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

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.