Abstract
A novel and efficient hybrid sorting algorithm, termed the Merge-Block-Insertion sort (MBISort) algorithm, is proposed. MBISort combines the principles of insertion sort, block sort, and merge sort into an in-place procedure that exhibits markedly improved average-case performance compared to standalone block sort and adaptive merge sort. Comparative analyses on structured datasets, such as sorted and partially sorted arrays, demonstrate that MBISort achieves faster execution times over a broad range of input sizes (from 100 to 1,000,000 elements). On average, performance improvements of 20 % over adaptive merge sort and 41 % over block sort are observed, highlighting its robust efficiency across diverse data types and distributions. The algorithm also performs exceptionally well for large datasets with high degrees of order, a result of the dynamic integration of insertion sort with an adaptive merging strategy. Additionally, a tunable threshold parameter allows MBISort to adapt to varying data distributions and optimize performance.