## 5 Conclusions  
In this study, hybridization of the K-means clustering algorithm with different MOAs has been presented. The primary objective of each hybridization was considered with the role of corresponding MOA and K-means in the resultant hybridized algorithm. The various dataset used for testing as well as the criteria used for performance evaluation were similarly extracted. The various existing MOA and hybrids used for comparison purposes for judging the performance of the hybridized algorithm were also presented. The publication rate of research on K-means hybridization with some MOA has also been presented as well as the normalized rate of the publications. The critical analysis of the findings from the study revealed the normalized publication rate of the different extracted articles on integrating K-means with MOAs. Five research questions were designed, and the corresponding answers were provided in this extensive literature analysis of the different hybridization methods incorporating the K-means clustering algorithm with MOA. From the response to the first research question, twenty-nine metaheuristics optimization algorithm, most of which are nature-inspired, were considered with a hundred and forty-seven articles reviewed that reports the various hybridization with K-means clustering algorithm or any of its variants. In the provided answers to the second research question, the various articles whose primary objective was to solve the problem of automatic clustering were identified among the reviewed articles. These articles were relatively small compared with the total number of articles selected for the study. Various areas of application where these hybridized algorithms have been deployed are also listed. The reviewed hybridized algorithm's various approaches to automatic clustering were discussed in response to the third research question. The response to the fifth question presented a thorough analysis of the publication trend with reference to K-means hybridization with MOA in the last two decades. A bifurcation presentation of the reviewed algorithms reveals that there is a generally low rate in research publication involving the hybridization of K-means with MOA in most of the reviewed literature. This indicates a great need for more attention in this area of research, most especially for handling automatic clustering problems. This was further verified by the graphical report obtained from the normalization of the publication rate. Finally, the study further reveals that the existing hybridized K-means algorithms with MOAs still require higher execution time when applied to the clustering of a big dataset to obtain higher quality clustering results. Conceptualization, A.E.E. ; methodology, A.M.I. and A.E.E. ; software, A.E.E. ; investigation, A.M.I. ; resources, A.E.E. ; data curation, A.M.I. ; writing--original draft preparation, A.M.I. and A.E.E. ; writing--review and editing, A.M.I., A.E.E. and M.S.A. ; supervision, A.E.E. ; project administration, A.E.E. and M.S.A. ; funding acquisition, M.S.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest regarding the publication of this manuscript.