In this paper the authors are concerned with global optimization, which can be defined as the problem of finding points on a bounded subset of ℝn in which some real valued function f assumes its optimal (maximal or minimal) value. They present a stochastic approach which is based on the simulated annealing algorithm. The approach closely follows the formulation of the simulated annealing as originally given for discrete optimization problems. The mathematical formulation is extended to continuous optimization problems, and the authors prove asymptotic convergence to the set of global optima. Furthermore, they discuss an implementation of the algorithm and compare its performance with other well-known algorithms. The performance evaluation is carried out for a standard set of test functions from the literature.
