التفاصيل البيبلوغرافية
| العنوان: |
Kriging-based convex subspace single linkage method with path-based clustering technique for approximation-based global optimization. |
| المؤلفون: |
Sakata, Sei-ichiro, Ashida, Fumihiro, Tanaka, Hiroyoshi |
| المصدر: |
Structural & Multidisciplinary Optimization; Sep2011, Vol. 44 Issue 3, p393-408, 16p |
| مصطلحات موضوعية: |
KRIGING, CONVEX domains, MATHEMATICAL optimization, CLUSTER theory (Nuclear physics), NUMERICAL analysis |
| مستخلص: |
This paper proposes an improved approach of the Kriging-based Convex Subspace Single Linkage Method (KCSSL method), which was reported as one of approximation-based global optimization methods. The KCSSL method consists of a convex subspace clustering procedure and a local optimization procedure. For the clustering procedure, previously, the cell-based clustering technique was employed. However, this approach will involve a huge number of convexity estimations in case of a higher dimensional problem. This will cause a very high computational cost, therefore, a path-based clustering procedure is newly developed. At first, a procedure for the convexity estimation with the Kriging method is introduced. Next, outline and detailed procedure of the proposed path-based clustering technique are explained. Also, the proposed method is applied to solving some approximate optimization problems. From the numerical results, validity and effectiveness of the proposed method are discussed. [ABSTRACT FROM AUTHOR] |
|
Copyright of Structural & Multidisciplinary Optimization is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
