.png){kind=icon}
.png){kind=icon}
Return
Human-in-the-loop optimization for vehicle body lightweight design
.png){kind=icon}
PRE
AI
H
R
L
Z
R
L
B
Z
Y
DOI：10.1016/j.aei.2024.102887.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Automatic optimization algorithms are crucial for vehicle body lightweight design; however, existing methods remain inefficient leading to excessive iterations that increase both time and costs. Current interactive optimization strategies partially mitigate this issue but lack a broad range of manipulation points and auxiliary information models. As such, we introduce a novel approach, Human-in-the-Loop based method for Vehicle Body Lightweight Design (HIL-VBLD). This method integrates human decision-making with optimization algorithms to reduce unproductive iterations. HIL-VBLD comprises two key components: (1) an innovative interaction mode that provides multiple manipulation points including constraint modification, algorithm switching, and selection of solutions of interest (SOI); (2) A comprehensive auxiliary information model that supports decision-making for designers. Our analysis demonstrates HIL-VBLD's efficacy, showing a 54.5 % reduction in iteration cycles for genetic algorithm using SOI selection. Algorithm switching led to a 4.5 % mass reduction, mitigating local optimum pitfalls associated with gradient algorithms. Additionally, the auxiliary information model achieved a further 1.25 % mass reduction, enhancing optimization robustness. Compared to conventional automatic algorithm switching strategies, HIL-VBLD maintains equivalent accuracy with 23.9 % fewer iterations.
Keywords:
Human-in-the-loop
Lightweight vehicle body
Optimization design
Interaction interface
Journal
IF:
9.9
Papers:
4.0K
Citations:
1.7W
Organization
Cited Papers