A systematic and efficient sequential iterative global multi-objective optimization strategy for optimal design of rear seat in a passenger car

Ensuring a balance between optimization efficiency and time cost has gradually become a primary focus of researchers' concerns. In this paper, an efficient design strategy for automotive seat optimization is proposed based on the concept of sequential iterative optimization. Initially, a finite element analysis model for the rear seat baggage crash test of passenger cars is established and validated through test results. Key optimization variables are identified through comprehensive contribution analysis, considering factors such as total cost, seat material mass, and safety performance indices. An advanced optimization strategy is then developed, integrating optimal Latin hypercube experimental design, adaptive genetic aggregation response surface surrogate model, non-dominated sorting genetic algorithm III, fuzzy analytic hierarchy process, improved criteria importance through intercriteria correlation combination weights, and an improved technique for order preference by similarity to ideal solution method based on Kullback-Leibler distance and grey relational analysis. Subsequently, this strategy is applied to optimize the automotive seat skeleton. The proposed global optimization strategy is compared with both surrogate model-based global optimization and local optimization strategies, demonstrating its significant advantages in optimization search performance and time cost. Optimization results show that the maximum horizontal deformation at the headrest and backrest measurement points of the rear seat is reduced by 10.89% and 8.07%, respectively, while material cost is reduced by 26.94% and weight is reduced by 28.25%. Thus, the multi-objective optimization strategy proposed in this paper proves effective and accurate, offering a reliable reference for related optimization efforts.