一种基于禁忌搜索的全局最优化模糊聚类算法

模糊C均值（FCM）算法是一种基于贪心思想的迭代算法，算法沿迭代序列收敛到一个极小值，但存在搜索能力弱、易陷入局部最优的缺点.本文提出了一种基于禁忌搜索的模糊聚类算法，该算法在一个解的邻域内使用禁忌搜索，并采用了基于FCM局部收敛性质的长期表禁忌策略，保证在不断移动搜索起点的同时避免重复搜索；其次使用混沌优化思想与动态步长策略来提升算法的全局搜索能力，以达到获取全局最优解的目的.实验结果表明，改进算法极大地提高了聚类准确率，并具有良好的稳定性，与群智算法和遗传算法的优化相比也具有一定的优势.

A Global Optimization Fuzzy Clustering Algorithm Based on Tabu Search

The fuzzy c-Means algorithm is a kind of iterative algorithms based on greedy algorithms.It converges to a local minimum value along the iteration sequence,yet it has the insufficient searching ability and can easily fall into local optimum solution.This paper,based on tabu search,introduces a fuzzy clustering algorithm.It uses tabu search in a solution's neighborhood and adopts the tabu strategy of long-term tabu lists based on the local convergence of FCM,which guarantees to move the search starting point constantly and avoids repeated searching.In addition,chaos optimization and dynamic step strategies are utilized to strengthen its global search ability in order to achieve global optimal solution.Experimental results show that this algorithm improves the accuracy of clustering considerably and has great stability.Compared with group-wise algorithm and genetic algorithm,this algorithm also has some advantages.