Multi objective optimization of passenger car rear seats using a hybrid surrogate model and adaptive sampling

In the context of frequent automobile collisions, optimizing passenger car seat structures is critical for improving vehicle safety and economic efficiency. This paper proposes a novel multi-objective optimization method for rear seats. A finite element analysis model under luggage collision conditions was developed and validated to ensure numerical accuracy. The material type and structural thickness of key stress areas in the backrest skeleton were selected as design variables, while displacement at safety index test points, material cost, and mass were established as optimization objectives. Regulatory requirements were incorporated as constraints to construct the multi-objective optimization model. An adaptive optimal hybrid surrogate model (AOHSM) was then built using the optimal Latin hypercube design and a point-adding strategy, integrated with the non-dominated sorting genetic algorithm II to derive the Pareto front solution set. Compared with local optimization methods and global optimization based on static surrogate models, the proposed AOHSM achieves superior optimization efficiency and performance, highlighting its methodological novelty. Finally, the solutions were ranked using a multi-criteria decision-making method based on combined weighting and improvement degree evaluation to identify the optimal compromise solution. The results show that, while meeting safety regulations, the optimized seat material cost was reduced by 27.67% and the mass decreased by 17.51%, significantly improving economic and lightweight performance. The proposed method also demonstrates strong optimization efficiency, solution accuracy, and engineering practicality, for example, being directly applicable to real seat structure design and manufacturing, thereby providing a valuable reference for collaborative automotive seat design.