On a new stochastic global optimization algorithm based on censored observations

In this paper a new algorithm is proposed for global optimization problems. The main idea is that of modifying a standard clustering approach by sequentially sampling the objective function while adaptively deciding an appropriate sample size. Theoretical as well as computational results are presented.

[1]  B. Betrò.  Bayesian testing of nonparametric hypotheses and its application to global optimization , 1984 .

[2]  Fabio Schoen,et al.  Optimal and sub-optimal stopping rules for the Multistart algorithm in global optimization , 1992, Math. Program..

[3]  G. T. Timmer,et al.  Stochastic global optimization methods part I: Clustering methods , 1987, Math. Program..

[4]  M. Piccioni,et al.  Stopping eules for the multistart method when different local minima have different function values , 1990 .

[5]  Bruno Betrò,et al.  Bayesian methods in global optimization , 1991, J. Glob. Optim..

[6]  Alexander H. G. Rinnooy Kan,et al.  Bayesian stopping rules for multistart global optimization methods , 1987, Math. Program..

[7]  Fabio Schoen,et al.  A wide class of test functions for global optimization , 1993, J. Glob. Optim..

[8]  G. T. Timmer,et al.  Stochastic global optimization methods part II: Multi level methods , 1987, Math. Program..

[9]  T. Ferguson,et al.  Bayesian Nonparametric Estimation Based on Censored Data , 1979 .

[10]  Fabio Schoen,et al.  Sequential stopping rules for the multistart algorithm in global optimisation , 1987, Math. Program..

[11]  Aimo A. Törn,et al.  Global Optimization , 1999, Science.

[12]  Fabio Schoen,et al.  Stochastic techniques for global optimization: A survey of recent advances , 1991, J. Glob. Optim..

[13]  S. Nash.  Newton-Type Minimization via the Lanczos Method , 1984 .

[14]  A. A. Zhigli︠a︡vskiĭ,et al.  Theory of Global Random Search , 1991 .