Final answer:
BubbleSort exhibits a worst-case time complexity of O(N²), where N represents the number of elements in the list. This is due to its inherent nature of repeatedly traversing the entire list, making comparisons, and swapping elements, resulting in a quadratic growth in time complexity with an increasing number of elements. So, the correct option is d. O(N²).
Step-by-step explanation:
BubbleSort, a simple sorting algorithm, operates by repeatedly traversing the list, comparing adjacent elements, and swapping them if they are in the wrong order. In the worst-case scenario, where the list is in reverse order, BubbleSort demonstrates a time complexity of O(N²), meaning the time taken for sorting grows quadratically with the number of elements (N) in the list.
This inefficiency arises because, for each element in the list, multiple passes through the entire list are required to ensure that the largest unsorted element "bubbles up" to its correct position.
During each pass, BubbleSort compares adjacent elements and swaps them if they are out of order. As the list size increases, the number of necessary comparisons and swaps grows exponentially, resulting in a quadratic time complexity. This makes BubbleSort impractical for large datasets, as more efficient sorting algorithms with lower time complexities, such as quicksort or mergesort, become preferable choices. In summary, the O(N²) worst-case time complexity of BubbleSort reflects its limited scalability, making it less suitable for real-world applications with sizable datasets.