BQM Hybrid Sorting Algorithm

In computer science, sorting is a fundamental operation and there are numerous sorting algorithms which have been developed with their own strengths and limitations. In this paper, I propose a hybrid sorting algorithm that combines the benefits of Merge Sort, Quick Sort, and Bubble Sort to overcome their respective drawbacks. The hybrid algorithm leverages Merge Sort's efficiency in merging sorted subarrays, Quick Sort's effectiveness in partitioning, and Bubble Sort's simplicity and efficiency for small data sets. By dynamically switching between these algorithms based on the input size, the hybrid approach offers improved performance and adaptability. I present a detailed description of the hybrid sorting algorithm, including the conditions for algorithm switching and the merging technique. Additionally, we conduct a comprehensive performance analysis, comparing the hybrid algorithm with standalone sorting techniques. Experimental results demonstrate the algorithm's efficiency and scalability across various data sets. Our findings show that the hybrid sorting algorithm achieves enhanced performance, particularly for large and partially sorted data sets. We discuss the practical applications of this algorithm, including scenarios where its adaptability, stability, and efficiency can be beneficial. Moreover, we outline potential directions for future research, such as optimizations and extensions of the hybrid algorithm. By combining the strengths of Merge sort, Quick Sort, and Bubble Sort, the proposed hybrid sorting algorithm offers a promising solution for efficient and adaptive sorting. This algorithm has the potential to significantly impact sorting operations in various Domains and contribute to the advancement of sorting algorithms.