Empirical Study of the Inertia Weight Particle Swarm Optimization with Constraint Factor

For improving the performance of the Particle Swarm Optimization (PSO), two major strategies are used, one is the parameter modifying method, and the other is the population diversity method. For these two methods, the first one obtains the balance between the local search ability and the global search ability of the PSO by using the parameter adjusting and the parameter adding or parameter reducing, in that it has less effect on the algorithm complexity and has attracted a great of attentions. One of the well-known improved PSO algorithms of the parameter modifying method is inertia weight PSO, by introducing the inertia weight, the performance of the original PSO is improved greatly. Experimentally, we find that the performance of the algorithm can be improved more when adding a constraint factor to the inertia weight. In this paper, we empirically study the effects of the constraint factor on the performance of the inertia weight PSO. Based on the experimental results, we obtain the optimal selection of the constraint factor and extend the ability of the inertia weight PSO.