Harsha, S.S.Muddi, K.S.Jindrali, S.S.Reji, S.Das, M.Mohan, B.R.2026-02-0620242024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024, 2024, Vol., , p. -https://doi.org/10.1109/IATMSI60426.2024.10503082https://idr.nitk.ac.in/handle/123456789/29074This paper explores a hybrid-optimization approach for reducing the expected loss of delivery in drone delivery.This paper aims to give a deep knowledge about drone scheduling using machine learning and bio-optimized approaches. Using hybridization of K-Mean Clustering algorithms and Genetic algorithms, the paper makes a comparison between the performance of the above algorithm with the hybridization of hierarchical agglomerative clustering algorithms and ant colony optimization algorithms, resulting in valuable insights into drone delivery efficiency and reliability. © 2024 IEEE.Ant Colony OptimizationBio-Optimized ApproachDroneExpected Loss Of DemandGenetic AlgorithmHierarchical Agglomerative ClusteringK-means ClusteringPayloadProbability Of Failure