Heuristic solution methods for combinatorial optimization problems are often based on local neighborhood searches. These tend to get trapped in a local optimum and the final result is often heavily dependent on the starting solution. Simulated annealing methods attempt to avoid these problems by randomizing the procedure so as to allow for occasional changes that worsen the solution. In this paper we provide probabilistic analyses of different designs of these methods.
Anily, Shoshana, and Awi Federgruen. "Simulated annealing methods with general acceptance probabilities." Journal of Applied Probability 24, no. 3 (September 1987): 657-667.
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