A simple water cycle algorithm with percolation operator for clustering analysis

  • Shilei Qiao
  • Yongquan Zhou
  • Yuxiang Zhou
  • Rui Wang
Methodologies and Application


The clustering problem consists in the discovery of interesting groups in a data set. Such task is very important and widely tacked in the literature. The K-means algorithm is one of the most popular techniques in clustering. However, the performance of the K-means algorithm depends highly on initial cluster centers and converges to local minima. This paper proposed a simple water cycle algorithm (WCA) with percolation operator for clustering analysis. The simple WCA discards the process of rainfall. The evolutionary process is only controlled by the process of flowing and percolation operator. The process of flowing can be thoroughly search the solution space; on the other hand, the percolation operator can find the solution more accuracy and represents the local search. Ten data sets are selected to evaluate the performance of proposed algorithm; the experiment results show that the proposed algorithm performs significantly better in terms of the quality, speed and stability of the final solutions.


Simple water cycle algorithm Percolation operator Clustering analysis Soft computing 



This work is supported by National Science Foundation of China under Grants No. 61463007.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Shilei Qiao
    • 1
  • Yongquan Zhou
    • 1
    • 2
  • Yuxiang Zhou
    • 1
  • Rui Wang
    • 1
  1. 1.College of Information Science and EngineeringGuangxi University for NationalitiesNanningChina
  2. 2.Key Laboratory of Guangxi High Schools Complex System and Computational IntelligenceNanningChina

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