A time-efficient particle swarm optimization-based codebook generation algorithm

By and large, population-based metaheuristics provide a better solution to the codebook generation problem of vector quantization (VQ) than Generalized Lloyd Algorithm (GLA) and single-solution-based metaheuristics. They are, however, all much slower. In this paper, we present an efficient method to speed up the performance of particle swarm optimization (PSO), called Fuzzy Pattern Reduction Enhanced Particle Swarm Optimization (FPREPSO). The proposed method first uses PSO to search for the global solutions. Then, it relies on pattern reduction to eliminate computations that are essentially redundant in the convergence process of PSO. And finally, it employs a set of fuzzy inference rules to decrease the chance of eliminating patterns that should not be eliminated. To evaluate the performance of the proposed algorithm, we compare it with GLA and GLA-based algorithms such as standard GLA, pattern reduction enhanced GLA, genetic k-means algorithm (GKA), and PSO. Our simulation results show that the proposed algorithm can cut the computation time down by 51.07% up to 81.77% compared to PSO and GKA.