RBS: A New Comparative and Better Solution of Sorting Algorithm for Array

Abstract

In modern technology or practical application, all things are gradually arranged. Therefore, in this paper, we attempt to establish a new sorting algorithm and compare it with some other standard sorting algorithms by their execution times. We designed and developed a methodology that can be executed on random data, ordering data, and some specific data (data means integers value), which can provide the best result for Right Bounding Sort (RBS). The methodology is comparing the array's left value with the rightmost value. Under a particular condition, these two values are swapped, then left index increase, and after completing the first step, the right index decrease, and it continues up to array size. Though there are many sorting algorithms invented for sorting an array with specific time complexity, space complexity, and other factors, they have some limitations because there is no sorting that is perfect for all various types of data. Some are good for small ranges of value, some are vast ranges of data, some are stable, and some are unstable. This algorithm performs with Big-O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) time complexity and Big-O (1) space complexity. We see that our new sorting algorithms provide better solutions than some other existing sorting algorithms like the bubble sort, insertion sort, cycle sort, etc. On the experimental dataset in respect of time complexity.