Name
Abstract | ||
|---|---|---|
We propose an integrated algorithm named low dimensional simplex evolution extension (LDSEE) for expensive global optimization in which only a very limited number of function evaluations is allowed. The new algorithm accelerates an existing global optimization, low dimensional simplex evolution (LDSE), by using radial basis function (RBF) interpolation and tabu search. Different from other expensive global optimization methods, LDSEE integrates the RBF interpolation and tabu search with the LDSE algorithm rather than just calling existing global optimization algorithms as subroutines. As a result, it can keep a good balance between the model approximation and the global search. Meanwhile it is self contained. It does not rely on other GO algorithms and is very easy to use. Numerical results show that it is a competitive alternative for expensive global optimization. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICNC.2010.5584477 | ICNC |
Keywords | Field | DocType |
radial basis function interpolation,rbf interpolation,response surface,radial basis function networks,model approximation,evolutionary computation,tabu search,interpolation,low dimensional simplex evolution,go algorithms,low dimensional simplex evolution extension,global search,search problems,expensive global optimization,expensive global optimization method,integrated evolutionary algorithm,ldsee algorithm,radial basis function,optimization,algorithm design and analysis,construction industry,response surface methodology,evolutionary algorithm,approximation algorithms,evolutionary computing,global optimization | Radial basis function,Evolutionary algorithm,Computer science,Interpolation,Artificial intelligence,Approximation algorithm,Mathematical optimization,Algorithm design,Global optimization,Algorithm,Evolutionary computation,Tabu search,Machine learning | Conference |
Volume | ISBN | Citations |
5 | 978-1-4244-5958-2 | 0 |
PageRank | References | Authors |
0.34 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Changtong Luo | 1 | 36 | 5.66 |
| Chun Wang | 2 | 5 | 3.80 |
| Zonglin Jiang | 3 | 18 | 2.81 |
| Shao-Liang Zhang | 4 | 92 | 19.06 |