Randomized algorithms in interval global optimization

This paper is a critical survey of the interval optimization methods aimed at computing the global optima of multivariable functions. To overcome some drawbacks of traditional deterministic interval techniques, we outline the ways of constructing stochastic (randomized) algorithms in interval global optimization, in particular those based on the ideas of a random search and simulated annealing.