Comparison study of sorting techniques in dynamic data structure

Sorting is an important and widely studied issue, where the execution time and the required resources for computation is of extreme importance, especially if it is dealing with real-time data processing. Therefore, it is important to study and to compare in details all the available sorting algorithms. In this project, an intensive investigation was conducted on five algorithms, namely, Bubble Sort, Insertion Sort, Selection Sort, Merge Sort and Quick Sort algorithms. Four groups of data elements were created for the purpose of comparison process among the different sorting algorithms. All the five sorting algorithms are applied to these groups. The worst time complexity for each sorting technique is then computed for each sorting algorithm. The sorting algorithms were classified into two groups of time complexity, O (n2) group and O(nlog2n) group. The execution time for the five sorting algorithms of each group of data elements were computed. The fastest algorithm is then determined by the estimated value for each sorting algorithm, which is computed using linear least square regression. The results revealed that the Merge Sort was more efficient to sort data from the Quick Sort for O(nlog2n) time complexity group. The Insertion Sort had more efficiency to sort data from Selection Sort and Bubble Sort for O (n2) group. Bubble Sort was the slowest or it was less efficient to sort the data. In conclusion, the efficiency of sorting algorithms can be ranked from highest to lowest as Merge Sort, Quick Sort, Insertion Sort, Selection Sort and Bubble Sort.