Searching for the Best Matching Atoms Based on Multi-swarm Co-operative PSO

Abstract

Sparse signal decomposition can get sparse representation of signal. Given that the sparse decomposition has a large number of calculations and is almost impossible to meet the request of real time. A novel multi-swarm co-operative particle swarm optimization (PSO) algorithm to implement matching pursuit was developed, where multi-swarm was adopted to maintain the diversity of population, and the exploration ability of particle swarm optimization was elegantly combined with the exploitation of extremal optimization (EO) to prevent premature convergence. This method could reduce very time-consuming inner product times and improve decomposition accuracy in signal sparse decomposition, thereby, balancing very well search efficiency of time-frequency atoms and computer memory for storing the over-complete dictionary. The results of experiments indicated that the proposed algorithm can effectively speed up the convergence and lead to a preferable solution.