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An Improved Algorithm of K-means Based on Evolutionary Computation

Yunlong Wang1,2,3, Xiong Luo1,2,4,*, Jing Zhang1,2,3, Zhigang Zhao1, Jun Zhang5

1 School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China
2 Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, 100083, China
3 Key Laboratory of Wind Energy and Solar Energy Technology (Inner Mongolia University of Technology), Ministry of Education, Hohhot, 010051, China
4 Shunde Graduate School, University of Science and Technology Beijing, Foshan, 528399, China
5 Science and Technology Division, North China Institute of Science and Technology, Beijing, 101601, China

* Corresponding Author: Xiong Luo. Email:

Intelligent Automation & Soft Computing 2020, 26(5), 961-971.


K-means is a simple and commonly used algorithm, which is widely applied in many fields due to its fast convergence and distinctive performance. In this paper, a novel algorithm is proposed to help K-means jump out of a local optimum on the basis of several ideas from evolutionary computation, through the use of random and evolutionary processes. The experimental results show that the proposed algorithm is capable of improving the accuracy of K-means and decreasing the SSE of K-means, which indicates that the proposed algorithm can prevent K-means from falling into the local optimum to some extent.


Cite This Article

Y. Wang, X. Luo, J. Zhang, Z. Zhao and J. Zhang, "An improved algorithm of k-means based on evolutionary computation," Intelligent Automation & Soft Computing, vol. 26, no.5, pp. 961–971, 2020.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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