Abstract
The Wiener process is a widely used statistical model for stochastic global optimization. One of the first optimization algorithms based on a statistical model, the so-called P-algorithm, was based on the Wiener process. Despite many advantages, this process does not give a realistic model for many optimization problems, particularly from the point of view of local behavior. In the present paper, a version of the P-algorithm is constructed based on a stochastic process with smooth sampling functions. It is shown that, in such a case, the algorithm has a better convergence rate than in the case of the Wiener process. A similar convergence rate is proved for a combination of the Wiener model-based P-algorithm with quadratic fit-based local search.
Original language | English (US) |
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Pages (from-to) | 479-495 |
Number of pages | 17 |
Journal | Journal of Optimization Theory and Applications |
Volume | 102 |
Issue number | 3 |
DOIs | |
State | Published - Sep 1999 |
All Science Journal Classification (ASJC) codes
- Management Science and Operations Research
- Control and Optimization
- Applied Mathematics
Keywords
- Gaussian processes
- Global optimization