Convergence and first hitting time of simulated annealing algorithms for continuous global optimization

In this paper simulated annealing algorithms for continuous global optimization are considered. Under the simplifying assumption of known optimal value, the convergence of the algorithms and an upper bound for the expected first hitting time, i.e. the expected number of iterations before reaching the global optimum value within accuracy ϵ, are established. The obtained results are compared with those for the ideal algorithm Pure Adaptive Search and for the simple Pure Random Search algorithm.