论文标题
用于混合分类结构优化问题的外部近似双层框架
An outer approximation bi-level framework for mixed categorical structural optimization problems
论文作者
论文摘要
在本文中，研究了混合的分类结构优化问题。目的是最大程度地减少相对于横截面区域，材料和横截面类型的桁架结构的重量。提出的方法包括使用涉及两个问题的双层分解：主和奴隶。主问题被公式为混合整数线性问题，其中线性约束使用从属问题解决方案的外近似值逐渐增强。从属问题解决了优化问题的连续变量。提出的方法对三个不同的结构优化测试案例进行了测试，复杂性的增加。与最先进的算法的比较强调了所提出的方法的效率，该方法在最佳质量，计算成本以及相对于问题维度方面的可伸缩性。还测试了一个具有挑战性的120杆圆顶桁架优化问题，每个栏也有90个分类选择。获得的结果表明，我们的方法能够有效地解决大规模混合分类结构优化问题。
In this paper, mixed categorical structural optimization problems are investigated. The aim is to minimize the weight of a truss structure with respect to cross-section areas, materials and cross-section type. The proposed methodology consists of using a bi-level decomposition involving two problems: master and slave. The master problem is formulated as a mixed integer linear problem where the linear constraints are incrementally augmented using outer approximations of the slave problem solution. The slave problem addresses the continuous variables of the optimization problem. The proposed methodology is tested on three different structural optimization test cases with increasing complexity. The comparison to state-of-the-art algorithms emphasizes the efficiency of the proposed methodology in terms of the optimum quality, computation cost, as well as its scalability with respect to the problem dimension. A challenging 120-bar dome truss optimization problem with 90 categorical choices per bar is also tested. The obtained results showed that our method is able to solve efficiently large scale mixed categorical structural optimization problems.
