A Short Comparation between PSO and QPSO for Signal Processing Applications

The aim of the work is to make an evaluation of two versions of Particle Swarm Optimization (PSO), in the framework of signal analysis and model parameters estimations. The versions of PSO are with constraint coefficients and inertia (PSO-COIN) and the version based on quantum models (QPSO). Both versions are members of the evolutionary computation domain and have bioinspired roots. Two case studies are considered. The first case study is represented by two test functions, chosen from a benchmark suite used for the evaluation of the optimization algorithms. The second case is the parameter estimation problem, in the context of the signal analysis framework, and under various signal-to-noise ratios to study the effects on the converge rates of the estimation processes. The computer-based experiments show comparable performances for PSO algorithms, for short time simulation, e.g., 100 iterations. After that, the version of PSO-COIN gives better results.