Aiming at the problems that Symbiotic Organisms Search （SOS） algorithm only can solve single tasks and negative information transfer affects Multi-Task Optimization （MTO） performance， an Information Transfer Multi-Task SOS （ITMTSOS） algorithm was proposed. Firstly， based on multi-population evolution framework MTO， multiple populations were set according to the number of tasks. Secondly， each population ran basic SOS algorithm independently， and by introducing individual itself optimal experience and neighborhood optimal individuals， the knowledge module containing the above two was formed and transferred to the process of individual evolution when a population stagnated for several consecutive generations. Finally， the time and space complexity of ITMTSOS was analyzed. Simulation results show that ITMTSOS converges rapidly to the global optimal solution 0 when resolving a batch of different shape high-dimensional functions， and the average running time is reduced around 25.25% when compared with single task SOS； when solving the multi-dimensional 0/1 knapsack problems and the teacher-student matching problems concurrently， the optimal fitnesses on weing1 and weing7 test sets are increased by 22 767 and 22 602 respectively compared with the current published optimal results， the absolute values of the optimal and the average matching difference of teacher-student matching problem are decreased by 26 and 33 respectively， and the average running time is reduced around 7.69%.

The Self-Organized Migrating Algorithm (SOMA) only can solve the single task, and the "implicit parallelism" of SOMA is not fully exploited. Aiming at the shortcomings, a new Self-Organized Migrating Algorithm for Multi-task optimization with Information Filtering (SOMAMIF) was proposed to solve multiple tasks concurrently. Firstly, the multi-task uniform search space was constructed, and the subpopulations were set according to the number of tasks. Secondly, the current optimal fitness of each subpopulation was judged, and the information transfer need was generated when the evolution of a task stagnated in a successive generations. Thirdly, the useful information was chosen from the remaining subpopulations and the useless information was filtered according to a probability, so as to ensure the positive transfer and readjust the population structure at the same time. Finally, the time complexity and space complexity of SOMAMIF were analyzed. Experimental results show that, SOMAMIF converges rapidly to the global optimal solution 0 when solving multiple high-dimensional function problems simultaneously; compared with those of the original datasets, the average classification accuracies obtained on two datasets by SOMAMIF combing with the fractal technology to extract the key home returning constraints from college students with different census register increase by 0.348 66 percentage points and 0.598 57 percentage points respectively.