An Improved Algorithm of K-means Based on Evolutionary Computation

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. Keywords Evolutionary computation; jaya algorithm; K-means; local optimum; simulated annealing Cite This Article APA Style Wang, Y., Luo, X., Zhang, J., Zhao, Z., Zhang, J. (2020). An Improved Algorithm of K-means Based on Evolutionary Computation. Intelligent Automation & Soft Computing , 26 (5) , 961–971. https://doi.org/10.32604/iasc.2020.010128 Vancouver Style Wang Y, Luo X, Zhang J, Zhao Z, Zhang J. An Improved Algorithm of K-means Based on Evolutionary Computation. Intell Automat Soft Comput. 2020;26(5):961–971. https://doi.org/10.32604/iasc.2020.010128 IEEE Style Y. Wang, X. Luo, J. Zhang, Z. Zhao, and J. Zhang, “An Improved Algorithm of K-means Based on Evolutionary Computation,” Intell. Automat. Soft Comput. , vol. 26, no. 5, pp. 961–971, 2020. https://doi.org/10.32604/iasc.2020.010128 BibTex EndNote RIS Citations 1 [click to view]