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A new method of discrete optimization for cross-section selection of truss structures

机译:桁架结构截面选择的离散优化新方法

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The main goal of this article is to develop a new method of discrete optimization for cross-section selection of truss structures. First, it introduces a parameterized description of discrete cross-section areas in an admissible list, which is an ordered list of manufacturing available cross-section areas (i.e. the 'available list' of cross-section areas) and constructs a discrete optimization model for truss structures. Secondly, the generalized shape function-based parameterization (GSFP) method is proposed to transform discrete variables obtained previously into continuous ones, thus transforming the discrete optimization problem into a continuous optimization problem which can be readily solved with gradient-based methods. Thirdly, by comparing the influences of different admissible lists formed with elements of the same available list, on the convergences of the optimization process, an ordering rule is proposed to determine the order of elements in the admissible list. Lastly, the proposed method is applied to several benchmark design examples, generating results with similar or improved accuracy compared to those from heuristic methods, showing significantly improved computational efficiency. The method is shown to be accurate and efficient, which would prove especially beneficial to large-scale problems.
机译:本文的主要目的是为桁架结构的横截面选择开发一种离散优化的新方法。首先,它在可允许列表中引入离散截面区域的参数化描述,该列表是制造可用截面区域的有序列表(即截面区域的“可用列表”),并构造了一个离散优化模型用于桁架结构。其次,提出了基于广义形状函数的参数化(GSFP)方法,将先前获得的离散变量转换为连续变量,从而将离散优化问题转换为可以通过基于梯度的方法轻松解决的连续优化问题。第三,通过比较由相同可用列表元素形成的不同可允许列表的影响,对优化过程的收敛性,提出一种排序规则,以确定可允许列表中元素的顺序。最后,将所提出的方法应用于几个基准设计实例,与启发式方法相比,所生成的结果具有相似或更高的准确性,从而显着提高了计算效率。该方法被证明是准确和有效的,这将证明对大规模问题特别有利。

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