用java写一段代码，比较冒泡排序、选择排序、插入排序、希尔排序、归并排序、快速排序、堆排序在有序数据情况下的表现

好的，以下是用Java实现的冒泡排序、选择排序、插入排序、希尔排序、归并排序、快速排序、堆排序以及测试它们在有序数据情况下的表现：

import java.util.Arrays;

public class SortingComparison {
    
    public static void main(String[] args) {
        int[] arr = new int[100000];
        
        // 生成有序数据
        for (int i = 0; i < arr.length; i++) {
            arr[i] = i;
        }
        
        long startTime, endTime;
        
        // 冒泡排序
        startTime = System.currentTimeMillis();
        bubbleSort(arr.clone());
        endTime = System.currentTimeMillis();
        System.out.println("Bubble Sort: " + (endTime - startTime) + "ms");
        
        // 选择排序
        startTime = System.currentTimeMillis();
        selectionSort(arr.clone());
        endTime = System.currentTimeMillis();
        System.out.println("Selection Sort: " + (endTime - startTime) + "ms");
        
        // 插入排序
        startTime = System.currentTimeMillis();
        insertionSort(arr.clone());
        endTime = System.currentTimeMillis();
        System.out.println("Insertion Sort: " + (endTime - startTime) + "ms");
        
        // 希尔排序
        startTime = System.currentTimeMillis();
        shellSort(arr.clone());
        endTime = System.currentTimeMillis();
        System.out.println("Shell Sort: " + (endTime - startTime) + "ms");
        
        // 归并排序
        startTime = System.currentTimeMillis();
        mergeSort(arr.clone());
        endTime = System.currentTimeMillis();
        System.out.println("Merge Sort: " + (endTime - startTime) + "ms");
        
        // 快速排序
        startTime = System.currentTimeMillis();
        quickSort(arr.clone());
        endTime = System.currentTimeMillis();
        System.out.println("Quick Sort: " + (endTime - startTime) + "ms");
        
        // 堆排序
        startTime = System.currentTimeMillis();
        heapSort(arr.clone());
        endTime = System.currentTimeMillis();
        System.out.println("Heap Sort: " + (endTime - startTime) + "ms");
    }
    
    // 冒泡排序
    public static void bubbleSort(int[] arr) {
        int n = arr.length;
        for (int i = 0; i < n - 1; i++) {
            boolean swapped = false;
            for (int j = 0; j < n - i - 1; j++) {
                if (arr[j] > arr[j + 1]) {
                    int temp = arr[j];
                    arr[j] = arr[j + 1];
                    arr[j + 1] = temp;
                    swapped = true;
                }
            }
            if (!swapped) {
                break;
            }
        }
    }
    
    // 选择排序
    public static void selectionSort(int[] arr) {
        int n = arr.length;
        for (int i = 0; i < n - 1; i++) {
            int minIndex = i;
            for (int j = i + 1; j < n; j++) {
                if (arr[j] < arr[minIndex]) {
                    minIndex = j;
                }
            }
            if (minIndex != i) {
                int temp = arr[i];
                arr[i] = arr[minIndex];
                arr[minIndex] = temp;
            }
        }
    }
    
    // 插入排序
    public static void insertionSort(int[] arr) {
        int n = arr.length;
        for (int i = 1; i < n; i++) {
            int key = arr[i];
            int j = i - 1;
            while (j >= 0 && arr[j] > key) {
                arr[j + 1] = arr[j];
                j--;
            }
            arr[j + 1] = key;
        }
    }
    
    // 希尔排序
    public static void shellSort(int[] arr) {
        int n = arr.length;
        int gap = n / 2;
        while (gap > 0) {
            for (int i = gap; i < n; i++) {
                int temp = arr[i];
                int j = i;
                while (j >= gap && arr[j - gap] > temp) {
                    arr[j] = arr[j - gap];
                    j -= gap;
                }
                arr[j] = temp;
            }
            gap /= 2;
        }
    }
    
    // 归并排序
    public static void mergeSort(int[] arr) {
        if (arr.length > 1) {
            int mid = arr.length / 2;
            int[] leftArr = Arrays.copyOfRange(arr, 0, mid);
            int[] rightArr = Arrays.copyOfRange(arr, mid, arr.length);
            mergeSort(leftArr);
            mergeSort(rightArr);
            int i = 0, j = 0, k = 0;
            while (i < leftArr.length && j < rightArr.length) {
                if (leftArr[i] < rightArr[j]) {
                    arr[k++] = leftArr[i++];
                } else {
                    arr[k++] = rightArr[j++];
                }
            }
            while (i < leftArr.length) {
                arr[k++] = leftArr[i++];
            }
            while (j < rightArr.length) {
                arr[k++] = rightArr[j++];
            }
        }
    }
    
    // 快速排序
    public static void quickSort(int[] arr) {
        quickSort(arr, 0, arr.length - 1);
    }
    
    private static void quickSort(int[] arr, int left, int right) {
        if (left < right) {
            int pivotIndex = partition(arr, left, right);
            quickSort(arr, left, pivotIndex - 1);
            quickSort(arr, pivotIndex + 1, right);
        }
    }
    
    private static int partition(int[] arr, int left, int right) {
        int pivot = arr[right];
        int i = left - 1;
        for (int j = left; j < right; j++) {
            if (arr[j] < pivot) {
                i++;
                int temp = arr[i];
                arr[i] = arr[j];
                arr[j] = temp;
            }
        }
        int temp = arr[i + 1];
        arr[i + 1] = arr[right];
        arr[right] = temp;
        return i + 1;
    }
    
    // 堆排序
    public static void heapSort(int[] arr) {
        int n = arr.length;
        for (int i = n / 2 - 1; i >= 0; i--) {
            heapify(arr, n, i);
        }
        for (int i = n - 1; i > 0; i--) {
            int temp = arr[0];
            arr[0] = arr[i];
            arr[i] = temp;
            heapify(arr, i, 0);
        }
    }
    
    private static void heapify(int[] arr, int n, int i) {
        int largest = i;
        int left = 2 * i + 1;
        int right = 2 * i + 2;
        if (left < n && arr[left] > arr[largest]) {
            largest = left;
        }
        if (right < n && arr[right] > arr[largest]) {
            largest = right;
        }
        if (largest != i) {
            int temp = arr[i];
            arr[i] = arr[largest];
            arr[largest] = temp;
            heapify(arr, n, largest);
        }
    }
    
}

输出结果：

Bubble Sort: 0ms
Selection Sort: 0ms
Insertion Sort: 0ms
Shell Sort: 1ms
Merge Sort: 4ms
Quick Sort: 2ms
Heap Sort: 2ms

可以看出，在有序数据情况下，各排序算法的表现都非常优秀，基本都可以在毫秒级别内完成排序。