Tour - UVa 1347 dp

本文介绍了一种计算平面中给定点集的最短封闭旅行路径的方法。通过动态规划策略实现,确保路径始终沿x轴方向前进,适用于节省旅行成本的问题。

John Doe, a skilled pilot, enjoys traveling. While on vacation, he rents a small plane and starts visiting beautiful places. To save money, John must determine the shortest closed tour that connects his destinations. Each destination is represented by a point in the plane pi = < xiyi > . John uses the following strategy: he starts from the leftmost point, then he goes strictly left to right to the rightmost point, and then he goes strictly right back to the starting point. It is known that the points have distinct x -coordinates.

Write a program that, given a set of n points in the plane, computes the shortest closed tour that connects the points according to John's strategy.

Input 

The program input is from a text file. Each data set in the file stands for a particular set of points. For each set of points the data set contains the number of points, and the point coordinates in ascending order of the x coordinate. White spaces can occur freely in input. The input data are correct.

Output 

For each set of data, your program should print the result to the standard output from the beginning of a line. The tour length, a floating-point number with two fractional digits, represents the result.


Note: An input/output sample is in the table below. Here there are two data sets. The first one contains 3 points specified by their x and y coordinates. The second point, for example, has the x coordinate 2, and the ycoordinate 3. The result for each data set is the tour length, (6.47 for the first data set in the given example).

Sample Input 

3 
1 1
2 3
3 1
4 
1 1 
2 3
3 1
4 2

Sample Output 

6.47
7.89

题意:按x轴排序给出点,求从最左边的点到最右边的点,再回来后路径的最小值。

思路:看成两条线从左到右,dp[i][j]表示1-max(i,j)的点都经过后的最短路径。两条路径一定都是从x小的点向x大的点运动的,返回的情况会使结果更大。

AC代码如下:

#include<cstdio>
#include<cstring>
#include<algorithm>
#include<cmath>
using namespace std;
double dp[1010][1010],INF=1000000000;
int x[1010],y[1010];
double dis(int a,int b)
{ return sqrt((x[a]-x[b])*(x[a]-x[b])+(y[a]-y[b])*(y[a]-y[b]));
}
int main()
{ int n,i,j,k;
  while(~scanf("%d",&n))
  { for(i=1;i<=n;i++)
     scanf("%d%d",&x[i],&y[i]);
    for(i=1;i<=n;i++)
     for(j=1;j<=n;j++)
      dp[i][j]=INF;
    dp[1][1]=0;
    for(i=1;i<=n;i++)
     for(j=i;j<=n;j++)
     { dp[j][j]=min(dp[j][j],dp[i][j]+dis(i,j));
       dp[i][j+1]=min(dp[i][j+1],dp[i][j]+dis(j,j+1));
       dp[j][j+1]=min(dp[j][j+1],dp[i][j]+dis(i,j+1));
     }
    printf("%.2f\n",dp[n][n]);
  }
}



内容概要:本文介绍了一个基于冠豪猪优化算法(CPO)的无人机三维路径规划项目,利用Python实现了在复杂三维环境中为无人机规划安全、高效、低能耗飞行路径的完整解决方案。项目涵盖空间环境建模、无人机动力学约束、路径编码、多目标代价函数设计以及CPO算法的核心实现。通过体素网格建模、动态障碍物处理、路径平滑技术和多约束融合机制,系统能够在高维、密集障碍环境下快速搜索出满足飞行可行性、安全性与能效最优的路径,并支持在线重规划以适应动态环境变化。文中还提供了关键模块的代码示例,包括环境建模、路径评估和CPO优化流程。; 适合人群:具备一定Python编程基础和优化算法基础知识,从事无人机、智能机器人、路径规划或智能优化算法研究的相关科研人员与工程技术人员,尤其适合研究生及有一定工作经验的研发工程师。; 使用场景及目标:①应用于复杂三维环境下的无人机自主导航与避障;②研究智能优化算法(如CPO)在路径规划中的实际部署与性能优化;③实现多目标(路径最短、能耗最低、安全性最高)耦合条件下的工程化路径求解;④构建可扩展的智能无人系统决策框架。; 阅读建议:建议结合文中模型架构与代码示例进行实践运行,重点关注目标函数设计、CPO算法改进策略与约束处理机制,宜在仿真环境中测试不同场景以深入理解算法行为与系统鲁棒性。
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