Abstract
A stochastic method for global optimization is described and evaluated. The method involves a combination of sampling, clustering and local search, and terminates with a range of confidence intervals on the value of the global optimum. Computational results on standard test functions are included as well. © 1982 The Mathematical Programming Society, Inc.
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Boender, C. G. E., Rinnooy Kan, A. H. G., Timmer, G. T., & Stougie, L. (1982). A stochastic method for global optimization. Mathematical Programming, 22(1), 125–140. https://doi.org/10.1007/BF01581033
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