Grey Wolf Optimizer, Ant Colony Optimization, and Quantum-Based Optimization for Document Retrieval: A Comparative Analysis with FLICM and FDMO

The relevancy and accuracy of document retrieval systems are essential for information retrieval tasks, particularly in large-scale and high-dimensional datasets. This study explores the integration of Grey Wolf Optimizer (GWO), Ant Colony Optimization (ACO), and Quantum-based optimization into a Fuzzy Local Information C-Means (FLICM) clustering framework for enhancing deep learning-based document retrieval systems. By correlating with recent advancements in Fractional Dwarf Mongoose Optimization (FDMO) for document retrieval, we investigate how these alternative optimizers affect system performance in terms of precision, recall, and computational efficiency.