<p>Optimization effect of material parameters.</p>

An aesthetic function is constructed using the curvature uniformity index, and a safety function is constructed combining collision intrusion and chest acceleration, forming a multi-objective joint evaluation function. The research results indicate that in the multi-objective testing function, the improved multi-objective grey wolf optimization algorithm has an inverse generation distance of 0.041, which is 32.7% lower than the original multi-objective grey wolf optimization algorithm.

In the high-dimensional testing problem Door3, the inverted generational distance of the improved multi-objective grey wolf optimization algorithm is only 0.082, which is 58.6% higher than that of the non-dominated sorting genetic algorithm II. After improving the multi-objective grey wolf optimization algorithm, the aesthetic function value of car doors increases from 0.62 to 0.89, the collision intrusion decreases from 65 mm to 42 mm, and the chest acceleration decreases from 45g to 32g.

This study provides an efficient algorithm and feasible solution for multi-objective optimization of car door structures, and customized designs can be obtained by adjusting the aesthetic and safety weights.</p></div>