Analysis of Non-Comparison Based Sorting algorithms: A review (original) (raw)

Comparison of Sorting Algorithms based on Input Sequences

International Journal of Computer Applications, 2013

Ordering is a very important for mankind .If anything is in unordered then it will not easily understand by anyone but if it is in order then it will easily understand and used by anyone. So ordering is a very important issue in computer science also. In computer science many programming applications use ordering to solving a problem either it is in ascending or descending order. In this paper we discuss four sorting algorithms which are already existed named as Insertion Sort, Bubble Sort, Selection Sort, Merge Sort and we design a new sorting algorithm named as index sort also. In this paper we check the performance and comparison of all five sorting algorithm on the basis of increasing the no of elements in bulk. We check how much processing time is taken by all four sorting algorithms with Index Sort and compared them and finding which sorting algorithm takes less time to sort the elements like 10, 100, 1000, 10000 . If any algorithm takes less processing time it means that it sorts the element faster than others. The processing time of a sorting algorithm is based on the processing speed of a Processor as well as internal memory (RAM) used by the system.

Sorting Algorithms – A Comparative Study

Sorting is nothing but alphabetizing, categorizing, arranging or putting items in an ordered sequence. It is a key fundamental operation in the field of computer science. It is of extreme importance because it adds usefulness to data. In this papers, we have compared five important sorting algorithms (Bubble, Quick, Selection, Insertion and Merge). We have developed a program in C# and experimented with the input values 1-150, 1-300 and 1-950. The performance and efficiency of these algorithms in terms of CPU time consumption has been recorded and presented in tabular and graphical form.

Comparative Analysis & Performance of Different Sorting Algorithm in Data Structure

— An algorithm is precise specification of a sequence of instruction to be carried out in order to solve a given problem. Sorting is considered as a fundamental operation in computer science as it is used as an intermediate step in many operations. Sorting refers to the process of arranging list of elements in a particular order. The elements are arranged in increasing or decreasing order of their key values. This research paper presents the different types of sorting algorithms of data structure like Bubble Sort, Selection Sort, Insertion Sort, Merge Sort and Quick Sort and also gives their performance analysis with respect to time complexity. These five algorithms are important and have been an area of focus for a long time but still the question remains the same of " which to use when? " which is the main reason to perform this research. Each algorithm solves the sorting problem in a different way. This research provides a detailed study of how all the five algorithms work and then compares them on the basis of various parameters apart from time complexity to reach our conclusion. I. INTRODUCTION Algorithm is an unambiguous, step-by-step procedure for solving a problem, which is guaranteed to terminate after a finite number of steps. In other words algorithm is logical representation of the instructions which should be executed to perform meaningful task. For a given problem, there are generally many different algorithms for solving it. Some algorithms are more efficient than others, in that less time or memory is required to execute them. The analysis of algorithms studies time and memory requirements of algorithms and the way those requirements depend on the number of items being processed. Sorting is generally understood to be the process of rearranging a given set of objects in a specific order and therefore, the analysis and design of useful sorting algorithms has remained one of the most important research areas in the field. Despite the fact that, several new sorting algorithms being introduced, the large number of programmers in the field depends on one of the comparison-based sorting algorithms: Bubble, Insertion, Selection sort etc. Hence sorting is an almost universally performed and hence, considered as a fundamental activity. The usefulness and significance of sorting is depicted from the day to day application of sorting in real-life objects. For instance, objects are sorted in: Telephone directories, income tax files, tables of contents, libraries, dictionaries. The methods of sorting can be divided into two categories: INTERNAL SORTING: If all the data that is to be sorted can be adjusted at a time in main memory, then internal sorting methods are used. EXTERNAL SORTING: When the data to be sorted can " t be accommodated in the memory at the time and some has to be kept in auxiliary memory (hard disk, floppy, tape etc) , then external sorting method are used. The complexity of a sorting algorithm measures the running time of function in which " n " numbers of items are sorted. The choice of which sorting method is suitable for a problem depends on various efficiency considerations for different problem. Three most important of these considerations are:  The length of time spent by programmer in coding a particular sorting program.  Amount of machine time necessary for running the program.  The amount of memory necessary for running program.  Stability-does the sort preserve the order of keys with equal values.

Comparative Analysis of Comparison and Non Comparison based Sorting Algorithms

International Journal of Computer Applications, 2020

Sorting is one of the most important task in many computer applications. Efficiency becomes a big problem when the sorting involves a large amounts of data. There are a lot of sorting algorithms with different implementations. Some of them sort data by comparison while others don't. The main aim of this thesis is to evaluate the comparison and noncomparison based algorithms in terms of execution time and memory consumption. Five main algorithms were selected for evaluation. Out of these five, three were comparison based algorithms (quick, bubble and merge) while the remaining two were non-comparison based (radix and counting). After conducting an experiment using array of different data sizes (ranging from 1000 to 35000), it was realized that the comparison based algorithms were less efficient than the noncomparison ones. Among the comparison algorithms, bubble sort had the highest time complexity due to the swapping nature of the algorithm. It never stops execution until the largest element is bubbled to the right of the array in every iteration. Despite this disadvantage, it was realized that it is memory efficient since it does not create new memory in every iteration. It relies on a single memory for the swapping array operation. The quick sort algorithm uses a reasonable amount of time to execute, but has a poor memory utilization due to the creation of numerous sub arrays to complete the sorting process. Among the comparison based algorithms, merge sort was far better than both quick and bubble. On the average, merge sort utilized 32.261 seconds to sort all the arrays used in the experiment while quick and bubble utilized 41.05 and 165.11 seconds respectively. The merge algorithm recorded an average memory consumption of 5.5MB for all the experiment while quick and bubble recorded 650.792MB and 4.54MB respectively. Even though the merge sort is better than both quick and bubble, it cannot be compared to the non-comparison based algorithms since they perform far better than the comparison based ones. When the two groups were evaluated against execution time, the comparison based algorithms recorded an average score of 476.757 seconds while the non-comparison obtained 17.849 seconds. With respect to the memory utilization, the non-comparison based algorithms obtained 27.12MB while the comparison ones obtained 1321.681MB. This clearly reveals the efficiency of the non-comparison based algorithms over the comparison ones in terms of execution time and memory utilization.

Review on Sorting Algorithms -A Comparative Study

— sorting of elements is an important task in computation that is used frequently in different processes. For accomplish task in reasonable amount of time efficient algorithm is needed. Different types of sorting algorithms have been devised for the purpose. Which is the best suited sorting algorithm can only be decided by comparing the available algorithms in different aspects. In this paper a comparison is made for different sorting algorithms used in computation. Keywords— best sorting algorithm, bubble sort algorithms, quick sort algorithms, sorting algorithms, efficient sorting

An Analytical Comparison of Different Sorting Algorithms in Data Structure

2015

Sorting is considered as a very basic operation in computer science. Sorting is used as an intermediate step in many operations. Sorting refers to the process of arranging list of elements in a particular order either ascending or descending using a key value. There are a lot of sorting algorithms have been developed so far. This research paper presents the different types of sorting algorithms of data structure like Bubble Sort, Selection Sort, Insertion Sort, Merge Sort, Heap Sort and Quick Sort and also gives their performance analysis with respect to time complexity. These six algorithms are important and have been an area of focus for a long time but still the question remains the same of "which to use when?" which is the main reason to perform this research. Each algorithm solves the sorting problem in a different way. This research provides a detailed study of how all the six algorithms work and then compares them on the basis of various parameters apart from time c...

An Efficient Optimized Sorting Technique Using Combination of Other Techniques

2020

The problem of sorting is a problem that arises frequently in computer programming and though which is need to be resolved. Many different sorting algorithms have been developed and improved to make sorting optimized and fast. As a measure of performance mainly the average number of operations or the average execution times of these algorithms have been compared. There is no one sorting method that is best for every situation. Some of the factors to be considered in choosing a sorting algorithm include the size of the list to be sorted, the programming effort, the number of words of main memory available,the size of disk or tape units, the extent to which the list is already ordered, and the distribution of values.

A Comparative Study of Sorting Algorithm Based on Their Time Complexity

The quest to develop the most memory efficient and the fastest sorting algorithm has become one of the crucial mathematical challenges of the last half century, resulting in many tried and tested algorithm available to the individual, who needs to sort the list of data. Today, the amount of data is very large, we require some sorting techniques that can arrange these data as fast as possible and also provide the best efficiency in terms of time and space. In this paper, we will discuss some of the sorting algorithms and compare their time complexities for the set of data

Analysis and Performance Measurement of Sorting Algorithms

Proceedings of National Conference on Convergent Innovative Technologies & Management (CITAM-2011) on 2 nd & 3 rd December 2011 at Cambridge Institute of Technology,Bangalore India, 2011

Any number of practical applications in computing requires things to be in order. The performance of any computation depends upon the performance of sorting algorithms. Like all complicated problems, there are many solutions that can achieve the same results. One sort algorithm can do sorting of data faster than another. A lot of sorting algorithms has been developed to enhance the performance in terms of computational complexity, memory and other factors. This paper chooses three of the sorting algorithms: the heap sort, merge sort, quick sort and measures their performance for the realization of time complexity with respect to the theories which are represented normally using asymptotic notation.