基于NSGA-II遗传算法的磁流变悬置多目标优化

磁流变悬置集总参数优化是设计高性能发动机悬置的关键。为克服以往悬置优化中优化目标单一、优化目标选取不合理、未考虑实际加工可行性等问题，建立单自由度磁流变悬置隔振系统数学模型，提出倍程区间灵敏度分析法，对各集总参数灵敏度进行分析，并以此为依据选取优化变量。以发动机常用转速激振频率段的力传递率积分为优化目标，采用改进型非支配排序遗传算法(NSGA-II)进行多目标优化。在一定范围内将结构尺寸进行离散化处理，计算各组离散尺寸对应的集总参数值，以离散集总参数与集总参数Pareto非劣解之间的综合距离为准则筛选最优解。

Multi-objective Optimization for MR Engine Mount Based on NSGA-II Algorithm

To achieve a high performance, the design optimization of lumped parameter for Magneto-Rheological (MR) engine mount is essential. Mathematical model of single degree vibration isolation system was established and multiple interval sensitivity method is proposed to overcome drawbacks of conventional optimization design such as single objective optimization, improper optimization objective, unfeasible machining and so on. Optimization variables in MR engine mount are selected by lumped parameter multiple interval sensitivity analysis. The integral of force transmissibility through normal frequency range of engine is assigned as objective functions, non-dominated sorting genetic algorithm (NSGA-II) is improved and used to optimize design variables. The synthesized distance between Pareto lumped parameters and discontinuous lumped parameters that match along with the physical discretization dimension were calculated to select the most appropriate solution from Pareto lumped parameters.