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Experiments with new stochastic global optimization search techniques. (English) Zbl 0967.90086
Summary: In this paper several probabilistic search techniques are developed for global optimization under three heuristic classifications: simulated annealing, clustering methods and adaptive partitioning algorithms. The algorithms proposed here combine different methods found in the literature and they are compared with well-established approaches in the corresponding areas. Computational results are obtained on 77 small to moderate size (up to 10 variables) nonlinear test functions with simple bounds and 18 large size test functions (up to 400 variables) collected from literature.

90C26 Nonconvex programming, global optimization
90B40 Search theory
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