基于改进遗传算法的双向渐进结构优化方法研究

Research on bi-directional evolutionary structural optimization method based on improved genetic algorithm

本文提出了一种基于改进遗传算法的软删除双向渐进结构优化法(G-BESO),以解决传统双向渐进结构优化法(BESO)中参数(如进化率)设置不当而导致无法获得最优拓扑构型的问题。首先确定单元权重系数与单元密度的递推关系式,形成一种考虑单元密度历史信息的材料插值模型,从而增强恢复误删高效单元的能力。然后引入遗传算法中的交叉和变异操作,启发式地更新结构状态以提高全局寻优能力。最后将该方法编写成可用于实际工程结构优化设计的程序。算例表明,提出的方法能稳定得到最优拓扑形状且计算效率更高,可为工程结构的拓扑优化设计提供一定参考。

A bi-directional evolutionary structural optimization method based on improved genetic algorithm is proposed to solve the problem of inability to obtain optimal topological layouts if the parameters(removal ratio)are not appropriately set in traditional bi-directional evolutionary structural optimization(BESO).First⁃ly,the relationship between the element weight coefficient and the element density is determined,forming a new material interpolation scheme that takes into account the historical information of element density,thus enhancing the ability to recover efficient elements deleted in previous iterations.Then,the crossover and mu⁃tation operations in the genetic algorithm are introduced to heuristically update the structure,which improves the global optimization ability.Finally,the proposed method is programmed for the optimization of practical engineering structures.The validity and robustness of the method are verified with two benchmark designs,which can provide reference for the optimal design of engineering structures.