渐进演化类拓扑优化算法的优化准则对比研究
DOI:
作者:
作者单位:

湖南科技大学

作者简介:

通讯作者:

中图分类号:

基金项目:


Comparative Study on Optimization Criteria of Evolutionary Topology Optimization Algorithms
Author:
Affiliation:

Hunan University of Science and technology

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    渐进类演化类拓扑优化算法的优化准则是影响结构优化结果的关键因素之一。本文以不同荷载和边界条件下的深梁模型为数值算例,比较了基于不同优化准则的三种算法在优化解和优化效率上的差别。结果表明,采用单向和确定性优化准则的渐进演化类拓扑优化算法对于荷载和边界等条件较简单的构件能高效地得到最优拓扑,采用概率性优化准则和采用双向优化准则的渐进演化类拓扑优化算法有着更广的适用范围,在荷载和边界等条件较复杂的构件上同样表现出较强的避免优化畸变的能力和全局寻优能力。本文中最后还对结合概率性优化准则和双向优化准则的遗传双向渐进演化结构优化算法建立了流程图,展开了初步讨论,以期进一步提高渐进演化类拓扑优化算法的实用性和寻优能力。

    Abstract:

    The optimization criterion of evolutionary topology optimization algorithms is one of the key factors affecting the structural optimization results. In this paper, the deep beam model under different load and boundary conditions is taken as a numerical example, and comparing the difference between the optimization solution and the optimization efficiency of the three algorithms based on different optimization criteria. The results show that the evolutionary topology optimization algorithms based on one-way optimization criteria and deterministic optimization criteria can efficiently obtain the optimal topology for components with simple conditions such as load and boundary, and the evolutionary topology optimization algorithms based on probabilistic optimization criterion and bidirectional optimization criterion has a stronger scope of application, and it is also shows strong ability to avoid optimized distortion and global optimization on components with complicated conditions such as load and boundary. At the end of this paper, a flow chart is established for the genetic bidirectional evolutionary structural optimization algorithm combining probabilistic optimization criterion and bidirectional optimization criterion, the preliminary discussion is carried out to further improve the practicability and optimization ability of evolutionary structural optimization algorithm.

    参考文献
    相似文献
    引证文献
引用本文
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2019-07-08
  • 最后修改日期:2019-08-20
  • 录用日期:2019-09-02
  • 在线发布日期:
  • 出版日期: