An Experimental Analysis on Different Pivot Selection Approaches for the Quicksort Algorithm

Abstract

The research primarily examines the significance of pivot selection of the widely used QuickSort algorithm in order to increase the overall performance and efficiency. Quicksort has an average time complexity of O(nlogn), but its performance can degrade to O(n2) in the worst-case scenario, which occurs when a pivot element is chosen badly. This study focuses on the influence of different pivot selection techniques on the efficiency of the Quicksort algorithm through empirical evaluation. To determine which strategy works best for an individual data set and array size, different methods have been evaluated, aiming to choose a pivot that is in close proximity to the median of the subarray, evaluating their efficiency and any drawbacks. In terms of efficiency, the Median of Seven (MO7) and Median of Three (MO3) exhibits the best results, where MO7 gives an execution time of 0.0112 s and MOT of 0.0124 s. A comparative decision criteria has also been proposed in this research in choosing the optimum approach among the best performing MOT and MO7, where MOT being simpler and MO7 being more efficient. These insights offer practical guidance for optimizing Quick Sort implementations in real-world scenarios, where its performance is paramount.