STP: A Stochastic Tunneling Algorithm for Global Optimization
A stochastic approach to solving continuous function global optimization problems is presented. It builds on the tunneling approach to deterministic optimization presented by Barhen et al, by combining a series of local descents with stochastic searches. The method uses a rejection-based stochastic procedure to locate new local minima descent regions and a fixed Lipschitz-like constant to reject unpromising regions in the search space, thereby increasing the efficiency of the tunneling process. The algorithm is easily implemented in low-dimensional problems and scales easily to large problems. It is less effective without further heuristics in these latter cases, however. Several improvements to the basic algorithm which make use of approximate estimates of the algorithms parameters for implementation in high-dimensional problems are also discussed. Benchmark results are presented, which show that the algorithm is competitive with the best previously reported global optimization techniques. A successful application of the approach to a large-scale seismology problem of substantial computational complexity using a low-dimensional approximation scheme is also reported.
- Research Organization:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- (US)
- DOE Contract Number:
- AC05-00OR22725
- OSTI ID:
- 814395
- Report Number(s):
- ORNL/TM-13737; TRN: US200317%%204
- Resource Relation:
- Other Information: PBD: 20 May 1999
- Country of Publication:
- United States
- Language:
- English
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