3. A NEW SORTING (D-SHUFFLE) TECHNIQUE USING THE DIVIDE AND CONQUER TECHNIQUE 3.1 Introduction Sorting has been analyzed by computer scientists for decades and therefore is an ideal topic to start with when studying computer science. Sorting is done through algorithms, which are a set of specific commands that must be followed in a certain order to complete a task. To study sorting, you must first be familiar with iteration and recursion. These two terms designate how tasks are performed repeatedly. The sorting problem can have two meanings. In one context, it refers to the grouping of similar items; for example, sorting your laundry into piles of shirts and socks. In another context it refers to the ordering of items according to some rules; for example, organize names alphabetically in an address book. The formal study of sorting is reserved for a data structures or algorithms course, in which numerous variations of sorting are studied. There are several elementary and advanced sorting algorithms. Some sorting algorithms are simple and spontaneous, such as bubble sort. Others, like quick sort, are enormously complex, but produce super-fast results. Some sorting algorithms work on a smaller number of elements, some are suitable for floating point numbers, some are suitable for specific ranges, some sorting algorithms are used for a large number of data, and some are used if the list has values repeated. Other factors to consider when choosing a sorting algorithm include programming effort, the number of main memory words available, the size of the disk or tape drives, and the extent to which the list is already sorted [4]. This means that all sorting algorithms are problem specific, meaning they work well on some specific problems and they don't work well... middle of the paper... the results are obtained by averaging 5 experiments. The proposed method offers 96% efficiency compared to Bubble sort and some equivalence of random datasets with Quick sort. The proposed D-Shuffle sort algorithm compares with some standard survey documents based on Divide & Conquer sorting methodology, and the running time results based on various data sizes are provided in Table 1. Based on the results in Table 1, for some larger datasets, the proposed algorithm works much faster than the GCS method. Author Name Year Sort Name Method 50 500 1000 5000 10000 Rajat K. Pal 2004 Sieve Sort Divide & Conquer 1 1 2 2 14.2 Khalid Suleiman Al-Kharabsheh 2013 Grouping Comparison Sort (GCS) Divide & Conquer 3 4 4 19 983S. Muthusundari Dr. RMSuresh 2013 D-Shuffle Sort Divide & Conquer 6 8 8 13 58.9Table 1. Comparative study of execution time in milliseconds (ms)