Abstract
This study develops an enhanced surrogate modeling method integrating back propagation neural network (BPNN) with an improved sparrow search algorithm (SSA) reinforced by reinforcement learning (ISSA-RL). The SSA algorithm is substantially modified through Tent chaotic mapping for population initialization to improve distribution uniformity, combined with a nonlinear adaptive weighting strategy to better balance global and local search capabilities. A multi-agent reinforcement learning framework based on Q-learning is incorporated to dynamically adjust search strategies according to prediction error and population diversity metrics. The proposed method demonstrates high predictive accuracy, which is rigorously validated through benchmark functions and engineering applications. The BPNN-based surrogate model effectively replaces computationally expensive finite element analyses, while uncertainty quantification techniques enhance model robustness against material property fluctuations and loading variations. To determine the optimal configuration of a framed body-in-white (BIW), a structural optimization considering four types of discrete design variables is formulated and optimized by the proposed method. The results show that a 29.2% mass reduction and higher computational efficiency are achieved. The integration of multi-variable optimization with enhanced neural network training and intelligent search algorithms significantly improves both design quality and computational efficiency for complex BIW structures.