# K-Means Clustering Algorithm Based on Chaotic Adaptive Artificial Bee Colony
## 5 Conclusions  
Modeling the behavior of honey bees to search and solve problems has been the emerging area of swarm intelligence. In this paper, an improved artificial bee colony algorithm (CAABC) is developed to solve clustering problems. The method is inspired by the forage behavior in nature. The CAABC-K-means algorithm can be adapted to the process that the number of clusters known as a priori. In the CAABC algorithm, for the purpose of stabilizing the disturbance caused by the variety of the initial value of the clustering algorithm, we adapt the max-min distance product method in the initialization stage of the algorithm, which weakened the randomness of the initialization process to some extent. In the iterative process, chaotic disturbance and adaptive adjustment are added to obtain a better performance. After the comparison, chaos disturbance was added to the elite solution and unsatisfactory solutions in a certain percentage. The introduce of the \"aggregate-disperse operation\" speeds up the convergence of the algorithm and provides favorable conditions for escaping from the local optimal. Furthermore, on this basis, the global optimal solution is cross-operated to retain dominant individuals and improve population diversity. Moreover, the simulated annealing criterion is integrated into the probability selection to achieve a better precision. By selecting the appropriate functions, the characteristics of ABC for group optimization are retained, and the local optimal solution can be avoided effectively. In addition, CAABC-K-means has the global search ability of CAABC, which reduces the number of iterations of K-means. The problem of poor global search ability of Kmeans algorithm is solved by the combination of two algorithms. Furthermore, according to the characteristics of the clustering algorithm, the impacts of the sample numbers and the distance between the sample centers are took into account in fitness selection, which reduces the possibility that the distribution of samples is excessive clustered. To evaluate the performance of the confluent algorithm, it is compared with other stochastic heuristic algorithms on several benchmark functions and real datasets. It can be concluded from the primary results of experience, which are very promising in terms of the accuracy of the solution found and the processing efficiency, that the CAABC-K-means clustering algorithm achieves better results.  
The efficiency and accuracy of the algorithm have been improved, but the time complexity cannot be reduced effectively because of the location update formula which is still guided by global optimal solution. How to ensure the advantages of the existing algorithm while reducing the time complexity will be our next research direction. Applying the proposed algorithm to solve other optimization problems and improving the performance of the clustering algorithm will be considered in our future work.  
All authors contributed extensively to the work presented in this paper. Conceptualization, Q.J. and N.L.; methodology, Q.J.; software, Q.J. and N.L.; validation, Q.J. and N.L.; formal analysis, N.L. and Y.Z.; investigation, Q.J., N.L. and Y.Z.; data curation, N.L.; writing--original draft preparation, N.L.; writing--review and editing, N.L. and Y.Z.; supervision, Q.J.; funding acquisition, Q.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant numbers 61673004 and the Fundamental Research Funds for the Central Universities of China (XK1802-4).  
Not applicable.  
Data Availability Statement: Data sharing is not applicable to this article.  
The authors declare no conflict of interest.