Optimization design of car door structure considering aesthetics and safety
- 1College of Fine Arts, Silla University, Busan, Republic of Korea
Abstract
To enhance the performance of multi-objective optimization algorithms in the optimization design of car door structures and comprehensively optimize the aesthetics and safety of car doors, an improved multi-objective grey wolf optimization algorithm is proposed. The core of this improvement lies not only in using the optimal solution set method for population initialization to enhance uniformity and diversity, but more importantly, in introducing a novel adaptive double population strategy. This strategy fundamentally reshapes the search behavior of the algorithm by dynamically balancing local development and global exploration throughout the optimization process. At the same time, a hierarchical optimization model for the car door structure is constructed, which organizes parameters on three functionally complementary and logically progressive levels: geometric layer, structural layer, and material layer. 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.
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BibTeX
@article{ning2026optimization,
title = {{Optimization design of car door structure considering aesthetics and safety}},
author = {Yu’an Ning and Kiesu Kim},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0357798},
url = {https://doi.org/10.1371/journal.pone.0357798},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Optimization design of car door structure considering aesthetics and safety
AU - Yu’an Ning
AU - Kiesu Kim
PY - 2026
DA - 2026/09/28
JO - PLOS One
DO - 10.1371/journal.pone.0357798
UR - https://doi.org/10.1371/journal.pone.0357798
AB - To enhance the performance of multi-objective optimization algorithms in the optimization design of car door structures and comprehensively optimize the aesthetics and safety of car doors, an improved multi-objective grey wolf optimization algorithm is proposed. The core of this improvement lies not only in using the optimal solution set method for population initialization to enhance uniformity and diversity, but more importantly, in introducing a novel adaptive double population strategy. This strategy fundamentally reshapes the search behavior of the algorithm by dynamically balancing local development and global exploration throughout the optimization process. At the same time, a hierarchical optimization model for the car door structure is constructed, which organizes parameters on three functionally complementary and logically progressive levels: geometric layer, structural layer, and material layer. 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.
SN - 1932-6203
ER -