A novel particle swarm optimizer without velocity: Simplex-PSO

作者：Xiao Hong-feng; Tan Guan-zheng^*

来源：Journal of Central South University of Technology, 2010, 17(2): 349-356.

DOI：10.1007/s11771-010-0052-0

• Nelder-Mead simplex method
• particle swarm optimizer
• high-dimension function optimization
• convergence analysis

A simplex particle swarm optimization (simplex-PSO) derived from the Nelder-Mead simplex method was proposed to optimize the high dimensionality functions. In simplex-PSO, the velocity term was abandoned and its reference objectives were the best particle and the centroid of all particles except the best particle. The convergence theorems of linear time-varying discrete system proved that simplex-PSO is of consistent asymptotic convergence. In order to reduce the probability of trapping into a local optimal value, an extremum mutation was introduced into simplex-PSO and simplex-PSO-t (simplex-PSO with turbulence) was devised. Several experiments were carried out to verify the validity of simplex-PSO and simplex-PSO-t, and the experimental results confirmed the conclusions: (1) simplex-PSO-t can optimize high-dimension functions with 200-dimensionality; (2) compared PSO with chaos PSO (CPSO), the best optimum index increases by a factor of 1x10(2)-1x10(4).

• 出版日期2010-4
• 单位中南大学