基于自适应进化率的拓扑优化方法研究

Research on Topology Optimization Method Based on Adaptive Evolutionary Rate

由于双向渐进结构优化法(BESO)拓扑优化中,进化率值常采用恒定值形式,这会导致在迭代初期计算缓慢,后期又会快速更新单元结构,很难保证迭代计算效率。本文分别基于两类函数构建两种自适应进化率机制,以动态控制进化率值,提高拓扑优化的计算效率。算例测试结果表明,所提出的自适应进化率模型相对于恒定值型提升了40%的计算效率。

In BESO method,the evolution rate value often adopts a constant value,leading to slow calculation in the early iteration period and rapid updating of elements in the later period,which is difficult to ensure the efficiency of iterative calculation.In this paper,two adaptive evolutionary rate mechanisms are constructed based on Two-class functions respectively to dynamically control evolutionary rate value and improve the computational efficiency of topology optimization.The test results of a numerical example show that the proposed adaptive evolution rate model improves the computational efficiency by 40% compared to the constant value type.