中文摘要：

针对复杂非线性系统多参数优化设计不仅计算工作量大，而且难以获得理论上最优解的问题，提出将全局敏感性分析和动态代理模型技术相结合的多参数多目标优化策略。通过基于方差的Sobol全局敏感性分析精简系统模型，确定敏感参数，并构造基于敏感参数的多目标代理模型，采用 NSGA-II 遗传优化算法得到当代最优解。优化过程中，代理模型和搜索空间不断更新，最优解附近的精度不断提高，直到满足优化迭代的收敛准则。将本方法应用于某汽车乘员约束系统的概念优化设计中，乘员的头部损伤指标（Head injury criterion, HIC）、胸部3 ms伤害指标C3ms和胸部压缩量D分别降低了11.3%、11.8%和9.4%，优化结果和最优解附近的精度都优于静态代理模型与NSGA-II遗传优化算法，取得明显的优化效果，证明了研究方法的有效性。

英文摘要：

Multi-parameter optimization design of complex nonlinear system with high nonlinearity involves huge computer, and the optimal solution in theory is also difficult to obtain. A new method is proposed that combines the global sensitivity analysis with dynamic metamodel. By using the variance-based Sobol global sensitivity analysis method, the complex system model is simplified, and the sensitivity parameters are defined to construct the multi-objective metamodel, which is solved by using the genetic optimization algorithm of NSGA-II to obtain the contemporary optimal solution. In the process of optimization, the metamodel and the searching space are continually updated, and the accuracy of the solutions in the optimal zone is improved gradually till the optimization iterations terminate with the convergence criteria satisfied. This method is used to concept optimization design of a vehicle occupant restraint system and good results are achieved with the head injury criterion（HIC）, chest acceleration C3ms and chest deflection D reduced by 11.3%, 11.8%and 9.4%respectively. The optimization results and the precision of the near optimal solution is better than that of static metamodel and the NSGA-II genetic optimization algorithm, which has proved the validity of the proposed new method.