Abstract
In-place sorting algorithms are essential for optimizing performance in environments with limited memory resources. This study presents a detailed comparative analysis of thirteen in-place sorting algorithms, including widely used techniques such as quick sort and heap sort, as well as less conventional methods like pancake sort, Gnome sort, and Smooth sort. A dual-method approach is employed: theoretical evaluation assesses time and space complexity, stability, and algorithmic adaptability, while empirical experiments benchmark performance across varying input sizes, ranging from 10 to 100,000 elements, using uniformly distributed data. All algorithms were implemented in Java and tested under consistent conditions, with execution time measured using nanosecond precision. Results reveal that no single algorithm performs best across all scenarios. For example, quick sort and heap sort excel in large datasets, while simpler algorithms like bubble sort and insertion sort are more efficient for small or nearly sorted inputs. This research offers practical insights into the strengths and trade-offs of each algorithm, guiding developers and researchers in choosing the most suitable in-place sorting strategy based on data characteristics and resource constraints.