Applications of Optimization Methods in Automotive and Agricultural Engineering: A Review

Wenjing Zhao, Libin Duan (), Baolin Ma, Xiangxin Meng, Lifang Ren, Deying Ye and Shili Rui
Additional contact information
Wenjing Zhao: Institute of Lightweight and Safety of New Energy Vehicle, School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Libin Duan: Institute of Lightweight and Safety of New Energy Vehicle, School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Baolin Ma: Institute of Lightweight and Safety of New Energy Vehicle, School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Xiangxin Meng: Institute of Lightweight and Safety of New Energy Vehicle, School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Lifang Ren: Institute of Lightweight and Safety of New Energy Vehicle, School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Deying Ye: Chery Automobile Co., Ltd., Wuhu 241006, China
Shili Rui: Chery Automobile Co., Ltd., Wuhu 241006, China

Mathematics, 2025, vol. 13, issue 18, 1-37

Abstract: The automotive and agricultural industries face increasingly stringent demands with technological advancements and rising living standards, resulting in substantially heightened engineering complexity. In this background, optimization methods become indispensable tools for solving diverse engineering challenges. This narrative review paper provides a comprehensive overview of the application and challenges of five optimization algorithms, including gradient-based optimization algorithms, heuristic algorithms, surrogate model-based optimization algorithms, Bayesian optimization algorithms, and hybrid cellular automata algorithms in two fields. To accomplish this objective, the research literature published from 2000 to the present is analyzed, focusing on automotive structural optimization, material optimization, crashworthiness, and lightweight design, as well as agricultural product inspection, mechanical parameter optimization, and ecological system optimization. A classification framework for optimization methods is established based on problem characteristics, elucidating the core strengths and limitations of each method. Cross-domain comparative studies are conducted to provide reference guidance for researchers in related fields.

Keywords: automotive; agricultural; gradient-based; heuristic algorithms; surrogate model-based; Bayesian optimization; hybrid cellular automata (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/18/3018/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/18/3018/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:18:p:3018-:d:1752254

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().