Skip to main content
Log in

Searching for optimal configurations by simulated tunneling

  • Published:
Zeitschrift für Physik B Condensed Matter

Abstract

Optimization theory deals with algorithms finding the lowest cost (energy) configuration in a minimal number of steps. When the cost function has many local minima, the deterministic algorithms become easily trapped in suboptimal solutions. The simulated annealing method tries to overcome this difficulty by introducting thermal noise in the problem. Here we explore the possibility of implementing search procedures analogous to the quantum tunneling effect. The suggested dynamics is a guided diffusion process of an interactingpopulation of configurations. Different dynamical aspects of the search process are formulated first in a simple one-dimensional tight-binding model with a hierarchical potential. The new algorithm is then applied to the Traveling Salesman Problem. It is demonstrated that the use of interacting, evolving populations of tours representing our “wave packet” leads to systematic improvements and possibly, to the optimal tour. In addition, the structure of the cost function landscape for a given instance becomes locally accessible. The performance of the algorithm and its implications for parallel computing and “genetic” programming are briefly discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Judd, S.: Learning in networks is hard. Proceedings of the IEEE First Conference on Neural Networks, San Diego, June '87, Vol. II. pp. 685–692 (1987)

    Google Scholar 

  2. Barahona, F., Maccioni, E.: J. Phys. A15, L611 (1982);

    Google Scholar 

  3. Barahona F.: J. Phys. A15, 3241 (1982);

    Google Scholar 

  4. Bachas, C.P.: J. Phys. A17, L 709 (1984)

    Google Scholar 

  5. Orland, H.: J. Phys. (Paris) Lett.46, L 763 (1985);

    Google Scholar 

  6. Mézard, M., Parisi, G.: J. Phys. (Paris) Lett.46, L 771 (1985);

    Google Scholar 

  7. Fu, Y., Anderson, P.W.: J. Phys. A19, 1605 (1986);

    Google Scholar 

  8. Mézard, M., Parisi, G.: J. Phys. (Paris)47, 1285 (1986)

    Google Scholar 

  9. Mézard, M., Parisi, G.: Europhys. Lett.2, 913 (1986)

    Google Scholar 

  10. Baskaran, G., Fu, Y., Anderson, P.W.: J. Stat. Phys.45, 1 (1986);

    Google Scholar 

  11. Liao W.: J. Phys. A21, 427 (1988);

    Google Scholar 

  12. Wong, K.Y.M., Sherrington, D., Mottishaw, P., Dewar, R., Dominicis, C. de: J. Phys. A21, L99 (1988)

    Google Scholar 

  13. Kirkpatrick, S., Gelatt, C.D. Jr., Vecchi, M.P.: Science220 671 (1983);

    Google Scholar 

  14. Černŷ, V.: Unpublished preprint 1982;

  15. Bonomi, E., Lutton, J.-L.: SIAM Rev.26, 551 (1984)

    Google Scholar 

  16. Stochastic optimization in engineering and biology. IBM — Europe Institute 1986

  17. Wille, L.T., Vennik, J.: J. Phys. A18, L419 (1985);

    Google Scholar 

  18. Carr, R., Parrinello, M.: Phys. Rev. Lett.55, 2471 (1985);

    Google Scholar 

  19. Hohl, D., Jones, R.O., Car, R., Parrinello, M.: Chem. Phys. Lett.139, 540 (1987)

    Google Scholar 

  20. Li, Z., Scheraga, H.A.: Proc. Natl. Acad. Sci.84, 6611 (1987);

    Google Scholar 

  21. Bryngelson, J.D., Wolynes, P.G.: Proc. Natl. Acad. Sci.84, 7524 (1987)

    Google Scholar 

  22. Brooks Harris, A., Aharony, A.: Europhys. Lett.4, 1355 (1987);

    Google Scholar 

  23. Li, Y.S., Duxbury, P.M.: Phys. Rev. B36, 5411 (1987)

    Google Scholar 

  24. Bremermann, H.J.: Numerical optimization procedures derived from biological evolution processes. In: Cybernetic problems in bionics. Oestreicher, H.L., Moore, D.R., (eds.), New York: Gordon & Breach 1968;

    Google Scholar 

  25. Genetic algorithms and simulated annealing. Davis, L., (ed.), Los Altos: Morgan Kaufmann Publ. 1987

    Google Scholar 

  26. Rechenberg, I.: Evolutionsstrategie: Otimierung technischer Systeme nach Prinzipien der biologischen Evolution. Stuttgart: Frommann-Holzboog 1973;

    Google Scholar 

  27. Schwefel, H.P.: Numerical optimization of computer models. New York: John Wiley 1981

    Google Scholar 

  28. Holland, J.H.: Escaping brittleness: the possibilities of general purpose learning algorithms applied to parallel rule-based systems. In: Machine learning II. Los Altos: Morgan Kaufmann 1986

    Google Scholar 

  29. Ebeling, W., Engel, A., Esser, B., Feistel, R.: J. Stat. Phys.37, 369 (1984);

    Google Scholar 

  30. Ebeling, W., Engel, A.: Syst. Anal. Model. Simul.3, 377 (1986)

    Google Scholar 

  31. Fisher, R.A.: The genetical theory of natural selection. Oxford: Clarendon Press 1930;

    Google Scholar 

  32. Eigen, M., Schuster, P.: The hypercycle. Berlin, Heidelberg, New York: Springer 1979

    Google Scholar 

  33. Boseniuk, T., Ebeling, W., Engel, A.: Phys. Lett. A125, 307 (1987)

    Google Scholar 

  34. Brady, R.M.: Nature317, 804 (1985)

    Google Scholar 

  35. Ablay, P.: Spektrum der Wissenschaft July 1987, 104

  36. Johnson, D.: Nature330, 525 (1987)

    Google Scholar 

  37. Ceperly, D.M., Kalos, M.H.: In: Monte Carlo methods in statistical physics. Binder, K., (ed.), Berlin, Heidelberg, New York: Springer 1979

    Google Scholar 

  38. Lin, S., Kernighan, B.W.: Oper. Res.21, 498 (1973)

    Google Scholar 

  39. Ackley, D.H.: In: Genetic algorithms and simulated annealing. Davis, L., (ed.), Los Altos: Morgan Kaufmann Publ. 1987

    Google Scholar 

  40. Huberman, B., Kerszberg, M.: J. Phys. A18, L 331 (1985)

    Google Scholar 

  41. Binder, K.: In: Monte Carlo methods in statistical physics. Binder, K., (ed.). Berlin, Heidelberg, New York: Springer 1979

    Google Scholar 

  42. Schneider, T., Würtz, D., Politi, A.: Phys. Rev. B36, 1789 (1987); Keirstead, W.P., Ceccatto, H.A., Huberman, B.A.: Vibrational properties of hierarhical systems. J. Stat. Phys. (to appear)

    Google Scholar 

  43. Ceccatto, H.A., Keirstead, W.P., Huberman, B.A.: Phys. Rev. A36, 5509 (1987)

    Google Scholar 

  44. Ceccatto, H.A., Keirstead, W.P.: J. Phys. A21, L 75 (1988)

    Google Scholar 

  45. Randelman, R.E., Grest, G.G.: J. Stat. Phys.45, 885 (1986);

    Google Scholar 

  46. Grest, G.G., Soukoulis, C.M., Levin, K.: Phys. Rev. Lett.56, 1148 (1986);

    Google Scholar 

  47. Rees, S., Ball, R.C.: J. Phys. A20, 1239 (1987)

    Google Scholar 

  48. Feynman, R.P.: Statistical mechanics. Reading: Benjamin/Cummings 1972

    Google Scholar 

  49. Hetherington, J.H.: Phys. Rev. B30, 2713 (1984)

    Google Scholar 

  50. Nieuwenhuizen, Th.M.: J. Phys. A21, L 567 (1988)

    Google Scholar 

  51. Patkós, A., Ruján, P.: Z. Phys. B — Condensed Matter33, 163 (1979)

    Google Scholar 

  52. Nattermann, T., Renz., W.: (to be published)

  53. Lifshitz, I.M., Gradeskul, S.A., Pastur, L.A.: Introduction to the theory of disordered systems (in Russian). Moscow: Nauka, 1982

    Google Scholar 

  54. Zhang, Y.C.: Phys. Rev. Let.56, 2113 (1986);

    Google Scholar 

  55. Engel, A., Ebeling, W.: Phys. Rev. Lett.59 1979 (1987)

    Google Scholar 

  56. Sinai, Ya.G.: In: Lecture Notes in Physics. Vol. 153, p. 12. Berlin, Heidelberg, New York: Springer 1982;

    Google Scholar 

  57. Derrida, B., Pomeau, Y.: Phys. Rev. Lett.48, 627 (1982)

    Google Scholar 

  58. Zeldovich, Ya.B., Molchanov, S.A., Rusmajkin, A.A., Sokolov, D.D.: Zh. Eksp. Teor. Fiz.89, 2061 (1985) (English translation: Sov. Phys. JETP62, 1188 (1985))

    Google Scholar 

  59. Kardar, M., Zhang, Y.C.: Phys. Rev. Lett.58, 2087 (1987)

    Google Scholar 

  60. Khachian, L.G.: Dokl. Akad. Nauk SSSR244 1093–1096 (1979) (English translation: Sov. Math. Dokl.20, 191 (1979);

    Google Scholar 

  61. Karmarkar, N.: Combinatorica4, 373 (1984)

    Google Scholar 

  62. Papadimitriou, C.H., Steiglitz, K.: Combinatorial optimization: algorithms and complexity. Englewood Cliffs: Prentice Hall 1982

    Google Scholar 

  63. The traveling salesman problem. Lawler, E.L., Lenstra, J.K., Ringnooy Kan, A.H.G., Shmoys, D.B., (eds.), New York: John Wiley 1984

    Google Scholar 

  64. Bellman, R.E.: J. Assoc. Comput. Math.9, 61 (1962);

    Google Scholar 

  65. Bellman, R.E., Dreyfus, S.E.: Applied dynamic programming. Princeton: Princeton University Press 1962

    Google Scholar 

  66. Garey, M.R., Johnson, D.S.: Computers and intractability: a guide to the theory ofN℘-completeness. San Francisco: Freeman 1979

    Google Scholar 

  67. Christofides, N.: Worst case analysis of a new heuristic for the travelling salesman problem, Report 388, Graduate School of Industrial Administration, Carnegie-Mellon University, Pittsburgh, P.A. (1976)

    Google Scholar 

  68. Johnson, D.S., Papadimitriou, C.H.: Chap. 5: The traveling salesman problem. Lawler, E.L., Lenstra, J.K., Rinnooy Kan, A.H.G., Shmoys, D.B. (eds.). New York: John Wiley 1984

    Google Scholar 

  69. Lin, S.: Bell Syst. Tech. J.44, 2245 (1965)

    Google Scholar 

  70. Papadimitrou, C.H., Steiglitz, K.: SIAM J. Comput.6, 76 (1977)

    Google Scholar 

  71. Beardwood, J., Halton, J.H., Hammersley, J.M.: Proc. Camb. Philos. Soc.55, 299 (1959)

    Google Scholar 

  72. Karp, R.M.: Math. Oper. Res.2, 209 (1979);

    Google Scholar 

  73. Karp, R.M.: SIAM J. Comput.8, 561 (1979)

    Google Scholar 

  74. Karp, R.M., Steele, J.M.: Chap. 6.: The traveling salesman problem. Lawler, E.L., Lenstra, J.K., Rinnooy Kan, A.H.G., Shmoys, D.B. (eds.), New York: John Wiley 1984

    Google Scholar 

  75. Gurke, R.: Ein Näherungsalgorithmus zur Lösung des euklidischen Traveling Salesman Problems auf einem MIMD-Rechner PARS-Workshop April 1987, Jülich, FRG

  76. Litke, J.D.: Comm. ACM27, 1227 (1984)

    Google Scholar 

  77. Padberg, M.W., Grötschel, M.: Chap. 8, 9: The traveling salesman problem. Lawler, E.L., Lenstra, J.K., Rinnooy Kan, A.H.G., Shmoys, D.B. (eds.), New York,: John Wiley 1984

    Google Scholar 

  78. Crowder, H., Padberg, M.W.: Management Sci.26, 495 (1980)

    Google Scholar 

  79. Padberg, M.W., Rinaldi, G.: Oper. Res. Lett.6, 1 (1987)

    Google Scholar 

  80. Hopfield, J.J., Tank, D.: Biol. Cybern.5, 141 (1985)

    Google Scholar 

  81. Durbin R., Willshaw, D.: Nature326 689 (1987)

    Google Scholar 

  82. Holland, J.H.: Adaptation in natural and artificial systems. Ann Arbor: University of Michigan Press 1975

    Google Scholar 

  83. Schwefel, H.P.: Collective Phenomena in Evolutionary Systems. In: Problems of Constancy and Change. Vol. II, 31 st Annual Meeting of the Int. Soc. for General System Res., Budapest, Hungary, June 1987

  84. Grefenstette, J.J.: In: Genetic algorithms and simulated annealing. Davis, L. (ed.). Los Altos: Morgan Kaufmann Publ. 1987

    Google Scholar 

  85. Kirkpatrick, S., Toulouse, G.: J. Phys. (Paris)46, 1277 (1985);

    Google Scholar 

  86. Sourlas, N.: Europhys. Lett.2, 919 (1986)

    Google Scholar 

  87. Gould, J.S., Eldredge, N.: Paleobiology3, 115 (1977)

    Google Scholar 

  88. Huberman, B.A., Hogg, T.: The behavior of computational ecologies. In: The ecology of computation. Amsterdam, Oxford, New York: North-Holland 1988

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ruján, P. Searching for optimal configurations by simulated tunneling. Z. Physik B - Condensed Matter 73, 391–416 (1988). https://doi.org/10.1007/BF01314278

Download citation

  • Received:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01314278

Keywords

Navigation