In this study, a new methodology, hybrid NSGA-III with SPEA/R (HNSGA-III&SPEA/R), has been developed to design and achieve cost optimization of powertrain mount system stiffness parameters. This problem is formalized as a multi-objective optimization problem involving six optimization objectives: mean square acceleration and mean square displacement of the powertrain mount system. A hybrid HNSGA-III&SPEA/R is proposed with the integration of Strength Pareto evolutionary algorithm based on reference direction for Multi-objective (SPEA/R) and Many-objective optimization genetic algorithm (NSGA-III). Several benchmark functions are tested, and results reveal that the HNSGA-III&SPEA/R is more efficient than the typical SPEA/R, NSGA-III. Powertrain mount system stiffness parameters optimization with HNSGA-III&SPEA/R is simulated respectively. It proved the potential of the HNSGA-III&SPEA/R for powertrain mount system stiffness parameter optimization problem.

在这项研究中，开发了一种新方法，即混合 NSGA-III 与 SPEA/R（HNSGA-III&SPEA/R），以设计动力总成悬置系统刚度参数并实现成本优化。这个问题被形式化为一个多目标优化问题，涉及六个优化目标：动力总成悬置系统的均方加速度和均方位移。基于多目标参考方向的强度帕累托进化算法(SPEA/R)和多目标优化遗传算法(NSGA-III)，提出了一种混合HNSGA-III&SPEA/R。测试了几个基准函数，结果表明 HNSGA-III&SPEA/R 比典型的 SPEA/R、NSGA-III 更有效。使用 HNSGA-III& 优化动力总成悬置系统刚度参数 分别模拟SPEA/R。它证明了 HNSGA-III&SPEA/R 在动力总成悬置系统刚度参数优化问题上的潜力。