Ship Appearance Optimal Design on RCS Reduction Using Response Surface Method and Genetic Algorithms

被引:0
作者
杨德庆
郭凤骏
机构
[1] SchoolofNavalArchitecture,OceanandCivilEngineering,ShanghaiJiaotongUniversity
关键词
radar cross section (RCS); characteristic section design method; response surface method; genetic algorithm (GA);
D O I
暂无
中图分类号
U662 [船舶设计];
学科分类号
082401 ;
摘要
Radar cross section (RCS) reduction technologies are very important in survivability of the military naval vessels. Ship appearance shaping as an effective countermeasure of RCS reduction redirects the scattered energy from one angular region of interest in space to another region of little interest. To decrease the scattering electromagnetic signals from ship scientifically, optimization methods should be introduced in shaping design. Based on the assumption of the characteristic section design method, mathematical formulations for optimal shaping design were established. Because of the computation-intensive analysis and singularity in shaping optimization, the response surface method (RSM) combined genetic algorithm (GA) was proposed. The poly-nomial response surface method was adopted in model approximation. Then genetic algorithms were employed to solve the surrogate optimization problem. By comparison RCS of the conventional and the optimal design, the superiority and effectiveness of proposed design methodology were verified.
引用
收藏
页码:336 / 342
页数:7
相关论文
共 4 条
[1]   Some variants of the controlled random search algorithm for global optimization [J].
Kaelo, P. ;
Ali, M. M. .
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS, 2006, 130 (02) :253-264
[2]  
Combining response surfaces and evolutionary strategies for multiobjective pareto-optimization in electromagnetics[J] . A. Bramanti,P. Di Barba,M. Farina,A. Savini.International Journal of Applied Electromagnetics and Mechanics . 2001 (1-4)
[3]   Adaptive response surface method - A global optimization scheme for approximation-based design problems [J].
Wang, GG ;
Dong, ZM ;
Aitchison, P .
ENGINEERING OPTIMIZATION, 2001, 33 (06) :707-733
[4]   MULTI-FACTOR EXPERIMENTAL-DESIGNS FOR EXPLORING RESPONSE SURFACES [J].
BOX, GEP ;
HUNTER, JS .
ANNALS OF MATHEMATICAL STATISTICS, 1957, 28 (01) :195-241