The exponential growth of unstructured text data generated by internet users has created an urgent need for efficient organization methods to uncover valuable insights. Text clustering, a widely used data mining approach, often relies on single-objective optimization, which can struggle to deliver optimal results for datasets with diverse clustering criteria. To address these challenges, we propose the Multi-objective Firefly Differential Jaya (MFDJ) algorithm, a novel nature-inspired optimization method designed to enhance text clustering. MFDJ integrates the strengths of NSGA-II, a well-established multi-objective optimization framework, with three complementary algorithms: the Firefly algorithm for swarm intelligence-based optimization, Differential Evolution for robust exploration through mutation, and the Jaya algorithm for parameter-free improvement leveraging both the best and worst solutions. This synergy significantly enhances the algorithm’s ability to balance exploration and exploitation, yielding superior clustering performance. We evaluated MFDJ on eight benchmark text datasets, where it demonstrated consistent superiority over state-of-the-art methods, including NSGA-II and MOMDE. On average, MFDJ achieved a 67.89% improvement in F-measure over NSGA-II and a 5.87% improvement over MOMDE, while also exhibiting better convergence properties for the majority of datasets. These results underscore the capability of MFDJ to generate high-quality clusters, making it a versatile tool for tackling complex text clustering and broader optimization challenges.
