nechetkikh i gibridnykh system [Fundamentals of the theory of
fuzzy and hybrid systems]. Moscow: Finansy i statistika, 2004.
7. Batyrshin I.Z., Nedosekin A.O. i dr. Nechetkie gibridnye sistemy. Teoriya i praktika [Fuzzy
hybrid systems. Theory and practice], ed. by N.G. Yarushkinoy. Moscow: Fizmatlit, 2007.
8. Haken H. Synergetics, an Introduction: Nonequilibrium Phase Transitions and Self-
Organization in Physics, Chemistry, and Biology. New York: Springer-Verlag, 1983.
9. Gladkov L.A., Kureychik V.M., Kureychik V.V., Sorokoletov P.V. Bioinspirirovannye metody v
optimizatsii [Bioinspired methods in optimization]. Moscow: Fizmatlit, 2009.
10. Gladkov L.A., Kureychik V.V., Kureychik V.M. Geneticheskie algoritmy [Genetic algorithms].
M.: Fizmatlit, 2010.
11. Karpenko A.P. Sovremennye algoritmy poiskovoy optimizatsii. Algoritmy, vdokhnovlennye
prirodoy [Modern search engine optimization algorithms. Algorithms inspired by nature].
Moscow: Izd-vo MGTU im. Baumana, 2016.
12. Gladkov L.A., Gladkova N.V., Leiba S.N., Strakhov N.E. Development and research of the
hybrid approach to the solution of optimization design problems, Proceedings of the Third International
Scientific Conference “Intelligent Information Technologies for Industry”
(IITI’18). IITI'18 2018. Advances in Intelligent Systems and Computing. Vol 875. Springer,
Cham, pp. 246-257.
13. Borisov V.V., Kruglov V.V., Fedulov A.S. Nechetkie modeli i seti [Fuzzy models and networks].
Moscow: Goryachaya liniya – Telekom, 2007.
14. Herrera F., Lozano M. Fuzzy Adaptive Genetic Algorithms: design, taxonomy, and future
directions, Soft Computing. 7(2003). Springer-Verlag, 2003, pp. 545-562.
15. Michael A., Takagi H. Dynamic control of genetic algorithms using fuzzy logic techniques,
Proc. of the 5th International Conference on Genetic Algorithms. Morgan Kaufmann, 1993,
pp. 76-83.
16. Lee M.A., Takagi H. Integrating design stages of fuzzy systems using genetic algorithms, Proceedings
of the 2nd IEEE International Conference on Fuzzy System, 1993, pp. 612-617.
17. King R.T.F.A., Radha B., Rughooputh H.C.S. A fuzzy logic controlled genetic algorithm for
optimal electrical distribution network reconfiguration, Proceedings of 2004 IEEE International
Conference on Networking, Sensing and Control, Taipei, Taiwan, 2004, pp. 577-582.
18. Herrera F., Lozano M. Adaptation of genetic algorithm parameters based on fuzzy logic
controllers. In: F. Herrera, J.L. Verdegay (eds.) Genetic Algorithms and Soft Computing,
Physica-Verlag, Heidelberg, 1996, pp. 95-124.
19. Rutkovskaya D., Pilin'skiy M., Rutkvoskiy L. Neyronnye seti, geneticheskie algoritmy i
nechetkie sistemy [Neural networks, genetic algorithms and fuzzy systems]. Moscow:
Goryachaya liniya – Telekom, 2006.
20. Gladkov L.A., Gladkova N.V., Gusev N.Y., Semushina N.S. Integrated approach to the solution
of computer-aided design problems, Proceedings of the 4th International Scientific Conference
“Intelligent Information Technologies for Industry” (IITI’19). Advances in Intelligent Systems
and Computing. Vol. 875. Springer, Cham, 2020, pp. 246-257.
