Three Sub-Swarm Particle Swarm Optimization Algorithm

In order to overcome the drawback of the standard PSO, such as being subject to falling into local optimization, an improved PSO algorithm based on three sub-swarms exchange is proposed. Firstly the method divides the whole swarm into three sub-swarms which evolve jointly according to three different models, that is, one evolves with the standard PSO model, and the second with social only model and the third with cognition only model respectively. When the community evolution achieves the equilibrium state, we exchange some particles between the three different sub-swarms, which can increase the information exchange between the sub-swarms, improve the population diversity and reduce the possibility of getting local extreme value. The results of simulation show that the proposed algorithm in the paper has the better optimization performance than the standard PSO.