Study of On-Ramp PI Controller Based on Dural Group QPSO with Different Well Centers Algorithm

摘要

A novel quantum-behaved particle swarm optimization (QPSO) algorithm, dual-group QPSO with different well centers (DWC-QPSO) algorithm, is proposed by constructing the master-slave subswarms. The new algorithm was applied in the parameter optimization of on-ramp traffic PI controller combining with nonlinear feedback theory. With the critical information contained in the searching space and results of the basic QPSO algorithm, this algorithm avoids the rapid disappearance of swarm diversity and enhances the global searching ability through collaboration between subswarms. Experiment results on an on-ramp traffic control simulation show that DWC-QPSO can be well applied in the study of on-ramp traffic PI controller and the comparison results illustrate that DWC-QPSO outperforms other evolutionary algorithms with enhancement in both adaptability and stability.