{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T17:19:59Z","timestamp":1787505599082,"version":"build-2736575974"},"reference-count":110,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,12,18]],"date-time":"2020-12-18T00:00:00Z","timestamp":1608249600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Text clustering is one of the efficient unsupervised learning techniques used to partition a huge number of text documents into a subset of clusters. In which, each cluster contains similar documents and the clusters contain dissimilar text documents. Nature-inspired optimization algorithms have been successfully used to solve various optimization problems, including text document clustering problems. In this paper, a comprehensive review is presented to show the most related nature-inspired algorithms that have been used in solving the text clustering problem. Moreover, comprehensive experiments are conducted and analyzed to show the performance of the common well-know nature-inspired optimization algorithms in solving the text document clustering problems including Harmony Search (HS) Algorithm, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) Algorithm, Ant Colony Optimization (ACO), Krill Herd Algorithm (KHA), Cuckoo Search (CS) Algorithm, Gray Wolf Optimizer (GWO), and Bat-inspired Algorithm (BA). Seven text benchmark datasets are used to validate the performance of the tested algorithms. The results showed that the performance of the well-known nurture-inspired optimization algorithms almost the same with slight differences. For improvement purposes, new modified versions of the tested algorithms can be proposed and tested to tackle the text clustering problems.<\/jats:p>","DOI":"10.3390\/a13120345","type":"journal-article","created":{"date-parts":[[2020,12,18]],"date-time":"2020-12-18T11:42:09Z","timestamp":1608291729000},"page":"345","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":78,"title":["Nature-Inspired Optimization Algorithms for Text Document Clustering\u2014A Comprehensive Analysis"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2203-4549","authenticated-orcid":false,"given":"Laith","family":"Abualigah","sequence":"first","affiliation":[{"name":"Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2798-0104","authenticated-orcid":false,"given":"Amir H.","family":"Gandomi","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7682-6269","authenticated-orcid":false,"given":"Mohamed Abd","family":"Elaziz","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5394-0678","authenticated-orcid":false,"given":"Abdelazim G.","family":"Hussien","sequence":"additional","affiliation":[{"name":"Faculty of Science, Fayoum University, Faiyum 63514, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1850-3269","authenticated-orcid":false,"given":"Ahmad M.","family":"Khasawneh","sequence":"additional","affiliation":[{"name":"Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3864-7323","authenticated-orcid":false,"given":"Mohammad","family":"Alshinwan","sequence":"additional","affiliation":[{"name":"Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8127-7233","authenticated-orcid":false,"given":"Essam H.","family":"Houssein","sequence":"additional","affiliation":[{"name":"Faculty of Computers and Information, Minia University, Minia 61519, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.proeng.2012.01.839","article-title":"Performance evaluation of semantic based and ontology based text document clustering techniques","volume":"30","author":"Punitha","year":"2012","journal-title":"Procedia Eng."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lu, Q., Conrad, J.G., Al-Kofahi, K., and Keenan, W. 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