期刊
INTERNATIONAL JOURNAL OF VEHICLE DESIGN
卷 80, 期 2-4, 页码 223-240出版社
INDERSCIENCE ENTERPRISES LTD
DOI: 10.1504/IJVD.2019.109866
关键词
surrogate-assisted optimisation; crash box design; evolutionary algorithm; constrained optimisation; meta-heuristics; crashworthiness optimisation; Kriging model
资金
- Thailand Research Funds (TRF) [RTA6180010]
This work proposes a multi-surrogate-assisted optimisation and performance investigation of several newly developed metaheuristics (MHs) for the optimisation of vehicle crashworthiness. The optimisation problem for car crashworthiness is posed to find the shape and size of a crash box while the objective function is to maximise the total energy absorption subject to a mass constraint. Two main numerical experiments are conducted. Firstly, the performance of different surrogate models along with the proposed multi-surrogate model is investigated. Secondly, several MHs are applied to tackle the proposed crashworthiness optimisation problem by employing the best obtained surrogate model. The results reveal that the proposed multi-surrogate model is the best performer. Among the several MHs used in this study, sine cosine algorithm is the best algorithm for the proposed multi-surrogate model. Based on this study, the application of the proposed multi-surrogate model is better than using one particular traditional surrogate model, especially for constrained optimisation.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据