Abstract:
The convergence rate of the simulated annealing algorithm is examined. It is shown that, if the objective function is nonsingular, then the number of its evaluations required to obtain the desired accuracy $\varepsilon$ in the solution can be a slowly (namely, logarithmically) growing function as $\varepsilon$ approaches zero.
Key words:simulated annealing algorithm, random search, global optimization, estimate of convergence rate.
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