Cross-section optimization of vehicle body through multi-objective intelligence adaptive optimization algorithm.

Cross-section optimization is an effective way to improve the mechanical performance of a vehicle body and reduce its structural mass. However, previous studies suffer from the deficiencies involving inaccurate cross-sectional model, insufficient consideration of manufacturability constraints and inefficient single-objective optimization. In this work, eight typical cross-sections of a body are optimized. A chain node-based parametric modeling is proposed to realize accurately cross-sectional discretization, and the geometric and manufacturability constraints as well as three optimization objectives are considered in the cross-sectional optimization models. To realize multi-objective optimization, a multi-objective intelligence adaptive optimization algorithm (MIAOA) is proposed. By classifying the non-dominated solutions and applying a reward-penalty strategy, the MIAOA realizes intelligent iteration. The experimental results on ZDT and DTLZ suites obtained by MIAOA are better than those of five typical algorithms in terms of convergence, stability, uniformity and extensiveness. Besides, the MIAOA is applied to improve the moments of inertia of the cross-sections and reduce their material areas. These optimized cross-sections are applied to the body, and the optimized body shows better mechanical performances involving torsional stiffness, bending stiffness, first-order mode and second-order mode, while reducing the total mass by 9.96 kg. In conclusion, the proposed methods can effectively realize lightweight automobiles. [ABSTRACT FROM AUTHOR]