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A hybrid global optimization method: The multi-dimensional case
- Source :
-
Journal of Computational & Applied Mathematics . Jun2003, Vol. 155 Issue 2, p423. 24p. - Publication Year :
- 2003
-
Abstract
- We extend the hybrid global optimization method proposed by Xu (J. Comput. Appl. Math. 147 (2002) 301–314) for the one-dimensional case to the multi-dimensional case. The method consists of two basic components: local optimizers and feasible point finders. Local optimizers guarantee efficiency and speed of producing a local optimal solution in the neighbourhood of a feasible point. Feasible point finders provide the theoretical guarantee for the new method to always produce the global optimal solution(s) correctly. If a nonlinear nonconvex inverse problem has multiple global optimal solutions, our algorithm is capable of finding all of them correctly. Three synthetic examples, which have failed simulated annealing and genetic algorithms, are used to demonstrate the proposed method. [Copyright &y& Elsevier]
- Subjects :
- *MATHEMATICAL optimization
*INTERVAL analysis
Subjects
Details
- Language :
- English
- ISSN :
- 03770427
- Volume :
- 155
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- Journal of Computational & Applied Mathematics
- Publication Type :
- Academic Journal
- Accession number :
- 9856609
- Full Text :
- https://doi.org/10.1016/S0377-0427(02)00878-6