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所属单位：航空学院

发表刊物：Beijing Hangkong Hangtian Daxue Xuebao

摘要：Cross entropy method is an efficient and adaptive stochastic optimization method and has immense potential in complex optimization problems with high dimension and nonlinear constraints. However, the traditional cross entropy method is lack of accuracy. In this study, both the concepts of current elite samples and global elite samples are introduced to extract more useful information from the whole iterative history. Then, a new parameter updating strategy is established based on these two concepts. New adaptive smoothing strategy and mutation operation are also applied to improve its computing performance. The proposed algorithm is illustrated by three numerical examples. The computational results indicate that the improved cross entropy method has higher calculation accuracy and better global search capability. © 2018, Editorial Board of JBUAA. All right reserved.

ISSN号：1001-5965

是否译文：否

发表时间：2018-01-01

合写作者：Ren, Chao,Zhang, Hang

通讯作者：李洪双