Analysis of static simulated annealing algorithms
Journal of Optimization Theory and Applications
Simulated Annealing and Graph Colouring
Combinatorics, Probability and Computing
A dynamic programming approach to efficient sampling from Boltzmann distributions
Operations Research Letters
Biological plausibility in optimisation: an ecosystemic view
International Journal of Bio-Inspired Computation
International Journal of Computer Applications in Technology
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Simulated Annealing has proven to be a very successful heuristic for various combinatorial optimization problems. It is a randomized algorithm that attempts to find the global optimum with high probability by local exchanges. In this paper we give a new proof of the convergence of Simulated Annealing by applying results about rapidly mixing Markov chains. With this proof technique it is possible to obtain better bounds for the finite time behavior of Simulated Annealing than previously known.