Simultaneous distribution and sizing optimization for stiffeners with an improved genetic algorithm with two-level approximation

被引:5
|
作者
Chen, Shenyan [1 ]
Dong, Tianshan [1 ]
Shui, Xiaofang [1 ]
机构
[1] Beihang Univ, Sch Astronaut, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
Stiffened shells; simultaneous optimization; mixed variables; genetic algorithm; two-level approximation; DESIGN; PLATES;
D O I
10.1080/0305215X.2018.1558444
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This article presents an improved genetic algorithm with two-level approximation (GATA) to optimize the distribution and size of stiffeners simultaneously. A novel optimization model of stiffeners, including two kinds of design variables, is established. The first level approximation problem transforms the original implicit problem to an explicit problem which involves the topology and size variables. Then, a genetic algorithm (GA) addresses the mixed variables. The individuals in the GA are coded by topology variables, and when calculating an individual's fitness, the second level approximation problem is embedded to optimize the size variables. Considering the stiffeners' optimization, several aspects of the initial GATA are updated, including the relationship between two kinds of variables, the weight and its sensitivity calculation and the GA strategy, to optimize the stiffeners' size and distribution simultaneously. Numerical examples show that the improved GATA is effective in optimizing the stiffened shells' topology and size variables simultaneously.
引用
收藏
页码:1845 / 1866
页数:22
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