7 Most used Sorting Algorithms in Java (with Code)

In the grand orchestra of programming, sorting algorithms are the conductors, bringing order to the chaos of data. Imagine a library with thousands of books scattered in disarray; a sorting algorithm is the diligent librarian who organizes them, ensuring each tome is exactly where it should be for the eager reader. In the realm of Java, a language rich with functionality and precision, understanding these algorithms is not just learning; it’s an initiation into a rite of passage for every developer.

Sorting is not merely a technical task; it’s a narrative of efficiency, a story of transforming jumbled arrays into harmonious sequences. It’s about finding the melody in the numbers and the rhythm in the indexes. The algorithms we’re about to explore are more than just methods; they are the ancient scrolls of Java, each with its own legend, its own strengths, and its own time to shine.

In this guide, we’ll embark on a quest through the 7 most utilized sorting algorithms in Java. We’ll start with the humble beginnings of Bubble Sort, a simple tune that sets the stage for more complex performances. Then, we’ll waltz through the precise steps of Selection Sort and the adaptive dance of Insertion Sort, before diving into the sophisticated symphonies of Merge Sort and the rapid-fire crescendos of Quick Sort.

As we dissect each algorithm, we’ll not only provide you with the code but also the understanding of the ‘why’ and the ‘when’—the strategy behind the application. We’ll look at the time complexities like a maestro looks at his sheet music, understanding the tempo and the time signatures of each sorting act.

So, dear reader, prepare your Java compiler, open your mind, and let your fingers dance across the keyboard. Whether you’re a novice to the scene or a seasoned programmer with code running through your veins, there’s a rhythm here for you to discover. Let’s begin the overture to Java’s most used sorting algorithms and orchestrate our data into perfect harmony.

 

Table of Contents

Brief overview of sorting and its importance in computer science

Sorting is a fundamental operation in computer science that involves arranging the elements of a collection, such as an array or a list, in a specified order – usually in numerical or lexicographic ascending or descending order. This operation is important because it has a significant impact on the efficiency and performance of complex algorithms and systems that depend on ordered data.

Importance of Sorting in Computer Science:

  • Search Optimization: Sorted data dramatically increases the efficiency of search algorithms. For example, binary search works on the principle of divide and conquer and can quickly find an item in a sorted array, providing a search time complexity of O(log n) compared to O(n) for unsorted data. Does it.
  • Data organization: Sorting helps organize data in a human-readable and meaningful way, making it easier to understand, analyze, and visualize. This is especially important in areas such as data analysis, where sorted data can reveal trends and patterns.
  • Efficient data processing: Many algorithms, such as those used in databases for join operations, work more efficiently if the data is sorted. Sorting can also facilitate the merging of datasets, as seen in the merge operation of the merge sort algorithm.
  • Problem-solving: Some problems require sorted data as a prerequisite. For example, it is easier to find the median, quartiles, or other statistical measures when a dataset is sorted.
  • Algorithmic efficiency: Some algorithms, especially those that deal with numerical calculations, can benefit from sorted data to avoid unnecessary calculations and improve cache performance.
  • Space complexity: In-place sorting algorithms can save space because they do not require any additional storage beyond the original data set.

Sorting Algorithm in Java Programming:

In Java, sorting is a common operation, and the language provides built-in methods like Arrays.sort() and Collections.sort() to perform this task. However, understanding how different sorting algorithms work is essential for Java programmers because:

This allows the selection of the most appropriate sorting technique depending on the context, such as the size and nature of the data.

It provides insight into the inner workings of Java’s built-in sorting methods.
This equips developers with the knowledge to implement custom sorting functions when needed, such as when sorting objects based on multiple fields or custom comparison logic.

In short, sorting is not just a basic utility but a vital operation that underlies many complex systems and applications in computer science. For Java programmers, efficiency in sorting algorithms means writing more efficient and effective code.

Explanation of sorting algorithms and their role in Java programming

Sorting algorithms are a series of steps or procedures used to sort elements in a list or array. In Java programming, as in many other programming languages, sorting algorithms are vital for organizing data so that it can be accessed and used more efficiently.

Role of sorting algorithms in Java programming:

  • Data Management: Java is widely used in enterprise applications where large data sets are common. Sorting algorithms help manage this data by sorting it according to specific criteria, which is essential for fast retrieval and efficient data manipulation.
  • Algorithm Optimization: Many complex algorithms require data to be sorted as a preliminary step. Having an ordered data set can reduce the complexity of subsequent operations, optimizing overall program performance.
  • System performance: Java applications often run on servers where response time is critical. Efficient sorting can reduce processing time, resulting in faster response times and better system performance.
  • Functional programming: Java 8 introduced sequences and lambda expressions, which often work with ordered collections. Understanding sorting algorithms helps Java developers use these features more effectively.
  • Customization: While Java provides built-in sorting methods, they may not be suitable for all data types. Knowledge of sorting algorithms allows Java developers to customize sorting logic to fit the specific needs of their application, such as classifying complex objects or implementing stable sorting.
  • Learning and problem-solving: Classification is a common topic in computer science education and interviews. Java programmers often need to implement or understand sorting algorithms to solve problems and demonstrate their knowledge of algorithmic thinking.
  • Library and API Development: Developers creating libraries and APIs in Java may need to implement sorting algorithms to provide these capabilities to end users, especially when it comes to custom data structures.

Common sorting algorithms in Java:

  • Bubble Sort: A simple comparison-based algorithm where each element is compared to its adjacent element and swapped if they are in the wrong order. It is not commonly used in practice due to its poor efficiency with large data sets.
  • Sort by selection: This algorithm divides the input list into two parts: the sublist of elements already sorted and the sublist of elements that remain to be sorted. It is also inefficient for large lists but is easy to understand and implement.
  • Insertion Sort: Creates the final sorted array, one element at a time. It is much less efficient on large lists than more advanced algorithms like quick sort, heap sort, or merge sort.
  • Merge Sort: An efficient and stable divide-and-conquer algorithm that splits the list into halves, sorts them, and then merges the sorted halves. It has good average and worst-case performance.
  • Quick Sort: A highly efficient sorting algorithm that uses partitioning to recursively split the list into smaller sublists. It performs well in the average case but performs poorly in the worst case.
  • Heap sort: This algorithm uses a binary heap data structure to create a sorted array. It has good performance and is not significantly affected by data distribution.

Each of these algorithms has its own use cases and performance characteristics, and Java programmers choose them based on the specific requirements of the application, such as the size of the data, the nature of the data (for example, whether are already partially ordered). and performance limitations. Understanding these algorithms is a critical part of a Java programmer’s toolkit, allowing them to write more efficient and effective code.

Prerequisites

Basic understanding of Java programming language

A basic understanding of the Java programming language includes becoming familiar with its syntax, core concepts, and fundamental programming constructs. Here’s what it typically involves:

  • Syntax: Knowing the rules for writing valid Java code, such as the correct use of semicolons, braces, and the structure of Java statements and expressions.
  • Variables and data types: Understanding how to declare and use variables, as well as the different data types available in Java, such as int, double, boolean, and string.
  • Operators: Familiar with arithmetic, relational, logical, and assignment operators and how they are used to manipulate data.
  • Control Flow Statement: The ability to use if, else, switch, for, while, and while statements to control the flow of execution based on conditions.
  • Methods: How to define methods (functions), pass parameters, return values and learn the concept of method overloading.
  • Object-Oriented Programming (OOP): Basic understanding of OOP principles such as classes, objects, inheritance, encapsulation, and polymorphism.
  • Standard Library: Familiarity with the Java standard library, especially commonly used classes in the java.lang, java.util, and java.io packages.
  • Exception Handling: Understanding Java’s exception handling model using try, catch, finally, and creating custom exception classes.
  • Arrays and collections: Knowing how to declare, initialize, and access arrays, as well as an initial understanding of collection frameworks (e.g., ArrayList, HashMap).
  • Basic Input/Output: The ability to read and write from the console, as well as basic file I/O operations.
  • Development Environment: Familiarity with the Java development environment, whether it’s a simple setup like a text editor and command-line tools or an integrated development environment (IDE) like Eclipse or IntelliJ IDEA.
  • Compilation and Execution: Understanding how to compile Java source code into bytecode and run it on the Java Virtual Machine (JVM).
  • Debugging: Basic skills in debugging Java code, such as setting breakpoints and stepping through code.

A fundamental understanding of these elements enables a programmer to write simple Java programs and lays the foundation for tackling more complex programming challenges. As one’s understanding deepens, they can explore advanced topics with Java such as concurrency, networking, graphical user interfaces, and web development.

Familiarity with Java arrays and array manipulation

Familiarity with Java arrays and array manipulation involves understanding several key concepts and being able to perform various operations on arrays. Here’s a breakdown of what this entails:

Array Declaration and Initialization:

  • Knowing how to declare an array and allocate memory for it.
  • Understanding how to initialize an array with values at the time of declaration or after it.
  •  
				
					int[] myArray = new int[10]; // declaration and allocation
myArray[0] = 1; // initialization of the first element

				
			

Accessing Array Elements:

  • Using indices to access and modify the elements of an array.
  • Knowing that array indices start at 0 and go up to the array’s length minus one.
				
					int firstElement = myArray[0]; // accessing the first element
myArray[0] = 5; // modifying the first element

				
			

Array Properties:

  • Using the length property to determine the size of an array.
				
					int arraySize = myArray.length;

				
			

Iterating Over Arrays:

  • Using loops (for, while, do-while) to iterate through an array.
  • Enhanced for-loop (for-each loop) for iterating without using index variables.
				
					for (int i = 0; i < myArray.length; i++) {
    // Access myArray[i]
}

for (int element : myArray) {
    // Access element
}

				
			

Multidimensional Arrays:

  • Understanding how to declare, initialize, and access multidimensional arrays (arrays of arrays).
				
					int[][] matrix = new int[3][3]; // a 3x3 integer matrix
matrix[0][0] = 1; // accessing and initializing the first element of the matrix

				
			

Array Copying:

  • Using loops or System.arraycopy() to copy elements from one array to another.
  • Understanding the difference between shallow copying and deep copying, especially for multidimensional arrays.

Common Array Operations:

  • Sorting arrays using Arrays.sort().
  • Searching for elements using methods like Arrays.binarySearch() after sorting.
  • Converting arrays to and from collections like ArrayList

Array Limitations:

  • Recognizing that arrays are of fixed size and cannot be resized once initialized.
  • Understanding that adding or removing elements from an array requires creating a new array or using a collection class.

Common Array Algorithms:

Implementing algorithms for common tasks such as reversing an array, finding the maximum or minimum value, and shuffling elements.

Array Utilities:

Using the Arrays class from java.util package for utility methods like equals(), fill(), copyOf(), and toString().

Having a good grasp of these concepts allows a Java programmer to effectively use arrays in their programs for data storage and manipulation, which is a fundamental aspect of many programming tasks.

Conceptual knowledge of algorithm complexity and Big O notation

Conceptual knowledge of algorithm complexity and Big O notation is crucial for understanding how algorithms perform under various conditions, especially with regard to time and space requirements. Here’s a detailed explanation:

  1. Algorithm Complexity:

    • This refers to a measure of the amount of time and/or space an algorithm takes to complete as a function of the size of the input data.
    • Complexity is often expressed in terms of worst-case, average-case, and best-case scenarios.
  2. Big O Notation:

    • Big O notation is a mathematical notation used to classify algorithms according to how their run time or space requirements grow as the input size grows.
    • It provides an upper bound on the growth rate of the algorithm’s time or space requirement, giving a worst-case scenario.
  3. Time Complexity:

    • Time complexity is a function that represents the number of basic operations an algorithm performs to finish its task.
    • These operations are considered to be executed in a constant amount of time.
    • Common time complexities include O(1) for constant time, O(log⁡n) for logarithmic time, O(n) for linear time, O(nlog⁡n) for linearithmic time, O(n2) for quadratic time, etc.
  4. Space Complexity:

    • Space complexity is a function that represents the amount of memory an algorithm uses in terms of the size of the input data.
    • It includes both the temporary space needed by the algorithm and the space for the input data.
  5. Understanding Big O:

    • Big O abstracts away constants and lower-order terms to focus on the main factor that affects the growth rate.
    • For example, an algorithm with a time complexity of 3n2+2n+1 is simply O(n2) in Big O notation.
  6. Examples of Big O:

    • Constant Complexity: O(1) – The running time does not change with the size of the input data.
    • Linear Complexity: O(n) – The running time grows linearly with the size of the input.
    • Quadratic Complexity: O(n2) – The running time grows quadratically with the size of the input.
  7. Analyzing Algorithms:

    • To determine the Big O complexity, you analyze the algorithm’s structure, identifying loops, recursive calls, and other constructs that contribute to the complexity.
    • Nested loops often lead to multiplicative complexities (e.g., two nested loops lead to O(n2)), while consecutive statements or loops lead to additive complexities (e.g., two separate loops lead to O(2n), which simplifies to O(n)).
  8. Practical Implications:

    • Understanding Big O notation helps in choosing the most efficient algorithm for a given problem, especially for large inputs.
    • It is also essential for optimizing existing algorithms and for conducting performance analysis and comparisons.
  9. Limitations of Big O:

    • Big O notation does not give the exact number of operations; it only describes the growth rate.
    • It does not account for the actual speed of the algorithm, which can be affected by hardware, compiler optimizations, and constants that are ignored in Big O analysis.

In summary, conceptual knowledge of algorithm complexity and Big O notation allows developers and computer scientists to predict the scalability of algorithms and to choose or design algorithms that are appropriate for their needs, especially in terms of efficiency and resource management.

8 Types of Most used Sorting Algorith

No we will go through the list and explanation of sorting algorithms. 

1. Bubble Sort

Introduction to Bubble Sort

Bubble Sort is one of the simplest sorting algorithms available. It’s often taught in introductory computer science courses because it’s straightforward to understand and implement, but it’s not very efficient for large datasets. The name “Bubble Sort” comes from the way elements “bubble up” to their correct position in the array.

How Bubble Sort Works

Bubble Sort works by repeatedly stepping through the list, comparing adjacent elements, and swapping them if they are in the wrong order. This process continues until the list is sorted. The algorithm gets its name because smaller elements “bubble” to the top of the list (beginning of the array) while the larger ones sink to the bottom (end of the array).

Here’s a step-by-step explanation of the process:

  1. Start with the first element in the array.
  2. Compare the current element with the next element.
  3. If the current element is greater than the next element, swap them.
  4. Move to the next element and repeat steps 2 and 3 until you reach the end of the array.
  5. By now, the largest element has bubbled up to the last position in the array.
  6. Repeat the entire process for the remaining elements (excluding the last sorted elements).
  7. Continue the iterations until no swaps are needed, indicating that the array is sorted.

Java Code Example for Bubble Sort

Here’s a simple Java code example that demonstrates the Bubble Sort algorithm:

				
					public class BubbleSortExample {
    public static void bubbleSort(int[] arr) {
        int n = arr.length;
        boolean swapped;
        for (int i = 0; i < n - 1; i++) {
            swapped = false;
            for (int j = 0; j < n - i - 1; j++) {
                if (arr[j] > arr[j + 1]) {
                    // Swap arr[j] and arr[j+1]
                    int temp = arr[j];
                    arr[j] = arr[j + 1];
                    arr[j + 1] = temp;
                    swapped = true;
                }
            }
            // If no two elements were swapped by inner loop, then break
            if (!swapped) {
                break;
            }
        }
    }

    public static void main(String[] args) {
        int[] data = { 5, 1, 4, 2, 8 };
        bubbleSort(data);
        System.out.println("Sorted Array in Ascending Order:");
        System.out.println(Arrays.toString(data));
    }
}

				
			
				
					Sorted Array in Ascending Order:
[11, 12, 22, 25, 64]

				
			

This output is produced by the Bubble Sort algorithm after sorting the array [64, 25, 12, 22, 11] in ascending order.

Time Complexity Analysis

The time complexity of Bubble Sort is easy to determine. In the worst-case scenario, where the array is in reverse order, and we need to do the maximum number of swaps, the time complexity is O(n2). This is because for each element, we’re potentially making a comparison with every other element.

  • Best Case: The best-case time complexity is O(n), which occurs when the array is already sorted. In this case, the algorithm only needs to pass through the array once; since no swaps are made, it can terminate early.
  • Average Case: The average-case time complexity is also O(n2), assuming that the elements are randomly distributed.
  • Space Complexity: The space complexity of Bubble Sort is O(1) because it only requires a single additional memory space for the temp variable used for swapping.

In a nutshell, Bubble Sort is a simple algorithm that’s useful for understanding basic sorting mechanics, but due to its O(n2) time complexity, it’s not practical for sorting large datasets. It’s best used for educational purposes or when you’re certain that the data is nearly sorted or the dataset is small.

2. Selection Sort

Selection Sort is another fundamental algorithm often introduced early in computer science education. It improves upon the concept of sorting by selecting the minimum element from an unsorted segment of the array and swapping it with the leftmost unsorted element, moving the boundary of the unsorted segment one step to the right each time.

Step-by-step Explanation of the Algorithm

Here’s how Selection Sort works:

  1. Start with the first element of the array as the minimum.
  2. Scan the remaining unsorted segment of the array to find the smallest element.
  3. After completing the scan, swap the smallest element found with the first element of the unsorted segment.
  4. Move the boundary of the unsorted segment one step to the right and treat the next element as the current minimum.
  5. Repeat the process of finding the smallest element in the unsorted segment and swapping it with the first unsorted element.
  6. Continue this process until the entire array is sorted.

Java Code Example for Selection Sort

				
					public class SelectionSortExample {
    public static void selectionSort(int[] arr) {
        int n = arr.length;

        for (int i = 0; i < n - 1; i++) {
            // Find the minimum element in unsorted array
            int minIndex = i;
            for (int j = i + 1; j < n; j++) {
                if (arr[j] < arr[minIndex]) {
                    minIndex = j;
                }
            }

            // Swap the found minimum element with the first element
            int temp = arr[minIndex];
            arr[minIndex] = arr[i];
            arr[i] = temp;
        }
    }

    public static void main(String[] args) {
        int[] data = { 64, 25, 12, 22, 11 };
        selectionSort(data);
        System.out.println("Sorted Array in Ascending Order:");
        System.out.println(Arrays.toString(data));
    }
}

				
			
Sorted Array in Ascending Order:
[11, 12, 22, 25, 64]

Time Complexity Analysis

The time complexity of Selection Sort is determined by the number of comparisons and swaps it makes:

  • Best Case: Even if the array is already sorted, Selection Sort makes the same number of comparisons, so the best-case time complexity is O(n2).
  • Average Case: On average, the time complexity remains O(n2) because each element is compared with all other elements.
  • Worst Case: The worst-case time complexity is also O(n2), which occurs when the array is sorted in reverse order.
  • Space Complexity: The space complexity of Selection Sort is O(1) because it only requires a single additional memory space for the temp variable used for swapping.

Selection Sort is not influenced by the order of the array, making it a non-adaptive sorting algorithm. It performs the same number of comparisons regardless of the initial order of the elements. Like Bubble Sort, it is not suitable for large datasets due to its quadratic time complexity but is useful for educational purposes and for small or nearly sorted datasets.

3. Insertion Sort

Insertion Sort is a straightforward and efficient sorting algorithm for small datasets. It works similarly to how you might sort a hand of playing cards, picking one card at a time and placing it at the correct position within the sorted part of the array.

Detailed Explanation of the Algorithm’s Process

The Insertion Sort algorithm segments the list into sorted and unsorted parts. It iterates over the unsorted segment and inserts the element being viewed into the correct position of the sorted list.

  1. Start with the second element: The algorithm starts at the second element of the array, considering the first element as the ‘sorted part’.
  2. Compare and insert: It compares the current element with the ones before it and inserts it into its correct position within the sorted part.
  3. Repeat: This process is repeated for all elements until the entire array is sorted.

Java Code Example for Insertion Sort

				
					public class InsertionSort {
    // Function to perform insertion sort on an array
    public static void insertionSort(int[] arr) {
        for (int i = 1; i < arr.length; i++) {
            // The current element to be inserted into the sorted portion of the array
            int current = arr[i];
            int j = i - 1;

            // Move elements of arr[0..i-1], that are greater than current,
            // to one position ahead of their current position
            while (j >= 0 && arr[j] > current) {
                arr[j + 1] = arr[j];
                j = j - 1;
            }
            // Insert the current element into its correct position
            arr[j + 1] = current;
        }
    }

    // Helper method to print the elements of the array
    public static void printArray(int[] arr) {
        for (int i : arr) {
            System.out.print(i + " ");
        }
        System.out.println();
    }

    // Main method to test the insertionSort function
    public static void main(String args[]) {
        int[] arr = { 9, 5, 1, 4, 3 };
        insertionSort(arr);
        printArray(arr); // Output the sorted array
    }
}

				
			

Time Complexity Analysis

  • Best Case: O(n) – This occurs when the array is already sorted, and no shifting is needed.
  • Average Case: O(n2) – This is the case for a random array where approximately half the elements need shifting.
  • Worst Case: O(n2) – This occurs when the array is sorted in reverse order, and every element needs to be moved.

Insertion sort has a quadratic time complexity, which makes it inefficient for large datasets. However, its simplicity and the fact that it is adaptive (performance improves with partially sorted arrays) make it useful for certain situations, such as small arrays or as the final finishing-off algorithm for more advanced sorts like quicksort.

4. Merge Sort

Introduction to Merge Sort

Merge Sort is a powerful sorting algorithm based on the divide and conquer paradigm, which divides the problem into smaller subproblems, solves them independently, and combines them to get the final sorted list.

Understanding the Divide-and-Conquer Strategy

The divide-and-conquer strategy involves three steps:

  1. Divide: Split the array into halves until each subarray contains a single element.
  2. Conquer: Sort each subarray (a single element array is already sorted).
  3. Combine: Merge the sorted subarrays to produce new sorted subarrays until the entire array is merged into a single sorted array.

Java Code Example for Merge Sort

				
					public class MergeSort {

    /**
     * Merges two subarrays of arr[].
     * First subarray is arr[l..m]
     * Second subarray is arr[m+1..r]
     */
    void merge(int arr[], int l, int m, int r) {
        // Find sizes of two subarrays to be merged
        int n1 = m - l + 1;
        int n2 = r - m;

        /* Create temp arrays */
        int L[] = new int[n1];
        int R[] = new int[n2];

        /*Copy data to temp arrays*/
        System.arraycopy(arr, l, L, 0, n1);
        System.arraycopy(arr, m + 1, R, 0, n2);

        /* Merge the temp arrays */

        // Initial indexes of first and second subarrays
        int i = 0, j = 0;

        // Initial index of merged subarray array
        int k = l;
        while (i < n1 && j < n2) {
            if (L[i] <= R[j]) {
                arr[k] = L[i];
                i++;
            } else {
                arr[k] = R[j];
                j++;
            }
            k++;
        }

        /* Copy remaining elements of L[] if any */
        while (i < n1) {
            arr[k] = L[i];
            i++;
            k++;
        }

        /* Copy remaining elements of R[] if any */
        while (j < n2) {
            arr[k] = R[j];
            j++;
            k++;
        }
    }

    /**
     * Main function that sorts arr[l..r] using merge()
     */
    void sort(int arr[], int l, int r) {
        if (l < r) {
            // Find the middle point to divide the array into two halves
            int m = (l + r) / 2;

            // Sort first and second halves
            sort(arr, l, m);
            sort(arr, m + 1, r);

            // Merge the sorted halves
            merge(arr, l, m, r);
        }
    }

    /**
     * A utility function to print array of size n
     */
    static void printArray(int arr[]) {
        for (int i : arr) {
            System.out.print(i + " ");
        }
        System.out.println();
    }

    /**
     * Driver method to test the MergeSort algorithm
     */
    public static void main(String args[]) {
        int arr[] = {12, 11, 13, 5, 6, 7

				
			
				
					Given Array
12 11 13 5 6 7 

Sorted array
5 6 7 11 12 13 

				
			

This output first shows the original unsorted array followed by the sorted array after the Merge Sort algorithm has been applied.

5. Quick Sort

Quick Sort is a highly efficient sorting algorithm and is based on the divide-and-conquer approach. It works by selecting a ‘pivot’ element from the array and partitioning the other elements into two sub-arrays, according to whether they are less than or greater than the pivot. The sub-arrays are then sorted recursively. This can be done in-place, requiring small additional amounts of memory to perform the sorting.

Here’s a fully commented Java code example for Quick Sort, including the output it generates:

				
					public class QuickSort {

    /**
     * This method takes the last element as pivot, places the pivot element at its correct
     * position in sorted array, and places all smaller (smaller than pivot) to left of
     * pivot and all greater elements to right of pivot.
     */
    int partition(int arr[], int low, int high) {
        int pivot = arr[high]; 
        int i = (low - 1); // index of smaller element
        for (int j = low; j < high; j++) {
            // If current element is smaller than the pivot
            if (arr[j] < pivot) {
                i++;

                // swap arr[i] and arr[j]
                int temp = arr[i];
                arr[i] = arr[j];
                arr[j] = temp;
            }
        }

        // swap arr[i+1] and arr[high] (or pivot)
        int temp = arr[i + 1];
        arr[i + 1] = arr[high];
        arr[high] = temp;

        return i + 1;
    }

    /**
     * The main function that implements QuickSort()
     * arr[] --> Array to be sorted,
     * low --> Starting index,
     * high --> Ending index
     */
    void sort(int arr[], int low, int high) {
        if (low < high) {
            // pi is partitioning index, arr[pi] is now at right place
            int pi = partition(arr, low, high);

            // Recursively sort elements before partition and after partition
            sort(arr, low, pi - 1);
            sort(arr, pi + 1, high);
        }
    }

    /**
     * A utility function to print array of size n
     */
    static void printArray(int arr[]) {
        for (int i : arr) {
            System.out.print(i + " ");
        }
        System.out.println();
    }

    /**
     * Driver method to test the QuickSort algorithm
     */
    public static void main(String args[]) {
        int arr[] = {10, 7, 8, 9, 1, 5};
        int n = arr.length;

        System.out.println("Given Array");
        printArray(arr);

        QuickSort ob = new QuickSort();
        ob.sort(arr, 0, n - 1);

        System.out.println("Sorted array");
        printArray(arr);
    }
}

				
			
				
					Given Array
10 7 8 9 1 5 

Sorted array
1 5 7 8 9 10 

				
			

The output displays the original array before sorting and then the sorted array after applying the Quick Sort algorithm.

Time Complexity Analysis:

  • Best Case: O(n log n) – occurs when the partition process always picks the middle element as pivot.
  • Average Case: O(n log n) – it’s typical for a well-implemented Quick Sort.
  • Worst Case: O(n^2) – occurs when the partition process always picks the greatest or smallest element as pivot.

Quick Sort is generally preferred over other sorting algorithms for large datasets because its average time complexity is better than other O(n^2) algorithms, such as Bubble Sort or Insertion Sort. However, Quick Sort can perform poorly in the worst-case scenario, which can be mitigated by using randomized version of Quick Sort.

6. Heap Sort

Heap Sort is a popular comparison-based sorting algorithm that uses a binary heap data structure to create a sorted array. It’s particularly useful for its ability to sort in O(n log n) time for the worst, average, and best cases, which is comparable to both Merge Sort and Quick Sort. However, unlike Merge Sort, Heap Sort sorts in place and does not require a large amount of auxiliary space.

Here’s a brief explanation of Heap Sort, followed by a Java code example that is fully commented. The output will demonstrate the sorted array after Heap Sort is applied.

Introduction to Heap Sort

Heap Sort works by visualizing the elements of the array as a special kind of complete binary tree called a heap. A heap can be of two types, max heap or min heap. In a max heap, the key present at the root node must be greatest among the keys present at all of its children. The same property must be recursively true for all sub-trees in that binary tree.

Understanding the heap data structure

A binary heap is typically represented as an array. The binary heap has two key operations, namely heapify which ensures the heap property is maintained, and buildHeap which builds a heap from an unsorted array.

Java code example for Heap Sort
				
					public class HeapSort {

    /**
     * Main function that sorts arr[] using HeapSort
     */
    public void sort(int arr[]) {
        int n = arr.length;

        // Build heap (rearrange array)
        for (int i = n / 2 - 1; i >= 0; i--)
            heapify(arr, n, i);

        // One by one extract an element from heap
        for (int i = n - 1; i > 0; i--) {
            // Move current root to end
            int temp = arr[0];
            arr[0] = arr[i];
            arr[i] = temp;

            // call max heapify on the reduced heap
            heapify(arr, i, 0);
        }
    }

    /**
     * To heapify a subtree rooted with node i which is an index in arr[].
     * n is size of heap
     */
    void heapify(int arr[], int n, int i) {
        int largest = i; // Initialize largest as root
        int l = 2 * i + 1; // left = 2*i + 1
        int r = 2 * i + 2; // right = 2*i + 2

        // If left child is larger than root
        if (l < n && arr[l] > arr[largest])
            largest = l;

        // If right child is larger than largest so far
        if (r < n && arr[r] > arr[largest])
            largest = r;

        // If largest is not root
        if (largest != i) {
            int swap = arr[i];
            arr[i] = arr[largest];
            arr[largest] = swap;

            // Recursively heapify the affected sub-tree
            heapify(arr, n, largest);
        }
    }

    /* A utility function to print array of size n */
    static void printArray(int arr[]) {
        int n = arr.length;
        for (int i = 0; i < n; ++i)
            System.out.print(arr[i] + " ");
        System.out.println();
    }

    // Driver code
    public static void main(String args[]) {
        int arr[] = {12, 11, 13, 5, 6, 7};
        int n = arr.length;

        HeapSort ob = new HeapSort();
        ob.sort(arr);

        System.out.println("Sorted array is");
        printArray(arr);
    }
}

				
			

Time complexity analysis and comparison with other sorting algorithms

The time complexity of Heap Sort is O(n log n) for all cases, which is the same as for Merge Sort and average-case Quick Sort. However, Heap Sort is not a stable sort, which means it does not maintain the relative order of equal elements. It’s also not as fast in practice as Quick Sort, despite having the same time complexity, due to more complex inner loop operations.

Output:

				
					Sorted array is
5 6 7 11 12 13

				
			

When comparing Heap Sort to other sorting algorithms, it’s important to note that it’s particularly efficient for large data sets and unlike Quick Sort, it guarantees O(n log n) time complexity. It’s also in-place, so it can be more memory efficient than Merge Sort. However, due to its non-stable nature, it might not be suitable for applications where the relative order of similar elements is to be maintained.

7. Shell Sort

Shell Sort is an in-place comparison-based algorithm that is a generalization of insertion sort. The algorithm allows the exchange of items that are far apart, unlike insertion sort which only exchanges adjacent items. This capability significantly improves the performance of insertion sort on larger lists.

Introduction to Shell Sort

Shell Sort was invented by Donald Shell in 1959. It improves upon the insertion sort by breaking the original list into a number of smaller sublists. The unique feature of Shell Sort is that it compares elements separated by a gap of several positions. This allows an element to take “bigger steps” toward its expected position. Multiple passes over the data are made with different gap sizes until the gap is 1. On the final pass, Shell Sort is the same as an insertion sort but most of the work was done in earlier increments.

The concept of diminishing increment sort

Shell Sort uses a sequence known as the gap sequence, which defines the gaps between compared elements. A common choice of sequence is to start with a gap of about half the size of the list, then reduce the gap by half each pass, until you end on a gap of 1.

Java code example for Shell Sort

				
					public class ShellSort {
    /* An utility function to print array of size n*/
    static void printArray(int arr[]) {
        int n = arr.length;
        for (int i = 0; i < n; ++i)
            System.out.print(arr[i] + " ");
        System.out.println();
    }
    
    /* Function to sort array using ShellSort */
    int sort(int arr[]) {
        int n = arr.length;

        // Start with a big gap, then reduce the gap
        for (int gap = n / 2; gap > 0; gap /= 2) {
            // Do a gapped insertion sort for this gap size.
            // The first gap elements arr[0..gap-1] are already in gapped order
            // keep adding one more element until the entire array is gap sorted
            for (int i = gap; i < n; i += 1) {
                // add arr[i] to the elements that have been gap sorted
                // save arr[i] in temp and make a hole at position i
                int temp = arr[i];

                // shift earlier gap-sorted elements up until the correct location for arr[i] is found
                int j;
                for (j = i; j >= gap && arr[j - gap] > temp; j -= gap)
                    arr[j] = arr[j - gap];

                // put temp (the original arr[i]) in its correct location
                arr[j] = temp;
            }
        }
        return 0;
    }

    // Driver method
    public static void main(String args[]) {
        int arr[] = {12, 34, 54, 2, 3};
        System.out.println("Array before sorting");
        printArray(arr);

        ShellSort ob = new ShellSort();
        ob.sort(arr);

        System.out.println("Array after sorting");
        printArray(arr);
    }
}

				
			
				
					Array before sorting
12 34 54 2 3
Array after sorting
2 3 12 34 54

				
			

Time complexity analysis and its effectiveness on medium-sized datasets

The time complexity of Shell Sort depends on the gap sequence it uses. In the worst case, it can be as bad as O(n^2), but with the right gap sequence, it can go down to as low as O(n log n) to O(n^(3/2)), depending on the sequence used. It is most effective on medium-sized datasets as the overhead of the recursive algorithms (like Quick Sort, Merge Sort, etc.) is not significant.

Shell Sort is particularly effective for medium-sized datasets and datasets that are already “almost sorted”. Its adaptive nature allows it to adjust dynamically and efficiently to the given data.

Conclusion

Throughout this tutorial, we’ve explored a variety of sorting algorithms, each with its own unique approach to organizing data. From the simplicity of Bubble Sort to the efficiency of Quick Sort and the advanced technique of Shell Sort, we’ve seen how different algorithms can be suited to different tasks.

Sorting is a fundamental concept in computer science, and understanding these algorithms is crucial for any Java developer. Each algorithm we’ve discussed has its place in the world of programming, whether it be for educational purposes, practical applications, or as a stepping stone to understanding more complex data structures and algorithms.

Bubble Sort and Selection Sort are straightforward and serve as excellent educational tools for beginners to understand the basics of algorithm design. Insertion Sort improves upon these by being adaptive and efficient for small or nearly sorted datasets.

Merge Sort and Quick Sort take a more advanced approach with divide-and-conquer strategies, offering better performance on large datasets. Heap Sort introduces the concept of a binary heap and is particularly useful in priority queue implementations. Shell Sort, with its diminishing increment sort, provides a more complex and efficient alternative to simple insertion sort, especially for medium-sized datasets.

As you continue your journey in Java programming, I encourage you to experiment with these algorithms. Modify the code, test it with different datasets, and observe the performance. Understanding the strengths and limitations of each will not only improve your coding skills but also your ability to choose the right tool for the job.

Remember, the best way to learn is by doing. So, take the code examples provided, run them, break them, fix them, and learn from them. The practical applications of these sorting algorithms are vast, and they form the backbone of many systems we use today.

Finally, keep exploring and learning. The field of computer science is always evolving, and there’s always more to learn. Whether you’re sorting simple arrays or working with complex data structures, the principles you’ve learned here will serve you well.

Happy coding!

F.A.Q.

Most Used Sorting Algorithms in Java (with Code)

The “best” sorting algorithm depends on the context. QuickSort is generally considered efficient for large datasets, while Insertion Sort might be better for small or mostly sorted data.

QuickSort is one of the most commonly used sorting algorithms due to its average-case efficiency and relatively simple implementation.

Bubble Sort and Insertion Sort are often considered the easiest to understand and code, making them great for educational purposes.

Insertion Sort is typically efficient for data that is already mostly sorted.

QuickSort and Merge Sort are generally faster for large datasets, while Insertion Sort can be quicker for small or nearly sorted data.

QuickSort is usually faster than Merge Sort due to lower overhead, but Merge Sort has a better worst-case time complexity.

Binary Search is highly efficient for sorted datasets. The best searching algorithm depends on whether the data is sorted and the size of the dataset.

Bubble Sort is often considered the slowest and least efficient sorting algorithm for large datasets.

QuickSort and Merge Sort are often considered two of the most important sorting algorithms due to their efficiency and widespread use.

Merge Sort and QuickSort are typically recommended for large datasets in Java due to their O(nlog⁡n) time complexity.

Merge Sort, Heap Sort, and QuickSort have the best average-case complexities of O(nlog⁡n).

Heap Sort has a better worst-case time complexity than QuickSort, but QuickSort is often faster in practice due to better cache performance and average-case time complexity.

About The Author

Leave a Comment

Your email address will not be published. Required fields are marked *