Application of the optimal Latin hypercube design and radial basis function network to collaborative optimization
Min Zhao , Wei-cheng Cui
Journal of Marine Science and Application ›› 2007, Vol. 6 ›› Issue (3) : 24 -32.
Application of the optimal Latin hypercube design and radial basis function network to collaborative optimization
Improving the efficiency of ship optimization is crucial for modern ship design. Compared with traditional methods, multidisciplinary design optimization (MDO) is a more promising approach. For this reason, Collaborative Optimization (CO) is discussed and analyzed in this paper. As one of the most frequently applied MDO methods, CO promotes autonomy of disciplines while providing a coordinating mechanism guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. However, there are some difficulties in applying the conventional CO method, such as difficulties in choosing an initial point and tremendous computational requirements. For the purpose of overcoming these problems, optimal Latin hypercube design and Radial basis function network were applied to CO. Optimal Latin hypercube design is a modified Latin Hypercube design. Radial basis function network approximates the optimization model, and is updated during the optimization process to improve accuracy. It is shown by examples that the computing efficiency and robustness of this CO method are higher than with the conventional CO method.
multidisciplinary design optimization (MDO) / collaborative optimization (CO) / optimal Latin hypercube design / radial basis function network / approximation
| [1] |
|
| [2] |
|
| [3] |
MCALLISTER C D, SIMPSON T W, KURTZ P H, et al. Multidisciplinary design optimization test based on autonomous underwater vehicle design[R]. AIAA-2002-5630, Atlanta: 2002. |
| [4] |
|
| [5] |
SOBIESKI I P, MANNING V M, KROO I M. Response surface estimation and refinement in collaborative optimization[R]. AIAA-1998-4753, St. Louis, 1998. |
| [6] |
KROO I M, MANNING V M. Collaborative optimization: status and directions[R]. AIAA-2000-4721, Long Beach, 2000. |
| [7] |
de MIGUEL A V, MURRAY W. An Analysis of Collaborative Optimization Methods [R]. AIAA-2000-4720, Long Beach, 2000. |
| [8] |
|
| [9] |
|
| [10] |
|
| [11] |
ERFANIAN, A, Chaotic radial basis function network with application to dynamic modeling of chaotic time series[A]. Chang H k and Zhang Y T. Proceedings of the 20th Annual International Conference of the IEEE[C]. Hong Kong, 1998. |
| [12] |
HE Xiangdong, LAPEDES A. Successive approximation radial basis function networks for nonlinear modeling and prediction[A]. Proceedings of 1993 International Joint Conference on Neural Networks[C]. Nagoya, 1993. |
| [13] |
|
| [14] |
|
| [15] |
KANSA E J. Motivation for using radial basis functions to solve PDEs[EB/OL]. 1999-08-24, http://rbf-pde.uah.edu/kansaweb.html/. |
| [16] |
JANG B S, YANG Y S, JUNG H S, et al. Managing approximation models in collaborative optimization[J]. Structural and Multidisciplinary Optimization, 30(1): 11–26. |
| [17] |
|
| [18] |
|
| [19] |
|
/
| 〈 |
|
〉 |