A stochastic technique for global optimization

AbstractA stochastic technique for multiextremal optimization is discussed; the technique derives from the adoption of sequential stopping rules for the well-known Multistart algorithm. The stopping rules are developed in a Bayesian nonparametric framework. A class of multiextremal test functions is introduced and results obtained through the application of the proposed techniques to this class are presented. The results show the effectiveness of the proposed stopping rules