Title: Enhancing Sorting Algorithms Using Artificial Intelligence: A Hybrid Approach.
Authors: Ihab M Aslim Samy S. Abu-Naser
Volume: 10
Issue: 7
Pages: 37-44
Publication Date: 2026/07/28
Abstract:
: Sorting is a fundamental operation in computer science, serving as the backbone for data organization, retrieval, and analysis across countless applications. While classical algorithms like QuickSort and MergeSort have been the standard for decades, the exponential growth of data volume and complexity has exposed their limitations in scalability and adaptability. This paper explores the integration of Artificial Intelligence (AI) into traditional sorting algorithms to create hybrid systems that overcome these challenges. By leveraging machine learning, reinforcement learning, and deep learning techniques, AI-driven sorting can dynamically adapt to data patterns, optimize performance, and handle heterogeneous datasets. This research examines various approaches, from neural network-guided sorting to reinforcement learning-based optimization, and presents a comprehensive review of current methodologies and their applications. The findings demonstrate that AI-enhanced sorting algorithms can achieve significant improvements in speed, accuracy, and scalability, with some hybrid models showing up to 40% reduction in processing time. The paper concludes by discussing the implications of these advancements for big data applications and identifying future research directions.