基于改进粒子群算法的机车二系弹簧载荷分配优化

The optimization of spring loads distribution can be summarized as a complicated nonlinear multidimensional optimization problem. In order to further improve the optimization effect and calculation efficiency of the existing solution methods, an improved particle swarm optimization algorithm(IPSO) with hierarchical structure was proposed by integrating the fireworks algorithm(FWA) into a particle swarm optimization algorithm(PSO). The IPSO is a three-layer architecture. The underlying layer was the basic framework of the IPSO and a classical PSO that adopted a dimensional mutation operator. The middle layer was a fusion layer used to expand the search range of IPSO. In essence, it was a particle update layer that introduced the explosion mechanism in FWA. The top layer was the disturbance layer, with a disturbance factor introduced to avoid redundant iteration due to local search and speed up the global convergence. The IPSO was tested on typical test function and applied to the simulation experiment of the distribution optimization and adjustment of the locomotive secondary spring loads. The results show that compared with traditional genetic algorithm(GA), FWA and PSO, the IPSO has stronger global search ability, better robustness and higher accuracy.