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中文摘要：
汽车结构的耐撞性及碰撞吸能优化是一个涉及到多变量、多约束和多目标的优化过程。为克服常规响应面法在整个设计空间进行逼近导致精度低和传统的单目标优化设计只能针对其中的一个目标进行优化的缺陷。提出采用逐次逼近方法，通过移动、缩放等方式在设计空间中不断更新兴趣域，在不同的兴趣域中将试验设计、能代表实际碰撞过程精度较高的近似模型和多目标粒子群优化算法相结合，获得一组最小化各目标函数的非劣解。利用最小距离选解法快速有效地从非劣解集中挑选出一组耐撞性效果晟好的解并以此解作为下～迭代步兴趣域的中心，直到收敛至最优解，最终优化解的各个目标函数值均得到提高。数字算例表明，该方法具有较高的精度和较强的工程实用性。
英文摘要：
Automotive structural optimization related to crashworthiness and energy absorption capability is a process of optimization which involves multi-variable, multi-constraint and multi-objective. To solve the problem of low precision of conventional response surface method caused by the approaching in the whole design space and the problem of traditional single-objective optimization design as it can only optimize one objective, a successive approximation method is proposed. Through moving and zooming, the region of interest is constantly renewed in the design space. In the different regions of interest, the experimental design, a high-precision approximation model that can represent the actual collision process, and multi-objective particle swarm optimization algorithm are combined, then a set of minimized non-inferior solutions for objective functions are obtained. By using the minimum distance solution selection method, a set of solutions with best crashworthiness effect are rapidly and effectively selected from the set of non-inferior solutions, and taken as the center of region of interest of next iterative step until converging to the best solution, then all the objective functions of the final solution are improved. Numerical example indicates that this method has high precision and strong engineering practicability.
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