Shape optimization of automotive body frame using an improved genetic algorithm optimizer

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.