Particle Swarm Optimization incorporating a Preferential Velocity-Updating Mechanism and Its Applications in IIR Filter Design

A particle swarm optimization (PSO) incorporating a preferential velocity-updating mechanism is proposed in this paper to improve the evolutionary performance. Based on the evolution experience from all particles in the swarm, the evolution of the proposed PSO is directed by imposing preference on different parts of the velocity-updating rule to all particles. The particles lying far away from the global best position are provided with more spontaneity to search, while ones close to the global best position are provided with better exploitation capability to search toward the direction of the global best position. Simulation results via the proposed approach to optimize 14 objective functions, estimating the reduced-order IIR have shown that satisfactory performance can be obtained over conventional PSO variants.