应用多目标粒子群的驾驶室悬置参数联合仿真优化
Co-simulation of Cab Mounting System Optimization using MOPSO Algorithm

为简化悬置系统开发流程、缩短周期，以某型装载机驾驶室悬置系统为对象，在ADAMS中建立虚拟样机模型，采用试验测试信号作为激励，并在MATLAB中编写了优化算法程序，通过ADAMS和MATLAB联合仿真的方法，解决了驾驶室悬置隔振性能的多目标优化问题。以振动总量值最小化和解耦率最大化作为优化目标，分别采用了多目标粒子群算法（MOPSO）与非支配排序遗传算（NSGA-Ⅱ）法，结果表明前者能找到更好的帕累托前沿，仿真分析验证了该方法的可行性和有效性。
In order to simplify development process and cut down the period of cab mounting system, the co-simulation method is used to solve the multi-objective optimization problem of a loader cab mounting system's vibration isolation performance whose virtual prototype is built in ADAMS, excitation signals come from experimental test and optimization algorithms are coded in MATLAB. The objective includes minimization of vibration total value and maximization decoupling ratio. The multi-objective particle swarm optimization (MOPSO) algorithm shows a better optimization performance than non-dominated sorting genetic (NSGA-Ⅱ) algorithm by covering a better Pareto frontier in this problem. Simulation results also confirm the feasibility and effectiveness of the approach in this study