模糊学习矢量量化算法（FLVQ）虽然解决了硬的竞争学习对初始码本的依赖性问题，但收敛速度变慢，且仍无法克服陷入局部最小。为此在分析模糊学习矢量量化图象编码原理的基础上，探讨了FLVQ算法的几种优化途径，进而进出了一种基于Tabu搜索（TS）的模糊学习矢量量化的新算法（TS－FLVQ），并给出了该算法的具体实现方法及步骤。该算法首先利用TS技术产生一个面向全局搜索的寻优列表，然后再进行模糊学习以得到最优解，实验结果表明，该算法在收敛速度及编码效果上均较FLVQ有较大的提高。。

Fuzzy learning vector quantization (FLVQ) algorithm outperforms the hard-competitive vector quantization in that it reduces the dependence of the resulting codebook on the initial codebook selection, yet it has the disadvantages of slow convergence and easy to be trapped in local minima. In this paper, the principle of fuzzy learning vector quantization for image coding is reviewed. Followed by a discussion of the possible ways for optimizing the FLVQ algorithm, a new fuzzy learning vector quantization algorithm based on tabu search(TS-FLVQ) is then proposed. In this algorithm, we firstly constructed a table listing oriented to global search by the tabu search algorithm, and afterwards took advantage of fuzzy learning to reach the global minimum point of the predefined objective function. The algorithm with a detailed description of the procedure involved was simulated in the computer finally. The algorithm differs from a standard greedy search in that the best move is executed also if it leads to a configuration with a greater energy than the current one; this is necessary to be able to escape from local minima. Experimental results show that TS-FLVQ has much better coding performance over FLVQ with remarkably faster convergence.