Haopeng Zhang

Asst Professor

Research interests

Control theory and optimization:

dynamical systems, distributed control, multi-agent systems, numerical optimization and swarm intelligence

Applications:

Power systems, swarm robots, glucose insulin regulation

Education

  • Ph.D. in Mechanical Enginnering, Texas Tech University, 2014
  • B.S. in Electrical Engineering, Sun Yat-Sen Univeristy, 2009

Publications

A discrete-time switched linear model of the particle swarm optimization algorithm- 2020

In this paper, the convergence issue of the Particle Swarm Optimization (PSO) algorithm is investigated. Most of the models of PSO algorithms are time-invariant linear models with the assumption the local and global best solutions do not change, i.e., the stagnation assumption. However, in this paper, a discrete-time switched linear model is introduced to study the stability and convergence of the PSO algorithm without the stagnation assumption. By considering the updates of local best positions and global best solutions, a sequence of state transform matrixes is generated during the searching process. The semistability of the proposed switched linear system is studied. The conditions of the convergence in mean and convergence in probability are derived by using the recently developed results in paracontraction. Moreover, numerical examples are provided to verify the results proposed in this paper.

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