An efficient hybrid framework for reliability-based size and shape optimization of trusses considering uncertainties

Abstract

Reliability-based design optimization (RBDO) of truss structures has become a powerful tool for achieving optimal designs under uncertainties in material properties, cross-sectional areas, and applied loadings. By integrating the sequential optimization and reliability assessment (SORA) into an efficient optimization tool based on the success-history-based adaptive differential evolution (SHADE) algorithm, this paper presents a novel RBDO methodology, which is the so-called SORA-SHADE, for the optimization of shape and size in truss structures under uncertainties of input data. The proposed paradigm is tested on well-known 2D and 3D trusses with and without the reliability analyses for validation.   For reliability-based analysis cases, each cross-sectional area is considered as a random variable under uncertainties. The results show that the proposed approach is highly effective and reliable in finding optimal designs that meet high reliability requirements.