Considerable academic research has been conducted on truss design optimization by standard metaheuristic methods; however, the generic nature of these methods becomes inefficient for problems with many decision variables. This may explain the simplicity of the relevant test problems in the academic literature in comparison with real structures. To address this challenge, this study advocates a customized optimization methodology which utilizes problem-specific knowledge. It improves upon a new bilevel truss optimization method to allow for an arbitrary trade-off between the stochastic upper level and the deterministic lower level search. Numerical simulations demonstrate that for large-scale truss design problems, the proposed method can find significantly lighter structures up to 300 times more quickly than the best existing metaheuristic methods. The remarkable findings of this study demonstrate the importance of using engineering knowledge and discourage future research on the development of purely metaheuristic methods for truss optimization.

已经通过标准的元启发式方法对桁架设计进行了大量的学术研究。但是，这些方法的通用性对于具有许多决策变量的问题变得效率低下。与真实结构相比，这可以解释学术文献中相关测试问题的简单性。为了应对这一挑战，本研究提倡一种利用特定问题知识的定制优化方法。它对新的双层桁架优化方法进行了改进，以允许在随机高层和确定性较低层搜索之间进行任意权衡。数值模拟表明，对于大规模的桁架设计问题，所提出的方法可以找到比现有的最佳元启发式方法快300倍的轻得多的结构。