Clustering Using Improved Cuckoo Search Algorithm

被引：0
|
作者：
Zhao, Jie [1 ]
Lei, Xiujuan [1 ,2 ]
Wu, Zhenqiang [1 ]
Tan, Ying [2 ]
机构：
[1] Shaanxi Normal Univ, Sch Comp Sci, Xian 710062, Peoples R China
[2] Peking Univ, Sch Elect Engn & Comp Sci, Beijing 100871, Peoples R China
来源：
ADVANCES IN SWARM INTELLIGENCE, PT1 | 2014年 / 8794卷
基金：
中国国家自然科学基金;
关键词：
Clustering; cuckoo search; Levy flights; swarm intelligence optimization algorithm;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Cuckoo search (CS) is one of the new swarm intelligence optimization algorithms inspired by the obligate brood parasitic behavior of cuckoo, which used the idea of Levy flights. But the convergence and stability of the algorithm is not ideal due to the heavy-tail property of Levy flights. Therefore an improved cuckoo search (ICS) algorithm for clustering is proposed, in which the movement and randomization of the cuckoo is modified. The simulation results of ICS clustering method on UCI benchmark data sets compared with other different clustering algorithms show that the new algorithm is feasible and efficient in data clustering, and the stability and convergence speed both get improved obviously.
引用
收藏
页码：479 / 488
页数：10
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