An array of elements can be sorted using the straightforward sorting technique known as selection sort.
The algorithm operates by continually selecting the initial unsorted element and exchanging it with the least element from the unsorted portion of the array. Up till the full array is sorted, this procedure is repeated.
The algorithm continues to find the minimum element until the entire array is sorted. It has a time complexity of O(n^2), making it slow for large arrays, but efficient for small arrays. The algorithm is in-place, meaning it requires no extra memory, making it a good choice for embedded systems with limited resources. However, it is not cache-friendly, making it unsuitable for high-performance computing applications.
Algorithm can be described as follows
- Set the first element of the unsorted portion of the array as the minimum.
- Compare this minimum element with the rest of the elements in the unsorted portion of the array.
- If a smaller element is found, set it as the new minimum.
- Replace the current minimum with the array’s first unsorted element.
- Shift the array’s unsorted section one position to the right.
- Carry out steps 1 through 5 once more to sort every element.
Selection sort has the following advantages
- Easy to understand and implement: Selection sort has a simple algorithm and is easy to understand, making it a good choice for educational purposes and for people who are just starting to learn about sorting algorithms.
- Stable: It is a stable sorting algorithm, which means that the sorted array still maintains the relative order of equal elements.
- In-Place: Selection sort: this is an in-place sorting algorithm, meaning that it sorts the array in place without using any extra memory.
- Effective for small arrays: The selection sort is inefficient for large arrays since its time complexity is O(n2). It may, however, be quicker than other sorting methods for tiny arrays.
- Adaptive: Selection sort is an adaptive sorting algorithm, meaning that its performance changes depending on the data it is sorting. If the array is already sorted or nearly sorted, the selection sort performs better than its average time complexity would suggest.
Although it is not as efficient as other sorting algorithms for large arrays, it still has its place in computer science and is used in certain applications where its simplicity and efficiency for small arrays are more important.
Selection sort has the following disadvantages
- Slow for large arrays: The selection sort has a time complexity of O(n^2), making it slow for large arrays. As the size of the array increases, the time it takes to sort the array grows quadratically, making it impractical for large arrays.
- Not efficient for data with many duplicates: Selection sort can be slow for data with many duplicates, as it will have to compare many equal elements before swapping them.
- Poor performance on partially sorted arrays: Selection sort performs poorly on partially sorted arrays, as it will have to swap elements multiple times before the array is sorted.
- Not cache-friendly: Selection sort is not cache-friendly, meaning that it does not take advantage of the cache to optimize performance.
- Unsuitable for real-time applications: The selection sort is not suitable for real-time applications that require a fast sorting algorithm, as its time complexity is too slow.
Selection sort has the disadvantages of being slow for large arrays, not efficient for data with many duplicates, poor performance on partially sorted arrays, not cache-friendly, and unsuitable for real-time applications. Although it has its advantages, these disadvantages make it unsuitable for many real-world applications, and more efficient sorting algorithms such as Quick Sort or Merge Sort are often preferred.
Selection sort is best used in situations such as
- Educational purposes: Selection sort is a simple and easy-to-understand sorting algorithm, making it a good choice for educational purposes and for people who are just starting to learn about sorting algorithms.
- Data with limited constraints: Selection sort can be used for data with limited constraints, such as arrays with a small number of elements or arrays with few duplicates.
- Embedded systems with limited resources: It is an in-place sorting algorithm that requires no extra memory, making it a good choice for embedded systems with limited resources.
- Testing and comparison: It can be used to test and compare the performance of other sorting algorithms, as it provides a baseline for comparison.
selection sort is best used for educational purposes, small arrays, data with limited constraints, embedded systems with limited resources, and testing and comparison. Although it is not as efficient as other sorting algorithms for large arrays, it still has its place in computer science and is used in certain applications where its simplicity and efficiency for small arrays are important.
Selection sort should not be used in the following situations
- Large arrays: The selection sort has a time complexity of O(n^2), making it slow for large arrays. As the size of the array increases, the time it takes to sort the array grows quadratically, making it impractical for large arrays.
- Real-time applications: It is not suitable for real-time applications that require a fast sorting algorithm, as its time complexity is too slow.
- Data with many duplicates: It can be slow for data with many duplicates, as it will have to compare many equal elements before swapping them.
- Partially sorted arrays: It performs poorly on partially sorted arrays, as it will have to swap elements multiple times before the array is sorted.
- High-performance computing: It is not cache-friendly, meaning that it does not take advantage of the cache to optimize performance. It is not suitable for high-performance computing applications that require fast sorting algorithms.
Conclusion
It works by repeatedly finding the minimum element in the unsorted portion of an array and swapping it with the first unsorted element. This process is repeated until the entire array is sorted. Although selection sort is simple to understand and implement, its time complexity makes it less efficient for large arrays.
