基于加权几何平均迭代的改进BESO法  

Improved BESO Method Based on Weighted Geometric Mean Iteration

作　　者：余威 韩艳彬 种永刚 鲁世红[1] YU Wei;HAN Yanbin;CHONG Yonggang;LU Shihong(College of Mechanical and Electrical Engineering,Nanjing University of Aeronautics&Astronautics,Nanjing,210016,China;AVIC Xi’an Aircraft Industry Group Company LTD,Xi’an,710089,China)

机构地区：[1]南京航空航天大学机电学院,南京210016 [2]航空工业西安飞机工业集团有限公司,西安710089

出　　处：《南京航空航天大学学报》2020年第3期416-421,共6页Journal of Nanjing University of Aeronautics & Astronautics

摘　　要：为了解决传统双向渐进结构优化法中存在迭代历程易出现局部振荡现象、算法效率低的问题,提出了一种基于加权几何平均迭代的改进双向渐进结构优化法。通过研究当前迭代步灵敏度权重因子和历史迭代步敏度权重因子对结构优化过程的影响程度,与当前迭代步敏度权重因子对应的迭代历程变化趋势,实现了最优当前迭代步敏度权重因子的优化选择。3个经典算例验证了较原生过滤法与基于算术平均的过滤法两种处理方法,本文方法在保持了同等刚度的同时,减轻了迭代历程的震荡程度,显著提高了迭代的稳定性,减少了迭代次数,效率提高了10%~37.5%,说明该方法的可行性与有效性。In order to solve the problem that the iterative process is prone to local oscillation and the algorithm is inefficient in the traditional BESO method,an improved BESO method based on weighted geometric mean iteration is proposed.By studying the influence degree of the current historical iterative step sensitivity weighting factor on the structural optimization process,and the iterative history change trend corresponding to the current iterative step sensitivity weighting factor,the optimal selection of current iterative step sensitivity weight is realized.It has been verified by three classical examples.Compared with the original filtering method and the filtering method based on arithmetic mean,the method presented can reduce the oscillation degree of the iterative process while maintaining the same stiffness,and significantly improve the stability of the iteration.The number of iterations is reduced,and the efficiency is increased by 10%—37.5%,which illustrates the feasibility and effectiveness of the method.

关 键 词：双向渐进结构优化法 均化处理 加权几何平均 权重因子 

分 类 号：TH11[机械工程—机械设计及理论]