Abstract
Simulated annealing is a powerful stochastic technique for solving combinatorial optimization problems. It differs from other techniques based on iterative improvements of the cost function in that it allows occasional increases in the cost function thus avoiding to get stuck into local minima. Metropolis first proposed the simulated annealing method to study complex physical systems which are first melted at high temperatures and then slowly cooled to attain low energy equilibrium configurations. The analogy between this problem and a combinatorial optimization problem was observed in [2] and [13]. In this analogy, the energy of the system is identified with the cost function of the optimization problem and the temperature with a control parameter.
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© 1992 Springer Science+Business Media New York
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Berna, A., Bongiovanni, G., Crescenzi, P., Guerra, C. (1992). Parallel Simulated Annealing for Shape Detection. In: Arcelli, C., Cordella, L.P., di Baja, G.S. (eds) Visual Form. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-0715-8_6
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