Mostafa-wael / Hybrid-Sorting-Algorithm

A hybrid sorting algorithm that is independent of the input sample size and its sorting status 🃏

Hybrid Sorting Algorithm

Objective:

• Creating a hybrid sorting algorithm that is independent of the input sample size and its sorting status.

Approach:

• Creating a hybrid optimized version of the merge and quick sorts.
• Checking the sorting status of the list to choose the proper algorithm to procced with it.
• If more than 90% of the list were sorted, Insertion sort is chosen since, it has the least running time for sorted lists.
• If less than 90% and more than 60% of the list were sorted, hybrid merge sort proved to have the least running time for the semi-sorted lists.
• If less than 60% of the list were sorted, hybrid quick sort proved to have the least running time for the unsorted lists.

Hybrid Quick Sort:

• Choosing a pivot that represents the median value of the first, middle and last elements.
• If the size of the list after portioning is less than a specified threshold, an adapted version of insertion sort is used since that, it proved to have the least running time for small lists regardless of their sorting status.

Hybrid Merge Sort:

• If the size of the list after portioning is less than a specified threshold, an adapted version of insertion sort is used since that, it proved to have the least running time for small lists regardless of their sorting status.

Achievements:

• Creating a hybrid quick sort that is two times faster than the normal quick sort for sorted lists.
• Crating a hybrid sort that has the best-case time complexity (O(nlogn)) for any input size regardless of the data being sorted or not.

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To test the code:

1. Open the terminal in your current working directory.
2. Type "python runscript.py n data.txt" where n is any valid integer.
3. Type "g++ -O3 SortingAlgo.cpp -o SortingAlgo".
4. Type "SortingAlgo 1 data.txt sorted_data.txt running_time.txt", you can change the name of the second two files.