Affiliation of Author(s):航空学院
Journal:Beijing Hangkong Hangtian Daxue Xuebao
Abstract: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 No.:1001-5965
Translation or Not:no
Date of Publication:2018-01-01
Co-author:Ren, Chao,Zhang, Hang
Correspondence Author:lhs
Date of Publication:2018-01-01