HYBRID APPROACH THE JOINT SOLUTION OF PLACEMENT AND TRACING PROBLEMS
Abstract
The article proposes an integrated approach to solving the problems of placing and tracing elements of circuits of electronic computing equipment. The approach is based on the joint solution of placement and tracing problems using fuzzy genetic methods. A description of the problem under consideration is given and a brief analysis of existing approaches to its solution is performed. The article discusses integrated approaches to solving optimization problems of computer-aided design of digital electronic computing equipment circuits. The urgency and importance of developing new effective methods for solving such problems is emphasized. It is noted that an important direction in the development of optimization methods is the development of hybrid methods and approaches that combine the advantages of various methods of computational intelligence. The article describes the following main points: the structure of the proposed algorithm and its main stages; modified genetic crossover operators; models for the formation of the current population are proposed; modified heuristics, operators and strategies for finding optimal solutions. The results of computational experiments are presented. The experiments carried out confirm the effectiveness of the proposed approach. In conclusion, a brief analysis of the results obtained is given.
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.M., Kureychik V.V., Sorokoletov P.V. Bioinspirirovannye metody v
optimizatsii [Bioinspired methods in optimization]. Moscow: Fizmatlit, 2009.
5. Gladkov L.A., Kureychik V.V., Kureychik V.M. Geneticheskie algoritmy [Genetic algorithms].
Moscow: Fizmatlit, 2010.
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. Luger G.F. Artificial Intelligence. Structures and Strategies for Complex Problem Solving. 6th
ed. Addison Wesley, Boston MA, 2009.
9. Russel S.J., Norvig P. Artificial Intelligence. A modern Approach. Prentice Hall, 2003.
10. 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.
11. 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
12. Herrera F., Lozano M. Fuzzy Adaptive Genetic Algorithms: design, taxonomy, and future
directions, Soft Computing. Springer-Verlag, 2003, No. 7, pp. 545-562.
13. 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
14. Praveen T., Arun Raj Kumar P. Multi-Objective Memetic Algorithm for FPGA Placement
Using Parallel Genetic Annealing, International Journal of Intelligent Systems and Applications
(IJISA), 2016, Vol. 8, No. 4, pp. 60-66,
15. Gladkov L.A., Gladkova N.V., Gromov S.A. Hybrid Fuzzy Algorithm for Solving Operational
Production Planning Problems, Advances in Intelligent Systems and Computing. Springer International
Publishing, Switzerland, 2017, Vol. 573, pp. 444-456.
16. 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, Advances in Intelligent Systems
and Computing. Springer, Cham, 2019, Vol. 875, pp. 246-257.
17. Deb K., Joshi D., Anand A. Real-coded evolutionary algorithms with parent-centric recombination,
Proc. Evol. Comput., 2002, Vol. 1, pp. 61-66.
18. Herrera F., Lozano M., Sánchez A.M.:A taxonomy for the crossover operator for real-coded
genetic algorithms: an experimental study, Int. J. Intell. Syst., 2003, Vol. 18 (3), pp. 309-338.
19. Lozano M., Herrera F., Krasnogor N., Molina D.: Real-coded memetic algorithms with crossover
hill-climbing, Evol. Comput., 2004, No. 12 (3), pp. 273-302.
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.
21. Gladkov L.A., Gladkova N.V., Leiba S.N. Manufacturing Scheduling Problem Based on Fuzzy
Genetic Algorithm, Proc. of IEEE East-West Design & Test Symposium (EWDTS’2014). Kiev,
Ukraine, September 26–29, 2014, pp. 209-213.
22. Gladkov L.A., Gladkova N.V., Legebokov A.A. Organization of Knowledge Management Based
on Hybrid Intelligent Methods, Software Engineering in Intelligent Systems. Proceedings of
the 4th Computer Science On-line Conference 2015 (CSOC 2015), Vol 3: Software Engineering
in Intelligent Systems. Springer International Publishing, Switzerland 2015, pp. 107-113.
23. 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.