Contents I. Introduction
For any calculation, sorting algorithms are crucial. They make it possible to arrange data [3] in a specific sequence, either descending or ascending, based on the preferences and needs of the individual. These algorithms are essential to computer science because they make it easier to do a number of other tasks, including data retrieval and search, as well as activities related to organisation and optimisation. From search engines and database management systems to extensive optimisations and data processing initiatives, sorting is ubiquitous. This work aims to examine and contrast various sorting algorithms with respect to their time difficulties, space complexity, and practicality. Bubble Sort mentioned as BSort Insertion Sort as ISort selection sort as SSort and Quick Sort as QSort and Merge Sort as MSort. [6] As data scale and processing demands have increased over time, sorting strategies have changed dramatically over time, moving from basic manual sorting procedures to complicated and effective algorithms. In order to improve operations in a variety of fields, including database administration, networking, search engine optimisation, and real-time system applications, sorting algorithms are essential in current computing. This demonstrates how adaptable and ubiquitous they are in the field of modern technology. Previous feasibility studies, like those by Bingmann et al. (2013)[13] and Sanders[2], have shown that the sorting method has a big effect on how well resources are used and how quickly things are processed overall. These studies show how important it is to do thorough feasibility analyses to see if an algorithm will work with the limits of the problem. Rizvi et al. (2024), who looked at how the performance of different algorithms changes in different situations and emphasized the need for tailored methods in real-world applications, also agree that feasibility studies are important.
