Tempter of the Bone

本文介绍了一种基于回溯算法的迷宫逃逸问题解决方法。通过分析迷宫布局及移动限制,利用深度优先搜索(DFS)寻找在限定时间内到达出口的路径。文章详细解释了如何通过奇偶性减枝来优化搜索过程。

The doggie found a bone in an ancient maze, which fascinated him a lot. However, when he picked it up, the maze began to shake, and the doggie could feel the ground sinking. He realized that the bone was a trap, and he tried desperately to get out of this maze. 

The maze was a rectangle with sizes N by M. There was a door in the maze. At the beginning, the door was closed and it would open at the T-th second for a short period of time (less than 1 second). Therefore the doggie had to arrive at the door on exactly the T-th second. In every second, he could move one block to one of the upper, lower, left and right neighboring blocks. Once he entered a block, the ground of this block would start to sink and disappear in the next second. He could not stay at one block for more than one second, nor could he move into a visited block. Can the poor doggie survive? Please help him. 

Input

The input consists of multiple test cases. The first line of each test case contains three integers N, M, and T (1 < N, M < 7; 0 < T < 50), which denote the sizes of the maze and the time at which the door will open, respectively. The next N lines give the maze layout, with each line containing M characters. A character is one of the following: 

'X': a block of wall, which the doggie cannot enter; 
'S': the start point of the doggie; 
'D': the Door; or 
'.': an empty block. 

The input is terminated with three 0's. This test case is not to be processed. 

Output

For each test case, print in one line "YES" if the doggie can survive, or "NO" otherwise. 

Sample Input

4 4 5
S.X.
..X.
..XD
....
3 4 5
S.X.
..X.
...D
0 0 0

Sample Output

NO
YES
/*
用到了回溯和减枝 
*/
#include<stdio.h>
#include<string.h>
#include<math.h>
#include<queue>

using namespace std;

const int maxn = 10;
int vis[maxn][maxn];
char str[maxn][maxn];
int n, m, T, s_x, s_y, e_x, e_y;
bool flag;

int d[4][2] = {1, 0, -1, 0, 0, 1, 0, -1};


bool check(int x, int y){
    if(x >= 0 && x < n && y >= 0 && y < m)    
		return true;
    else return false;
}


void DFS(int x, int y, int t){
	if (str[x][y] == 'D' && t == T){
		flag = true;
		return;
	}
	vis[x][y] = 1;
	// 奇偶减枝  
	int temp = T - t - (abs(x-e_x)+abs(y-e_y));
// 总时间 要求时间 已用时间 最短距离所用时间 
    if(temp < 0 || (temp & 1))  return;
//如果temp小于0证明当前时间无法到达 return
//如果temp为奇数是不可能的 return	
	for (int i = 0; i < 4; i++){
		int dx = x + d[i][0];
		int dy = y + d[i][1];
		if (check(dx, dy) && !vis[dx][dy] && str[dx][dy] != 'X'){
			DFS(dx, dy, t+1);
			//回溯
			if (flag) return; 
			vis[dx][dy] = 0;
		}
	}
}

int main()
{
	while (scanf ("%d %d %d", &n, &m, &T) != EOF && (n || m || T)){
		for (int i = 0; i < n; i++){
			scanf ("%s", str[i]);
		}
		
		memset(vis, 0, sizeof(vis));
		flag = false;
		
		for (int i = 0; i < n; i++)
			for (int j = 0; j < m; j++){
				if (str[i][j] == 'S')
					s_x = i, s_y = j;
				if (str[i][j] == 'D')
					e_x = i, e_y = j;
			}	
		DFS (s_x, s_y, 0);
		if (flag) printf ("YES\n");
		else printf ("NO\n");
	}
	return 0;
}
/*
把矩阵看成如下形式: 
0 1 0 1 0 1 
1 0 1 0 1 0 
0 1 0 1 0 1 
1 0 1 0 1 0 
0 1 0 1 0 1 
从为 0 的格子走一步,必然走向为 1 的格子 。
从为 1 的格子走一步,必然走向为 0 的格子 。
即: 
从 0 走向 1 必然是奇数步,从 0 走向 0 必然是偶数步。

所以当遇到从 0 走向 0 但是要求时间是奇数的或者
 从 1 走向 0 但是要求时间是偶数的,都可以直接判断不可达!
比如一张地图c

 

S...  
....  
....  
....  
...D  
要求从S点到达D点,此时,从S到D的最短距离为s = abs ( dx - sx ) + abs ( dy - sy )。

如果地图中出现了不能经过的障碍物:

S..X  
XX.X  
...X  
.XXX  
...D  
此时的最短距离s' = s + 4,为了绕开障碍,不管偏移几个点,偏移的距离都是最短距离s加上一个偶数距离。

就如同上面说的矩阵,要求你从0走到0,无论你怎么绕,永远都是最短距离(偶数步)加上某个偶数步;
要求你从1走到0,永远只能是最短距离(奇数步)加上某个偶数步。
 
*/

 

内容概要:本文系统介绍了算术优化算法(AOA)的基本原理、核心思想及Python实现方法,并通过图像分割的实际案例展示了其应用价值。AOA是一种基于种群的元启发式算法,其核心思想来源于四则运算,利用乘除运算进行全局勘探,加减运算进行局部开发,通过数学优化器加速函数(MOA)和数学优化概率(MOP)动态控制搜索过程,在全局探索与局部开发之间实现平衡。文章详细解析了算法的初始化、勘探与开发阶段的更新策略,并提供了完整的Python代码实现,结合Rastrigin函数进行测试验证。进一步地,以Flask框架搭建前后端分离系统,将AOA应用于图像分割任务,展示了其在实际工程中的可行性与高效性。最后,通过收敛速度、寻优精度等指标评估算法性能,并提出自适应参数调整、模型优化和并行计算等改进策略。; 适合人群:具备一定Python编程基础和优化算法基础知识的高校学生、科研人员及工程技术人员,尤其适合从事人工智能、图像处理、智能优化等领域的从业者;; 使用场景及目标:①理解元启发式算法的设计思想与实现机制;②掌握AOA在函数优化、图像分割等实际问题中的建模与求解方法;③学习如何将优化算法集成到Web系统中实现工程化应用;④为算法性能评估与改进提供实践参考; 阅读建议:建议读者结合代码逐行调试,深入理解算法流程中MOA与MOP的作用机制,尝试在不同测试函数上运行算法以观察性能差异,并可进一步扩展图像分割模块,引入更复杂的预处理或后处理技术以提升分割效果。
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