Lipschitzian optimization without the Lipschitz constant
Journal of Optimization Theory and Applications
Tabu Search
Journal of Global Optimization
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
A Radial Basis Function Method for Global Optimization
Journal of Global Optimization
Proceedings of the 6th International Conference on Genetic Algorithms
Radial Basis Functions
Constrained Global Optimization of Expensive Black Box Functions Using Radial Basis Functions
Journal of Global Optimization
Journal of Global Optimization
Low Dimensional Simplex Evolution--A Hybrid Heuristic for Global Optimization
SNPD '07 Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 02
An adaptive radial basis algorithm (ARBF) for expensive black-box global optimization
Journal of Global Optimization
Simulated annealing: Practice versus theory
Mathematical and Computer Modelling: An International Journal
Lipschitz gradients for global optimization in a one-point-based partitioning scheme
Journal of Computational and Applied Mathematics
Block matching algorithm for motion estimation based on Artificial Bee Colony (ABC)
Applied Soft Computing
Block-matching algorithm based on harmony search optimization for motion estimation
Applied Intelligence
Hi-index | 7.29 |
Expensive optimization aims to find the global minimum of a given function within a very limited number of function evaluations. It has drawn much attention in recent years. The present expensive optimization algorithms focus their attention on metamodeling techniques, and call existing global optimization algorithms as subroutines. So it is difficult for them to keep a good balance between model approximation and global search due to their two-part property. To overcome this difficulty, we try to embed a metamodel mechanism into an efficient evolutionary algorithm, low dimensional simplex evolution (LDSE), in this paper. The proposed algorithm is referred to as the low dimensional simplex evolution extension (LDSEE). It is inherently parallel and self-contained. This renders it very easy to use. Numerical results show that our proposed algorithm is a competitive alternative for expensive optimization problems.