hdoj 1087Super Jumping! Jumping! Jumping!【dp】

本文介绍了一款名为“SuperJumping!Jumping!Jumping!”的游戏算法实现,玩家需从起点跳跃至终点,途中经过的棋子数值总和即为得分。通过动态规划算法求解最大得分路径。

Super Jumping! Jumping! Jumping!

Time Limit: 2000/1000 MS (Java/Others)    Memory Limit: 65536/32768 K (Java/Others)
Total Submission(s): 28894    Accepted Submission(s): 12906


Problem Description
Nowadays, a kind of chess game called “Super Jumping! Jumping! Jumping!” is very popular in HDU. Maybe you are a good boy, and know little about this game, so I introduce it to you now.



The game can be played by two or more than two players. It consists of a chessboard(棋盘)and some chessmen(棋子), and all chessmen are marked by a positive integer or “start” or “end”. The player starts from start-point and must jumps into end-point finally. In the course of jumping, the player will visit the chessmen in the path, but everyone must jumps from one chessman to another absolutely bigger (you can assume start-point is a minimum and end-point is a maximum.). And all players cannot go backwards. One jumping can go from a chessman to next, also can go across many chessmen, and even you can straightly get to end-point from start-point. Of course you get zero point in this situation. A player is a winner if and only if he can get a bigger score according to his jumping solution. Note that your score comes from the sum of value on the chessmen in you jumping path.
Your task is to output the maximum value according to the given chessmen list.
 

Input
Input contains multiple test cases. Each test case is described in a line as follow:
N value_1 value_2 …value_N 
It is guarantied that N is not more than 1000 and all value_i are in the range of 32-int.
A test case starting with 0 terminates the input and this test case is not to be processed.
 

Output
For each case, print the maximum according to rules, and one line one case.
 

Sample Input
3 1 3 2 4 1 2 3 4 4 3 3 2 1 0
 

Sample Output
4 10 3
最后的遍历很重要, 之前忘加上了,  老是输出不对.....

#include<cstdio>
#include<cstring>
#include<algorithm>
using namespace std;
int dp[10020],a[1010];
int main()
{
	int n;
	while(scanf("%d", &n)==1,n)
	{
		int i,j,k;
		for(i = 1; i <= n; i++)
		{
			scanf("%d", &a[i]);
			dp[i] = a[i];
		}
		for(i = 2; i <= n; i++)
		{
			for(j = 1; j < i; j++)
			{
				if(a[i] > a[j] )
				dp[i] = max(dp[i], dp[j]+a[i]);
			}
		}
		int ans = dp[1];
		for(i = 1; i<= n; i++)
		{
			if(ans < dp[i])
				ans = dp[i];
		}
		printf("%d\n", ans);
	}
	return 0;
}




内容概要:本文介绍了一个基于MATLAB实现的无人机三维路径规划项目,采用蚁群算法(ACO)与多层感知机(MLP)相结合的混合模型(ACO-MLP)。该模型通过三维环境离散化建模,利用ACO进行全局路径搜索,并引入MLP对环境特征进行自适应学习与启发因子优化,实现路径的动态调整与多目标优化。项目解决了高维空间建模、动态障碍规避、局部最优陷阱、算法实时性及多目标权衡等关键技术难题,结合并行计算与参数自适应机制,提升了路径规划的智能性、安全性和工程适用性。文中提供了详细的模型架构、核心算法流程及MATLAB代码示例,涵盖空间建模、信息素更新、MLP训练与融合优化等关键步骤。; 适合人群:具备一定MATLAB编程基础,熟悉智能优化算法与神经网络的高校学生、科研人员及从事无人机路径规划相关工作的工程师;适合从事智能无人系统、自动驾驶、机器人导航等领域的研究人员; 使用场景及目标:①应用于复杂三维环境下的无人机路径规划,如城市物流、灾害救援、军事侦察等场景;②实现飞行安全、能耗优化、路径平滑与实时避障等多目标协同优化;③为智能无人系统的自主决策与环境适应能力提供算法支持; 阅读建议:此资源结合理论模型与MATLAB实践,建议读者在理解ACO与MLP基本原理的基础上,结合代码示例进行仿真调试,重点关注ACO-MLP融合机制、多目标优化函数设计及参数自适应策略的实现,以深入掌握混合智能算法在工程中的应用方法。
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