Query expansion is an approach, which plays a critical role on the web-based search and retrieval methods. It’s primary objective is to expedite the performance of the retrieval process in retrieving information by merging alternate word forms in the original query. The related word forms are retrievable from the document available in databases, which stores documents with a high density. A typical method for query expansion involves clustering of related documents and extracting closely related expressions using these clusters. However, traditional clustering poses a few problems to query expansion. Biclustering appears to be more effective in query expansion than traditional clustering. Recently a nature-inspired algorithm, namely the stellar mass black hole optimization has been introduced, to carry out the process of biclustering. Modification of the SBO has been performed to propose MSBO. Preliminary results have shown that the proposed modified stellar mass black hole (MSBO) method effectively clusters identical expressions and consequently the detected biclustering algorithms were effective in generating suitable words for expanding a given query. The MSBO has been tested on particular sets of documents with the objective of identifying groups of relevant categories of text that form the biclusters. Thus the proposed MSBO proves to be an effective query expansion tool. Based on the analysis of the obtained results, and on the comparisons performed with the existing methods, it has been found that MSBO is effective in generating results that are encouraging. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.