Shape optimization of automotive body frame using an improved genetic algorithm optimizer. (July 2018)

• Record Type: Journal Article
• Authors: Qin, Huan; Guo, Yi; Liu, Zijian; Liu, Yu; Zhong, Haolong
• Is Part Of: Advances in engineering software. Volume 121(2018)
• Page Start: 235
• Page End: 249
• Publication Date: 2018-07
• Subjects: BIW frame -- Shape optimization -- Penalty-parameterless approach -- Improved genetic algorithm -- Meta-heuristic algorithms -- Integrated optimizer; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2018.03.015
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: Shape optimization of automotive body frame is proposed to promote the conceptual development of auto-body. An improved and penalty-parameterless genetic algorithm (IGA) optimizer is developed. IGA is capable of solving single-objective and multi-objective optimization problems. IGA is benchmarked on 12 functions and compared with multiple recent metaheuristic algorithms. IGA is utilized to solve the constrained shape optimization problem. Abstract: At conceptual design stage, the cross-sectional shape design of automotive body-in-white (BIW) frame is a critical and intractable technique. This paper presents shape optimization using an improved genetic algorithm (GA) optimizer to promote the development of auto-body. The shape optimization problem is formulated as a mass minimization problem with static stiffness, dynamic eigenfrequency and manufacture constraints. Then the transfer stiffness matrix method (TSMM) proposed in our previous study is adopted for the exact static and dynamic analyses of BIW frame. Additionally, the scale vector method is introduced to remarkably reduce design variables. Especially, an integrated object-oriented GA optimizer, which employs penalty-parameterless approach to handle constraints, is developed to solve constrained single-objective and multi-objective optimization problems. The optimizer is benchmarked on 12 test functions and compared with a variety of current metaheuristic algorithms to demonstrate its validity and effectiveness. Lastly, the optimizer is applied to the solution of BIW shape optimization.