图 书 馆
替代辅助优化方法：最新发展和挑战Surrogate Assisted Optimization Methods: Recent Developments and Challenges |
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| 课程网址: | http://videolectures.net/mla09_ray_saom/ |
| 主讲教师: | Tapabrata Ray |
| 开课单位: | 新南威尔士大学 |
| 开课时间: | 2009-07-20 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | There is an ever increasing trend in the use of more and more accurate and often more computationally expensive analyses tools in early stages of design. Optimization using such computationally expensive analyses tools demand the use of surrogate assisted methods, where a surrogate or an approximation is used in lieu of the expensive analysis to contain the computational time within affordable limits. The performance of such methods is known to be largely dependent on the choice of the underlying optimization algorithm, the surrogate model, the training and surrogate model management schemes. This presentation will introduce a surrogate assisted optimization framework developed by the MDO Group at UNSW[url] which alleviates some of the common problems associated with the current approaches. In the proposed approach, surrogates of multiple types (MLP, RBF, Kriging and RSM) coexist within the optimization framework at all times and the surrogate with the least prediction error (based on neighborhood RMSE) is used to approximate the objective and the constraint functions individually. An external archive of all solutions evaluated via actual analysis is maintained to train the surrogates, while a surrogate validity check is performed prior to its use to avoid misguiding the search (in the event of poor approximation or attempts to approximate in unexplored regions). The underlying optimization algorithm is a population based, elitist evolutionary algorithm which explicitly maintains marginally infeasible solutions for a faster rate of convergence. Apart from standard recombination schemes used in any evolutionary algorithm, a memetic recombination operator is embedded to further improve the rate of convergence. A number of examples will be presented to illustrate the performance of the proposed schemes. Finally, the presentation will list areas of further development and present some preliminary results of constrained many objective optimization and spatial approximation schemes that are currently being developed by the group. |
| 关 键 词: | 分析工具; 基础优化算法; 基础优化算法 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-06-28：cjy |
| 阅读次数: | 64 |