In this paper, the performance of dynamic multi-swarm particle swarm optimizer (DMS-PSO) on the set of benchmark functions provided for the CEC2008 Special Session on Large Scale optimization is reported. Different from the existing multi-swarm PSOs and local versions of PSO, the sub-swarms are dynamic and the sub-swarmspsila size is very small. The whole population is divided into a large number sub-swarms, these sub-swarms are regrouped frequently by using various regrouping schedules and information is exchanged among the particles in the whole swarm. The Quasi-Newton method is combined to improve its local searching ability.
['Shi-Zheng Zhao', 'Jing J. Liang', 'Ponnuthurai N. Suganthan', 'Mehmet Fatih Tasgetiren']
Dynamic multi-swarm particle swarm optimizer with local search for Large Scale Global Optimization