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选择排序从入门到精通——Java实现

2021-01-02
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01/02

选择排序

算法实现原理

1、选取出 n 条记录中最小的记录与第一条记录进行交换 —— 循环的第一趟
2、选取出除第一条记录以外的 n-1 条记录中最小的记录与第二条记录进行交换 —— 循环的第二趟
3、以此类推直到整个数组全部遍历排序完成。

与冒泡排序的对比

选择排序可以看成冒泡排序的改进版本
冒泡排序实际上是将数据从右至左排序完成(从右至左、从大到小进行交换排序),而快速排序是将数据从左到右排序完成(从左至右、从小到大进行交换排序),虽然选择排序相对于冒泡排序将交换次数从$O(n^2)$减少到了$O(n)$,但是比较次数还是保持$O(n^2)$

算法复杂度

比较次数 $O(n^2)$——比较次数与关键字的初始状态无关,总的比较次数 $N=(n-1)+(n-2)+...+1=n(n-1)/2$。

交换次数 $O(n)$——最好情况是,已经有序,交换 0 次;最坏情况交换 n-1 次,逆序交换 $n/2$ 次。

算法稳定性

选择排序是不稳定,当数据为2,3,2,1,4,5时就会出现第一次出现的2会被交换到第二次出现的2后面,这样就造成了排序并没有按照相同数据按照初始的顺序进行排序的要求。

然而冒泡排序却不会出现这样的问题,因为冒泡排序是相邻的两个元素进行比较交换,当数据相等时是不进行操作的所以没有影响相同数据的初始顺序,也就是说冒泡排序是稳定的。

算法Java实现

/**
     * @Author zhuangyan
     * @Description //TODO 选择排序
     * @Date 10:43 2020/3/9
     * @Param []
     * @return void
     **/
    public static void SelectSort(){
        int temp;//临时变量
        //创建新的数组
        int[] arr = new int[]{4,7,1,6,2,8,432,67,26,65,31765,765,5235,54756,643,52345,76};
        //进行排序
        for(int i = 0;i < arr.length-1;i++){
            int min = i;//选出"最小"下标(数据的第一条)
            for(int j = i+1;j < arr.length;j++){
                //寻找相对最小值对应下标
                if(arr[min]>arr[j]){
                    min = j;
                }
            }
            //如果寻找到最小值,将最小值与当前位置交换
            if(min != i){
                temp = arr[min];
                arr[min] = arr[i];
                arr[i] = temp;
            }
        }
        //输出
        System.out.print("选择排序:");
        for(int i : arr){
            System.out.print(i);
            System.out.print(" ");
        }
        System.out.println();
    }

优化

对于选择排序的优化,其实是对选择排序的深入理解的一个过程,相信看到了这里大家都知道了选择排序在每一次从左到右的循环周期内都是确定了一个最小的元素放置在前面,相信这时候大家已经想到了,既然从左到右可以选出最小的,那一定可以选出最大的。是的,本次优化的出发点也是从这一思想开始,不多说上代码。

    /**
     * @Author zhuangyan
     * @Description //TODO 选择排序优化
     * @Date 10:43 2020/3/9
     * @Param []
     * @return void
     **/
    public static void OptimizeSelectSort(){
        int temp;//临时变量
        //创建新的数组
        int[] arr = new int[]{4,7,1,6,2,8,432,67,26,65,31765,765,5235,54756,643,52345,76};
        int left = 0;//数据项的左侧下标
        int right = arr.length - 1;//数据项的右侧下标
        //进行排序
        while(left < right){
            int min = left;//初始化最小标兵
            int max = left;//初始化最大标兵
            //无需与本身进行比较无意义所以 i 从 left+1 开始计算
            for(int i = left + 1;i <= right;i++){
                if(arr[min] > arr[i]){
                    min = i;
                }
                if(arr[max] < arr[i]){
                    max = i;
                }
            }
            //选取出最小值与第一元素进行交换
            if(min != left){
                temp = arr[left];
                arr[left] = arr[min];
                arr[min] = temp;
            }
            //这一步很重要,最开始没有考虑到,导致测试用例不能100%
            //这一步的意义其实很简单,假如最大值在最左测,此时程序走到这里时
            //最大的值已经被上面的步骤替换到了原本最小值所对应的下标处
            //如果继续使用left作为最大值下标就会导致最小值被交换到后面
            if(max == left){
                max = min;
            }
            if(max != right){
                temp = arr[right];
                arr[right] = arr[max];
                arr[max] = temp;
            }
            //左侧指针右移(此处的指针是本人的类比,这样比较形象,其实是数组的下标)
            left++;
            //右侧指针左移(此处的指针是本人的类比,这样比较形象,其实是数组的下标)
            right--;
        }
        //输出
        System.out.print("选择排序优化:");
        for(int i : arr){
            System.out.print(i);
            System.out.print(" ");
        }
        System.out.println();
    }

具体代码详见github:https://github.com/zytwist/algorithm

Java数据结构排序算法
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  1. 李林超
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  2. Leefs
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}!}

  5. 吴萌
    2021-01-08

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