Abstract

Document clustering has become an essential component of information retrieval text mining and large-scale data management systems. As digital repositories continue to expand rapidly traditional clustering algorithms often struggle with scalability convergence speed and optimization precision. To address these limitations hybrid metaheuristic approaches combining evolutionary computation and swarm intelligence have gained widespread attention. This research explores the design and optimization of a Hybrid Genetic–Particle Swarm Model (HG-PSM) for document clustering where the structural exploration capacity of Genetic Algorithms (GA) is synergistically integrated with the fast convergence and adaptive velocity mechanisms of Particle Swarm Optimization (PSO). The paper investigates how this hybrid model enhances cluster formation reduces intra-cluster variance improves global search capability and avoids premature convergence. This abstract outlines the scope of this research emphasizin