Comparing nonlinear inertia weights and constriction factors in particle swarm optimization

In this paper, we empirically study the performance of particle swarm optimization (PSO) using a nonlinear inertia weight compared with the performance using a constriction factor. Adjusting the parameters of nonlinear inertia weight directly affects the performance of PSO. Five benchmark functions are used for evaluation. Under all testing functions, the PSO with nonlinear inertia weights consistently converges faster than the PSO with constriction factors.

[1]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[2]  T. Ross.  Fuzzy Logic with Engineering Applications , 1994 .

[3]  Werasak Kurutach,et al.  Modified Particle Swarm Optimization for an Optimal Feeder-Switch Relocation , 2008, Int. J. Artif. Intell. Tools.

[4]  R. Eberhart,et al.  Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[5]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[6]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

[7]  M. Clerc,et al.  The swarm and the queen: towards a deterministic and adaptive particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[8]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).