Title: SmartSort: An Intelligent Framework for Optimizing Sorting Efficiency Using AI
Authors: Nesreen S. Aljerjawi and Samy S. Abu-Naser
Volume: 9
Issue: 8
Pages: 134-138
Publication Date: 2025/08/28
Abstract:
Traditional sorting algorithms, while fundamental to computer science, often operate with fixed time and space complexities that are heavily dependent on the characteristics of the input data. This presents a significant challenge: no single algorithm is universally optimal for all data types, sizes, and initial conditions. This study introduces SmartSort, a conceptual intelligent framework designed to overcome this limitation. SmartSort employs a rule-based expert system to dynamically select the most efficient sorting algorithm for a given dataset. The framework's core components include a knowledge base, which contains rules about algorithm performance metrics and data characteristics, and an inference engine that uses these rules to reason and make an informed decision. By analyzing real-time data features such as size, sortedness, and value distribution, SmartSort can choose from a suite of algorithms (e.g., QuickSort, MergeSort, InsertionSort, TimSort) to achieve superior performance. This paper outlines the system's design, presents a hypothetical methodology, and discusses the potential benefits and challenges. Preliminary analysis suggests that such an intelligent approach could significantly enhance sorting efficiency, reduce computational overhead, and provide a more robust solution for a wide range of applications.