Abstract:A series of research achievements have been made in multi-task optimization(MTO) which based on the implicit parallelism of swarm intelligence. However, the frequent vertical information transfer between tasks led to excessive increase of population heterogeneity, resulting in the negative impact of information migration,which was 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 was combined, then the idea of benchmarking management was introduced to realize multi-level information migration and intelligent emergence, finally the frequency of information transfer was effectively controlled by calculating the population diversity index, and the multi-level information transfer multi-task optimization PSO (MLITMTPSO) was proposed. Experimental results show that MLITMTPSO 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.