Application of Modified Particle Swarm Optimization Algorithm in FIR Filter Design

In this work we present an application of Particle Swarm Optimization (PSO) algorithm as a support in the design of Finite Impulse Response (FIR) filters. The conventional PSO algorithm was not sufficient to obtain desired filter parameters for filters longer than 20 coefficients. For this reason, we used an adaptive PSO algorithm that allows for adjustment of several key parameters during the optimization process of the swarm. In comparison to existing adaptive algorithms, in which for example only the inertia coefficient was subject to change, in our approach the possibility of changing several parameters simultaneously has been introduced. In our approach we additionally modify the social and cognitive coefficients in parallel. As a result, it was possible to obtain satisfactory results for FIR filters of lengths exceeding 50. In this work, we focused in particular on examining the effect of the swarm population size on the algorithm convergence. It turned out that for FIR filters of lengths around 40-50, satisfactory results are obtained with the number of particles in the swarm at the level of 100-150.