An enhanced grey wolf optimizer-based Facebook artificial intelligence similarity search for Alzheimer's disease diagnosis.

Early diagnosis of Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI), requires efficient computational frameworks capable of handling high-dimensional and large-scale 18F-FDG-PET data. To address challenges in feature redundancy and computational efficiency, we propose a unified framework integrating an enhanced Grey Wolf Optimizer (CDL-GWO) with approximate nearest neighbor search based on Facebook AI Similarity Search (FAISS). The proposed CDL-GWO incorporates Logistic chaotic initialization to improve search space coverage, dual random projection to enhance population diversity via inter-solution differences, and Lévy flight to escape local optima, achieving a better balance between exploration and exploitation. Experimental results on the CEC2017 benchmark demonstrate that CDL-GWO outperforms several classical and state-of-the-art metaheuristic algorithms. Combined with FAISS, the framework enables efficient similarity search for large datasets. Validation on 890 subjects from the ADNI 18F-FDG-PET dataset across six AD-related classification tasks shows that the proposed method achieves competitive diagnostic performance (up to 0.943 accuracy) while reducing computational cost by approximately 70% compared to conventional KNN. These results indicate that the proposed framework provides an effective and scalable solution for early AD diagnosis and MCI classification.