The particle damper as a passive means for vibration suppression, is preferred due to its simplicity and easy to implement and control, with no need for any auxiliary power equipment. The bottleneck of the design for the particle damper is that damping performance is influenced by many parameters including particle material and size, packing ratio and cavity dimensions of enclosure such as length, width and diameter. In fact, the overall damping effect of particle damper is closely related to the primary structure system parameters. However, the influences of the excitation point, dampers arrangements, excitation force amplitude, and excitation force type for overall damping effect are scarce reported in the open literatures. How to exert particle damper on the structure and how many particles filled within the cavity, which are needed to face the problem in the engineering practice. In the face of doubt above-mentioned, the above questions will be clarified in this paper. At the same time, an optimization algorithm by the application of genetic algorithms of BP neural network is carried out in order to get most excellent damping effect. These will offer significance guidance to design and conduct particle dampers implement in the specific engineering practice with reference significance
