A fast particle swarm optimization algorithm for vector quantization

Chun Wei Tsai, Chung Fu Lin, Ming Chao Chiang, Chu-Sing Yang

研究成果: Article同行評審

2 引文 斯高帕斯(Scopus)


As a new promising population-based metaheuristic algorithm, the particle swarm optimization (PSO) provides a better solution to the codebook generation problem of vector quantization than Generalized Lloyd Algorithm (GLA) and single-solution-based metaheuristics. Unfortunately, PSO is computationally much more expensive than GLA and single-solution-based metaheuristics. In this paper, we present an efficient method to reduce the computation time of PSO. 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. Our simulation results show that, with a small loss of quality, the proposed algorithm can significantly speed up not only the population-based but also the single-solution-based metaheuristics. ICIC International

頁(從 - 到)137-143
期刊ICIC Express Letters, Part B: Applications
出版狀態Published - 2010 十二月 1

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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