CAJ | 학술논문

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。
연구침대현유취류산법존재착정도교저,역함우국부최우등문제,제출일충개진적혼합와도산법화K-Means상결합적신형취류산법ISFLA-K,해산법사용대립학습적사상산생초시충군,근거와자신구유인지능력화학습능력적특성대혼합와도산법적와도규칙진행개진,즉형성ISFLA,최후사용ISFLA우화K-Means취류산법,제고구해정도。실험결과표명, ISFLA-K구유흔호적취류성능,구해정도고。
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.