Abstract：An improved particle swarm optimization algorithm for vector quantization is proposed. The Concept of comprehensive learning in comprehensive learning particle swarm optimization (CLPSO) is adopted and merged into the learning strategies of original particle swarm optimization (PSO). The mapping between a particle and its example particle is built. And the particle can learn from the mapped dimensions in the example particle instead of the corresponding dimensions. Thus, the local search ability is greatly enhanced as well as the diversity of the swarm is effectively maintained. The experimental results show that the algorithm can effectively alleviate the problem of premature convergence and obtain good reconstruction image quality.
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2008, Vol. 21 
