Enhanced insertion sort by threshold swapping

Abstract

Sorting is an essential operation that takes place in arranging data in a specific order, such as ascending or descending with numeric and alphabetic data. There are various sorting algorithms for each situation. For applications that have incremental data and require e an adaptive sorting algorithm, the insertion sort algorithm is the most suitable choice, because it can deal with each element without the need to sort the whole dataset. Moreover, the Insertion sort algorithm can be the most popular sorting algorithm because of its simple and straightforward steps. Hence, the insertion sort algorithm performance decreases when it comes to large datasets. In this paper, an algorithm is designed to empirically improve the performance of the insertion sort algorithm, especially for large datasets. The new proposed approach is stable, adaptive and very simple to translate into programming code. Moreover, this proposed solution can be easily modified to obtain in-place variations of such an algorithm by maintaining their main features. From our experimental results, it turns out that the proposed algorithm is very competitive with the classic insertion sort algorithm. After applying the proposed algorithm and comparing it with the classic insertion sort, the time taken to sort a specific dataset was reduced by 23%, regardless of the dataset's size. Furthermore, the performance of the enhanced algorithm will increase along with the size of the dataset. This algorithm does not require additional resources nor the need to sort the whole dataset every time a new element is added.