| 匡兵,李应弟,刘夫云,刘娟.基于单元密度进化步长控制的双向渐进结构优化方法[J].计算力学学报,2016,33(1):15~21 |
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| 基于单元密度进化步长控制的双向渐进结构优化方法 |
| Bi-directional evolutionary structural optimization method based on control for evolutionary step length of element density |
| 投稿时间：2014-09-24 修订日期：2014-11-19 |
| DOI：10.7511/jslx201601003 |
| 中文关键词: 渐进结构优化方法 单元密度进化步长 权重系数 灵敏度误差 棋盘格式 |
| 英文关键词:evolutionary structural optimization method evolutionary step length of element density weight coefficient sensitivity error checkerboard pattern |
| 基金项目:国家自然科学基金(51265006)资助项目. |
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| 摘要点击次数: 2076 |
| 全文下载次数: 1486 |
| 中文摘要: |
| 在渐进结构优化方法中,单元密度的进化步长是获得全局最优解的关键因素之一。为了提高渐进结构优化方法的全局寻优能力,提出一种基于单元密度进化步长控制的双向渐进结构优化方法。该方法根据各单元对结构性能影响的权重系数,建立单元密度进化步长的控制模型以控制主/次要单元的删除速率和添加速率,减小灵敏度误差并抑制灰度单元的产生。在控制单元密度进化步长的基础上结合双向渐进结构优化方法中添加单元的特点,以避免由于误删单元导致优化失败。同时,采用灵敏度再分配技术抑制棋盘格式以获得更平滑的优化构形。最后,通过两个算例验证了本文方法能有效地通过控制单元密度进化步长提高全局寻优能力。 |
| 英文摘要: |
| In evolutionary structural optimization (ESO) method, evolutionary step length of element density is one of key factors to obtain global optimal solution of optimization problem.To improve the ability of global optimization for ESO method, this paper presents bi-directional evolutionary structural optimi-zation method (BESO) based on control for evolutionary step length of element density.Control model of evolutionary step length of element density is built according to weight coefficient of each unit impacting on structure performance.The rate of elements being deleted or added of the principal elements and the secondary elements is controlled by using the proposed control model.Evolutionary step length of element density is controlled so that error of sensitivity can be decreased and gray elements are controlled.To avoid optimization of failure on account of error deleting element, the operation for add element of BESO method is combined based on controlling evolutionary step length of element density.At the same time, checkerboard pattern is controlled by using sensitivity redistribution technology to obtain smooth optimal structure.Finally, the presented method is verified by two numerical examples can effectively decrease sensitivity error by controlling evolutionary step length of element density to improve the ability of global optimization. |
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