改进混合蛙跳算法和K-Means的新型聚类算法

研究针对现有聚类算法存在着精度较低,易陷于局部最优等问题,提出一种改进的混合蛙跳算法和K-Means相结合的新型聚类算法ISFLA-K,该算法使用对立学习的思想产生初始种群,根据蛙自身具有认知能力和学习能力的特性对混合蛙跳算法的蛙跳规则进行改进,即形成ISFLA,最后使用ISFLA优化K-Means聚类算法,提高求解精度。实验结果表明, ISFLA-K具有很好的聚类性能,求解精度高。

New Clustering Algorithm Based on Improved Shuffled Frog Leaping Algorithm and K-Means Algorithm

Existing clustering algorithms have the problems of low precision and easy to fall into local optimum. The paper proposes a new algorithm-ISFLA-K, which combined with an improved shuffled frog leaping algorithm and K-Means clustering algorithm. The algorithm uses the idea of an independent study to generate the initial population. According to the frogs’ characteristics of cognitive and learning ability, it improve the rules of shuffled frog leaping algorithm leapfrog. The paper uses ISFLA to optimize K-Means clustering algorithm, which improved solution accuracy. The experimental results can prove the validity and superiority of the proposed algorithm.