A series of research achievements have been made in multi-task optimization(MTO) based on the implicit parallelism of swarm intelligence. However, the frequent vertical information transfer between tasks leads to excessive increase of population heterogeneity, resulting in the negative impact of information migration,which is also one of the problems that has not been completely solved in the field of MTO. Firstly, PSO and multi-population evolution information sharing mechanism are combined, then the idea of benchmarking management is introduced to realize multi-level information migration and intelligent emergence, finally the frequency of information transfer is effectively controlled by calculating the population diversity index, and the multi-level information transfer multi-task PSO(MITMPSO) is proposed. Experimental results show that the MITMPSO can significantly improve the solution quality and accelerate the convergence speed of multiple high-dimensional functions, multiple multi-constraints functions and multiple binary discrete optimization problems concurrently in polynomial time by setting a reasonable information migration threshold.