摘要
基于遗传算法和单纯形法构造出了一种混合优化算法,对不同编码方式的算法进行了对比分析。发现混合算法的寻优能力明显优于遗传算法的寻优能力。实数编码的混合算法能够更好的保持种群多样性,在存在多个局部最优解的情况下,比二进制编码的混合算法的寻优能力强。利用构造的基于实数编码的混合算法,结合Hicks-Henne型函数参数化方法和Kriging代理模型,对高速列车的截面变化率进行了减小气动阻力的优化设计,得到了在设计空间内的最优截面变化率。优化后,三辆编组列车的气动阻力减小9.41%,其中,压差阻力减小38.02%,摩擦阻力基本不变,头车气动阻力减小12.55%,尾车气动减小13.98%。
A hybrid optimization algorithm （HOA） is proposed based on genetic algorithm （GA） and simplex method. The searching abilities of optimization algorithms with different coding methods are compared. The results show that the searching abilities of HOA is remarkable performance against that of GA. The real-coded HOA can maintain the population diversity and is competitive with the binary-coded HOA when solving problems of many lo- cal optimal solution. In order to reducing the aerodynamic drag of high-speed trains, the cross-sectional area distri- bution of a high-speed train nose is optimized with the real-coded HOA combined with Hicks-Henne function para- metric method and Kriging surrogate model, and the best cross-sectional area distribution in the design space is found. The aerodynamic drag of the original shape is reduced by 9. 41% , the viscous drag is reduced by 38.02%, the inviscid drag change little, the aerodynamic drag of the nose and the trailing car is reduced by 12.55% and 13.98% , respectively.
出处
《科学技术与工程》
北大核心
2013年第28期8349-8355,共7页
Science Technology and Engineering
基金
国家科技支撑计划(2009BAG12A03)
国家重点基础研究发展计划("973")(2011CB71100)资助
关键词
混合算法
截面变化率
遗传算法
Kriging代理模型
高速列车
hybrid algorithm distribution of cross-sectional area genetic algorithm Kriging surro-gate model high-speed trains
