We use a probability to accept new worse solutions. This probability depends on the modification of the objective function
A
brief bibliography:
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S. Kirkpatrick, C.D. Gelatt, y M.P. Vecchi. Optimization by Simulated Annealing. Science, 220:671{680, 1983. |
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V. Cerny, A thermodynamical approach to the travelling salesman problem: an efficient simulation algorithm. Journal of Optimization Theory and Applications, 45:41-51, 1985 |
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P.J.M. van Laarhoven y E.H.L. Aarts. Simulated Annealing: Theory and Applications. Kluwer Academic Press, 1987. |
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P.J.M. van Laarhoven and E.H.L. Aarts, 1987, Simulated Annealing: Theory and Applications, D.Reidel Publishing Company, Kluwer |
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E.H.L. Aarts y J. Korst. Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing. Wiley, 1989. |
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E. Weinberger, Correlated and Uncorrelated Fitness Landscapes and How to Tell the Difference, Biological Cybernetics, 63, No. 5, 325-336 (1990). |
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D.S. Johnson, C.R. Aragon, L.A. McGeoch, and C. Schevon. Optimization by simulated annealing: An experimental evaluation; part i, graph partitioning. Operations Research, 37:865–892, 1989. |
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R.V.V. Vidal. Applied Simulated Annealing, volume 396 of Lecture Notes in Econ. and Math. Systems. Springer Verlag, 1993. |
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Dowsland, K.A. (1993) “Simulated annealing,” in C. Reeves, editor, “Modern Heuristic Techniques for Combinatorial Problems,” Halsted, Blackwell, pp. 20–69. |
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A. Das and B. K. Chakrabarti (Eds.), Quantum Annealing and Related Optimization Methods, Lecture Note in Physics, Vol. 679, Springer, Heidelberg (2005) |
Here
there is an Java applet that allows you to experiment with simulated
annealing.