Abasi, Ammar KamalKhader, Ahamad TajudinAl-Betar, Mohammed AzmiNaim, SyibrahAwadallah, Mohammed A.Alomari, Osama Ahmad2024-09-112024-09-1120202088-8708https://doi.org/10.11591/IJECE.V10I6.PP6361-6369https://hdl.handle.net/11363/8426In this study, a multi-verse optimizer (MVO) is utilised for the text document clustering (TDC) problem. TDC is treated as a discrete optimization problem, and an objective function based on the Euclidean distance is applied as similarity measure. TDC is tackled by the division of the documents into clusters; documents belonging to the same cluster are similar, whereas those belonging to different clusters are dissimilar. MVO, which is a recent metaheuristic optimization algorithm established for continuous optimization problems, can intelligently navigate different areas in the search space and search deeply in each area using a particular learning mechanism. The proposed algorithm is called MVOTDC, and it adopts the convergence behaviour of MVO operators to deal with discrete, rather than continuous, optimization problems. For evaluating MVOTDC, a comprehensive comparative study is conducted on six text document datasets with various numbers of documents and clusters. The quality of the final results is assessed using precision, recall, F-measure, entropy accuracy, and purity measures. Experimental results reveal that the proposed method performs competitively in comparison with state-of-the-art algorithms. Statistical analysis is also conducted and shows that MVOTDC can produce significant results in comparison with three well-established methods. Copyright c 2020 Insitute of Advanced Engineeering and Science. All rights reserved.eninfo:eu-repo/semantics/openAccessMulti-verse optimizer; Optimization; Swarm intelligence; Test document clusteringArticle1066361636910.11591/IJECE.V10I6.PP6361-63692-s2.0-85092309466Q3