A metamodel-assisted evolutionary algorithm for expensive optimization

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

卷:236

Article; Proceedings Paper

Luo, Changtong¹  Zhang, Shao-Liang²  Wang, Chun¹  Jiang, Zonglin¹ 

[1] Chinese Acad Sci, Inst Mech, Beijing 100190, Peoples R China

[2] Nagoya Univ, Dept Computat Sci & Engn, Nagoya, Aichi 4648603, Japan

关键词: Expensive optimization;    Evolutionary algorithm;    Low dimensional simplex evolution;    Metamodel;    Radial basis function;   

DOI  :  10.1016/j.cam.2011.05.047

来源: Elsevier

【 摘 要 】

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. (C) 2011 Elsevier B.V. All rights reserved.

【 授权许可】

Free