Abstract

Information sorting is used in a large range of applications and is critical in determining current effectiveness, efficiency, and consumption. Sorting is not just essential in data science; it is also an important aspect of managing algorithms. It's a difficult challenge to choose the optimal sorting algorithm for a certain application. The trade-off between resource use, speed, and area is the basis for this. This study establishes a helpful link between sorting and categorization, resulting in a general method for speeding up sorting algorithms. In this paper, the performance evaluation of a novel sorting technique is implemented by incorporating a sub-array for obtaining the max index value from the given sub-array. Then calculate the mid value by divide and conquer tactics then proceed to binary search for identified the position of the element. This hybrid approaches yields O(nlogn) in the average cases. Sorting is becoming increasingly needed in real-time applications, such as real-time visualization, database management systems, sensor fusion, and finite element. As a result, on average, the suggested proposed sorting algorithm with modified Cycle sort outperforms the existing Sorting algorithm by 75% in time complexity. The goal of this proposed sorting algorithm is a 75%-time efficiency using the existing modified cycle sorting algorithm.