The dynamic vibration absorber, which is adopted to suppress the unbalanced vibration of rotor, is optimized for the optimal parameters in this paper. This paper proposes a parameter optimization method for dynamic vibration absorbers and seeks parameters of a dynamic vibration absorber with better vibration suppression performance. Firstly, the frequency response function of the dynamic vibration absorber-rotor coupling system is obtained by using the finite element method. Then, basing on the optimal mathematical model, the optimal design variables are solved with the adaptive particle swarm optimization algorithm. Also, an example is used to prove the validity of the optimization design method mentioned in this paper. Further, in order to master the influence of deviation from the optimal value on the suppressing vibration effect, the vibration suppression performance changes of the dynamic vibration absorber whose parameters deviate from the optimal value are analyzed. The results show that: compared with conventional design method, this method is more superior; The dynamic vibration absorber with optimal parameters has better vibration suppression performance; At the same degree deviated from the optimal value, the stiffness has a more remarkable influence on the vibration suppression performance than damping for suppressing the first resonance; For the dynamic vibration absorber which is adopted to suppress the fixed-frequency vibration, the influence of stiffness deviation on the vibration suppression performance appears an obvious interval which is related to working speed.

Based on the resource allocation method of bipartite graph projected to one-mode graph, the bipartite graph network is established using six evaluating comfortableness indices and seven subsystems of high-speed train as the vertices. Our research shows that: This method can be used to find out the key factors which have high influence on the high-speed trains comprehensive comfortableness; also its ratio can be obtained. The overall quality of the high-speed train also can be forecasted by this method if the seven subsystems performance indices are given. Meanwhile, the interacting dependency level among the indices can be obtained.