Comparing inertia weights and constriction factors in particle swarm optimization
The performance of particle swarm optimization using an inertia weight is compared with performance using a constriction factor. Five benchmark functions are used for the comparison. It is concluded that the best approach is to use the constriction factor while limiting the maximum velocity Vmax to the dynamic range of the variable Xmax on each dimension. This approach provides performance on the benchmark functions superior to any other published results known by the authors.
 R. W. Dobbins,et al. Computational intelligence PC tools , 1996 .
 Russell C. Eberhart,et al. Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.
 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).