Adaptive Hybrid Sort: Dynamic Strategy Selection for Optimal Sorting Across Diverse Data Distributions

Sorting is an essential operation in computer science with direct consequences on the performance of large scale data systems, real-time systems, and embedded computation. However, no sorting algorithm is optimal under all distributions of data. The new adaptive hybrid sorting paradigm proposed in this paper is the paradigm that automatically selects the most effective sorting algorithm Counting Sort, Radix Sort, or QuickSort based on real-time monitoring of patterns in input data. The architecture begins by having a feature extraction module to compute significant parameters such as data volume, value range and entropy. These parameters are sent to a decision engine involving Finite State Machine and XGBoost classifier to aid smart and effective in choosing the optimal sorting strategy. It implements Counting Sort on small key ranges, Radix Sort on large range structured input with low-entropy keys and QuickSort on general purpose sorting. The experimental findings of both synthetic and real life dataset confirm that the proposed solution is actually inclined to excel significantly by comparison in execution time, flexibility and the efficiency of conventional static sorting algorithms. The proposed framework provides a scalable, high perhaps and applicable to a wide range of data processing operations like big data analytics, edge computing, and systems with hardware limitations.