https://scholars.lib.ntu.edu.tw/handle/123456789/403690
標題: | An adaptive sequential linear programming algorithm for optimal design problems with probabilistic constraints | 作者: | Chan, K.-Y. Skerlos, S.J. Papalambros, P.Y. |
關鍵字: | RBDO; probabilistic design optimization; design under uncertainty; sequential linear programming | 公開日期: | 2007 | 出版社: | ASME | 來源出版物: | Journal of Mechanical Design, Transactions of the ASME | 摘要: | Optimal design problems with probabilistic probabilistic constraints, often referred to as reliability-based design optimization problems, have benn the subject of extensive recent studies. Solution methods to date have focused more on improving efficiency rather than accuracy and the global convergence behavior of the solution. A new strategy utilizing an adaptive sequential linear programming (SLP) algorithm is proposed as a promising approach to balance accuracy, efficiency, and convergence. The strategy transforms the nonlinear probabilistic constraints into equivalent deterministic ones using both first order and second order approximations, and applies a filter-based SLP algorithm to reach the optimum. Simple numerical examples show promise for increased accuracy without sacrificing efficiency. Copyright © 2007 by ASME. |
URI: | http://www.scopus.com/inward/record.url?eid=2-s2.0-34248393897&partnerID=MN8TOARS https://scholars.lib.ntu.edu.tw/handle/123456789/403690 |
ISSN: | 10500472 | DOI: | 10.1115/1.2337312 |
顯示於: | 機械工程學系 |
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