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Convergence of a simulation method for solution of combinatorial optimization problems

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References

  1. V. M. Glushkov, “On system optimization,”Kibernetika, No. 5, 89–90 (1980).

    Google Scholar 

  2. C. H. Papadimitriou and K. Steiglitz,Combinatorial Optimization: Algorithms and Complexity, Prentice Hall, Englewood Cliffs, NJ (1982).

    Google Scholar 

  3. I. V. Sergienko,Mathematical Models and Solution Methods for Discrete Optimization Problems [in Russian], Naukova Dumka, Kiev (1985).

    Google Scholar 

  4. I. V. Sergienko, V. M. Grinchuk, L. F. Gulyanitskii, and L. B. Koshlai, “Algorithms to construct a mathematical model of preferences using expert judgments,”Kibernetika, No. 2, 16–22 (1991).

    Google Scholar 

  5. S. Kirkpatick, C. D. Gelatt, and M. P. Vecchi, “Optimization by simulated annealing,”Science,220, No. 4598, 671–680 (1983).

    Google Scholar 

  6. P. I. M. van Laarhoven and E. H. L. Aarts,Simulated Annealing: Theory and Practice, Kluwer, Dordrecht (1987).

    Google Scholar 

  7. T. Elperin, “Monte Carlo structural optimization in discrete variables with annealing algorithm,”Int. J. Num. Meth. Eng.,26, 815–821 (1988).

    Google Scholar 

  8. F. A. Ogbu and D. K. Smith, “The application of the simulated annealing algorithm to the solution of the n/m/Cmax flowshop problem,”Comput. Oper. Res.,17, No. 3, 243–253 (1990).

    Google Scholar 

  9. D. S. Johnson, C. R. Aragon, L. A. McGeoch, and C. Schevon, “Optimization by simulated annealing: an experimental evaluation. I. Graph partitioning,”Oper. Res.,37, No. 6, 865–892 (1989).

    Google Scholar 

  10. E. H. L. Aarts, P. J. M. van Laarhoven, “Statistical cooling: a general approach to combinatorial optimization problems,”Philips J. Res.,40, 193–226 (1985).

    Google Scholar 

  11. M. Lundy and A. Mess, “Convergence of an annealing algorithm,”Math. Progr.,34, 111–124 (1986).

    Google Scholar 

  12. D. Mitra, F. Romeo, A. Sanglovanni-Vincentelli, “Convergence and finite-time behavior of simulated annealing,”Adv. Appl. Prob.,18, 747–771 (1986).

    Google Scholar 

  13. S. Geman and D. Geman, “Stochastic relaxation, Gibbs distribution, Bayesian restoration of image,”IEEE Trans., PAMI,6, 721–741 (1984).

    Google Scholar 

  14. B. Hajek, “Cooling schedules for optimal annealing,”Math. Oper. Res.,13, 311–329 (1988).

    Google Scholar 

  15. J. N. Tsitsiklis, “Markov chains with rare transitions and simulated annealing,”Math. Oper. Res.,14, No. 1, 70–90 (1989).

    Google Scholar 

  16. L. F. Gulyanitskii,Solution of Quadratic Assignment Problems by Probabilistic Simulation Algorithms [in Russian], Preprint 91-45, IK AN UkrSSR, Kiev (1991).

    Google Scholar 

  17. L. B. Koshlai,Development of Algorithms and Software for the Solution of One Class of Weakly Structured Optimization Problems [in Russian], Thesis, IK AN UkrSSR, Kiev (1992).

    Google Scholar 

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The research is financed by State Foundation for Basic Research of the State Committee of Science and Technology of Ukraine.

Translated from Kibernetika i Sistemnyi Analiz, No. 3, pp. 163–167, May–June, 1993.

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Gulyanitskii, L.F., Koshlai, L.B. & Sergienko, I.V. Convergence of a simulation method for solution of combinatorial optimization problems. Cybern Syst Anal 29, 445–449 (1993). https://doi.org/10.1007/BF01125551

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