Time Complexity Optimization of Data Sorting Using Super Sort Algorithm Method

Abstract

Sorting algorithms are essential for organizing data in a specific order, making them a critical component of computing processes. Due to their significance, many researchers have developed various sorting techniques to improve both time and space complexity. Super Sort, introduced in 2018, is a hybrid algorithm that combines the processes of Merge Sort and Quick Sort, with the goal of outperforming both. However, further research revealed a weakness in Super Sort: it generates a large number of sublists, particularly when handling duplicate elements. This results in higher memory usage, as multiple small sublists must be combined during the sorting process. To address this issue, the present study introduces a method to reduce the number of sublists by grouping identical values and using a key-based approach to minimize the amount of data being sorted. The experimental results show that the modified Super Sort algorithm demonstrates improved time and space complexity compared to the original version.

Access to Document

Other files and links

• Link to publication in Scopus

Fingerprint