Cluster analysis is an important exploratory data analysis technique which divides data into groups based on their similarity. Document clustering is the process of employing clustering algorithms on textual data so that text documents can be retrieved, organized, navigated and summarized in an efficient way. Document clustering can be utilized in the organization, summarization and classification of text documents. Metaheuristic algorithms have been successfully utilized to deal with complex optimization problems, including cluster analysis. In this paper, we analyze the clustering quality of five metaheuristic clustering algorithms (namely, particle swarm optimization, genetic algorithm, cuckoo search, firefly algorithm and yarasa algorithm) on fifteen text collections in term of F-measure. In the empirical analysis, two conventional clustering algorithms (K-means and bi-secting k-means) are also considered. The experimental analysis indicates that swarm-based clustering algorithms outperform conventional clustering algorithms on text document clustering.