Control theory and optimization:
dynamical systems, distributed control, multi-agent systems, numerical optimization and swarm intelligence
Power systems, swarm robots, glucose insulin regulation
- Ph.D. in Mechanical Enginnering, Texas Tech University, 2014
- B.S. in Electrical Engineering, Sun Yat-Sen Univeristy, 2009
In this paper, the performance of the Particle Swarm Optimization (PSO) algorithm is studied from the system dynamics point of view. The dynamics of the particles in PSO algorithm are considered as second-order systems. Depending on the selections of the parameters, the second-order systems have over-damped, critically damped, underdamped or undamped responses. Different responses give the algorithm different types of performance. Therefore, in this paper, we derive the conditions for parameters in the PSO algorithm such that the particles have different responses. The exploration and exploitation of PSO is discussed numerically. Moreover, due to the fact the discrete model of PSO is converted from a continuous model by certain sampling ratio, the sampling ratio variable is introduced to the PSO algorithm.