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Optimization design of car door structure considering aesthetics and safety.

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Τίτλος: Optimization design of car door structure considering aesthetics and safety.
Συγγραφείς: Ning Y; College of Fine Arts, Silla University, Busan, Republic of Korea., Kim K; College of Fine Arts, Silla University, Busan, Republic of Korea.
Πηγή: PloS one [PLoS One] 2026 Sep 28; Vol. 21 (9), pp. e0357798. Date of Electronic Publication: 2026 Sep 28 (Print Publication: 2026).
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
Imprint Name(s): Original Publication: San Francisco, CA : Public Library of Science
Ιατρικοί όροι (MeSH): Algorithms* , Esthetics* , Safety* , Automobiles*, Models, Theoretical ; Genetic Algorithms ; Humans
Περίληψη: 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.
(Copyright: © 2026 Ning, Kim. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Competing Interests: The authors have declared that no competing interests exist.
Entry Date(s): Date Created: 20260928 Date Completed: 20260928 Latest Revision: 20260930
Update Code: 20260930
PubMed Central ID: PMC13618961
DOI: 10.1371/journal.pone.0357798
PMID: 42804451
Βάση Δεδομένων: MEDLINE
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  Data: <searchLink fieldCode="MM" term="%22Algorithms%22">Algorithms*</searchLink> <br /><searchLink fieldCode="MM" term="%22Esthetics%22">Esthetics*</searchLink> <br /><searchLink fieldCode="MM" term="%22Safety%22">Safety*</searchLink> <br /><searchLink fieldCode="MM" term="%22Automobiles%22">Automobiles*</searchLink><br /><searchLink fieldCode="MH" term="%22Models%2C+Theoretical%22">Models, Theoretical</searchLink> ; <searchLink fieldCode="MH" term="%22Genetic+Algorithms%22">Genetic Algorithms</searchLink> ; <searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink>
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  Data: 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.<br /> (Copyright: © 2026 Ning, Kim. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
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  Data: The authors have declared that no competing interests exist.
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