Oil Deposits HDU - 1241

本文介绍了一种通过网格划分来探测地下油藏分布的方法。使用深度优先搜索(DFS)来确定不同油藏的数量,当两个相邻的油点被视为同一油藏的一部分时,这种方法尤为有效。网格中的每个点表示一个地块,'@'代表可能存在油藏,'*'则表示没有油藏。

题目

Oil Deposits

Time Limit: 2000/1000 MS (Java/Others)    Memory Limit: 65536/32768 K (Java/Others)
Total Submission(s): 30156    Accepted Submission(s): 17445


Problem Description
The GeoSurvComp geologic survey company is responsible for detecting underground oil deposits. GeoSurvComp works with one large rectangular region of land at a time, and creates a grid that divides the land into numerous square plots. It then analyzes each plot separately, using sensing equipment to determine whether or not the plot contains oil. A plot containing oil is called a pocket. If two pockets are adjacent, then they are part of the same oil deposit. Oil deposits can be quite large and may contain numerous pockets. Your job is to determine how many different oil deposits are contained in a grid. 
 

Input
The input file contains one or more grids. Each grid begins with a line containing m and n, the number of rows and columns in the grid, separated by a single space. If m = 0 it signals the end of the input; otherwise 1 <= m <= 100 and 1 <= n <= 100. Following this are m lines of n characters each (not counting the end-of-line characters). Each character corresponds to one plot, and is either `*', representing the absence of oil, or `@', representing an oil pocket.
 

Output
For each grid, output the number of distinct oil deposits. Two different pockets are part of the same oil deposit if they are adjacent horizontally, vertically, or diagonally. An oil deposit will not contain more than 100 pockets.
 

Sample Input
  
1 1 * 3 5 *@*@* **@** *@*@* 1 8 @@****@* 5 5 ****@ *@@*@ *@**@ @@@*@ @@**@ 0 0
 

Sample Output
  
0 1 2 2

思路

DFS求连通块,DFS经典应用题。。。

代码

#include <bits/stdc++.h>

using namespace std;
#define MAX_N 110
int R,C;
char maze[MAX_N][MAX_N];
void solve();
void dfs(int x, int y);

int main()
{
    int n, m;
    while (cin >> m >> n && m && n) {
		R = m; C = n;
		for (int i = 0; i < m; i++) {
			while(getchar() != '\n') continue;
			for (int j = 0; j < n; j++) {
				scanf("%c", &maze[i][j]);
			}
		}
		solve();
    }
    return 0;
}
void solve() {
	int res = 0;
	for (int i = 0; i < R; i++) {
		for (int j = 0; j < C; j++) {
			if (maze[i][j] == '@') {
				dfs(i, j);
				res++;
			}
		}
	}

	cout << res << endl;
}

//int dx[] = {-1, 0, 0, 1};
//int dy[] = { 0, 1,-1, 0};
void dfs(int x, int y) {
	maze[x][y] = '*';

	for (int dx = -1; dx <= 1; dx++) {
		for (int dy = -1; dy <= 1; dy++) {
			int nx = dx + x, ny = dy + y;
			if (nx >= 0 && nx < R && ny >= 0 && ny < C && maze[nx][ny] == '@') {
				dfs(nx, ny);
			}
		}
	}
}


【无人机】基于改进粒子群算法的无人机路径规划研究[和遗传算法、粒子群算法进行比较](Matlab代码实现)内容概要:本文围绕基于改进粒子群算法的无人机路径规划展开研究,重点探讨了在复杂环境中利用改进粒子群算法(PSO)实现无人机三维路径规划的方法,并将其与遗传算法(GA)、标准粒子群算法等传统优化算法进行对比分析。研究内容涵盖路径规划的多目标优化、避障策略、航路点约束以及算法收敛性和寻优能力的评估,所有实验均通过Matlab代码实现,提供了完整的仿真验证流程。文章还提到了多种智能优化算法在无人机路径规划中的应用比较,突出了改进PSO在收敛速度和全局寻优方面的优势。; 适合人群:具备一定Matlab编程基础和优化算法知识的研究生、科研人员及从事无人机路径规划、智能优化算法研究的相关技术人员。; 使用场景及目标:①用于无人机在复杂地形或动态环境下的三维路径规划仿真研究;②比较不同智能优化算法(如PSO、GA、蚁群算法、RRT等)在路径规划中的性能差异;③为多目标优化问题提供算法选型和改进思路。; 阅读建议:建议读者结合文中提供的Matlab代码进行实践操作,重点关注算法的参数设置、适应度函数设计及路径约束处理方式,同时可参考文中提到的多种算法对比思路,拓展到其他智能优化算法的研究与改进中。
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