Efficient Hyperparameter Optimization Using Metaheuristics for Machine Learning in Truss Steel Structure Cross-Section Prediction

Abstract

The optimal design of truss structures is one of the most complex problems, as it requires achieving high stiffness and stability while pursuing lightweight structures. With the recent advancements in AI technologies, machine learning-based approaches for predicting the optimal cross-sectional areas of truss structures have garnered significant attention from researchers. However, the design problem of truss structures poses substantial challenges for machine learning models due to the highly diverse and nonlinear characteristics of the optimal cross-sectional distributions, which may hinder effective learning. To address these limitations, the importance of hyperparameter optimization (HPO) has been increasingly recognized. This paper employs metaheuristic algorithms, which are efficient in searching for global optima, to perform HPO on 10-bar and 17-bar truss structure datasets. By balancing exploitation and exploration capabilities, metaheuristic algorithms demonstrate superior performance and time efficiency compared to conventional HPO methods. The results underscore the critical role of hyperparameters in machine learning-based truss structure design and suggest that leveraging metaheuristic algorithm-based HPO holds significant potential for addressing complex structural design problems in future applications.