The performance of particles' social-learning ability directly affects the search capability of PSO.To overcome some shortcomings caused by mono-social-learning model

such as premature convergence and slow convergence speed at later evolution stage

a multi-population based self-adaptive migration PSO (MSMPSO) is proposed.In MSMPSO

the two common neighbor typologies are integrated into particle's social-learning part aiming to give more information source for the particles.Furthermore

the entire population is divided into three sub-populations which are evolved in parallel.Based on the multi-population mechanism

different search characteristics caused by three different combinations of acceleration coefficients are assigned to the three sub-populations.To take advantage of different merits of different sub-populations

and realize the reasonable allocation of computing resources

individuals carry out a migration operator based on subpopulations' historical performance during the last period.Simulation results based on CEC2013 test suite manifest that the favorable comprehensive performance of MSMPSO

in terms of convergence speed and solutions accuracy.