Which of these sort algorithms is the most efficient/and least efficient?

Might you list them from higher to lower? Thank you.

Algorithms An algorithm is a specific sequence of steps that describe a process. Sorting Algorithm - an algorithm that transforms a random collection of elements into a sorted collection of elements. Examples include: Bubble Sort: continuously steps through a list, swapping items until they appear in the correct order. Insertion Sort: takes elements from a list one by one and inserts them in their correct position into a new sorted list. Merge Sort: divides the list into the smallest unit (e.g., 1 element), then compares each element with the adjacent list to sort and merge the two adjacent lists. This process continues with larger lists until at last, two lists are merged into the final sorted list. Heap Sort: divides a list into sorted and an unsorted lists and extracts the largest element from the unsorted list and moves it to the bottom of the sorted list. Quick Sort: partitions list using a pivot value, placing elements smaller than the pivot before the pivot value and greater elements after it. The lesser and greater sublists are then recursively sorted. Searching Algorithm - an algorithm that determines if an element exists in a collection of elements. If the element does exist, its location is also returned. Examples include: Binary search: finds a search value within a sorted list by comparing the search value to the middle element of the array. If they are not equal, the half in which the target cannot lie eliminated and the search continues on the remaining half, again taking the middle element to compare to the target value, and repeating this until the target value is found. Hashing: uses a hashing function that maps data of arbitrary size (e.g., a string of characters) to data of a fixed size (e.g., an integer) and then to compute an index that suggests where the entry can be found in a hash table (an array of buckets or slots, from which the desired value can be found through the index)