POJ 1797 Heavy Transportation(二分+宽搜)

本文介绍了一种使用二分搜索结合广度优先搜索(BFS)算法来解决寻找两个点间最大载重路径的问题。该算法适用于城市规划场景,如确定从起点到终点的最大运输重量,确保所有经过的街道不会超过其最大允许载重。

Description

Background
Hugo Heavy is happy. After the breakdown of the Cargolifter project he can now expand business. But he needs a clever man who tells him whether there really is a way from the place his customer has build his giant steel crane to the place where it is needed on which all streets can carry the weight.
Fortunately he already has a plan of the city with all streets and bridges and all the allowed weights.Unfortunately he has no idea how to find the the maximum weight capacity in order to tell his customer how heavy the crane may become. But you surely know.

Problem
You are given the plan of the city, described by the streets (with weight limits) between the crossings, which are numbered from 1 to n. Your task is to find the maximum weight that can be transported from crossing 1 (Hugo’s place) to crossing n (the customer’s place). You may assume that there is at least one path. All streets can be travelled in both directions.
Input

The first line contains the number of scenarios (city plans). For each city the number n of street crossings (1 <= n <= 1000) and number m of streets are given on the first line. The following m lines contain triples of integers specifying start and end crossing of the street and the maximum allowed weight, which is positive and not larger than 1000000. There will be at most one street between each pair of crossings.
Output

The output for every scenario begins with a line containing “Scenario #i:”, where i is the number of the scenario starting at 1. Then print a single line containing the maximum allowed weight that Hugo can transport to the customer. Terminate the output for the scenario with a blank line.
Sample Input

1
3 3
1 2 3
1 3 4
2 3 5
Sample Output

Scenario #1:
4

题意:你有一辆卡车可以在城市之间运输,城市与城市之间有一个最大载重量。现在要你计算从城市1到城市n卡车最大载重量是多少。

题解:找一条路使得最小的道路载重量尽可能大。
二分+bfs
注意:每次输出答案后要输出一行空行

#include<iostream>
#include<cstdio>
#include<queue>
#include<cstring>
using namespace std;
const int maxn=1e6+10;
struct cc{
    int from,to,cost;
}es[maxn];
int first[maxn],nxt[maxn];
bool vis[1000+10];
int tot=0;
void build(int ff,int tt,int pp)
{
    es[++tot]=(cc){ff,tt,pp};
    nxt[tot]=first[ff];
    first[ff]=tot;
}
int n,m;
queue<int>q;
bool bfs(int mid)
{
    q.push(1);
    vis[1]=1;
    while(!q.empty())
    {
        int u=q.front();q.pop();
        if(u==n)
        {
            return 1;
        }
        for(int i=first[u];i;i=nxt[i])
        {
            int v=es[i].to;
            if(es[i].cost>=mid)
            {
                if(!vis[v])
                {
                    q.push(v);
                    vis[v]=1;
                }
            }
        }
    }
    return 0;
}
int main()
{
    int T;
    scanf("%d",&T);
    for(int k=1;k<=T;k++)
    {
        tot=0;
        memset(first,0,sizeof(first));
        memset(nxt,0,sizeof(nxt));
        scanf("%d%d",&n,&m);
        for(int i=1;i<=m;i++)
        {
            int x,y,z;
            scanf("%d%d%d",&x,&y,&z);
            build(x,y,z);
            build(y,x,z);
        }
        int l=0,r=1e9;
        int ans=0;
        while(l<=r)
        {
            int mid=(l+r)/2;
            memset(vis,0,sizeof(vis));
            while(!q.empty())
            {
                q.pop();
            }
            if(bfs(mid))
            {
                l=mid+1;
                ans=max(ans,mid);
            }
            else
            {
                r=mid-1;
            }
        }
        printf("Scenario #%d:\n",k);
        printf("%d\n",ans);
        printf("\n");
    }
    return 0;
}

(Kriging_NSGA2)克里金模型结合多目标遗传算法求最优因变量及对应的最佳自变量组合研究(Matlab代码实现)内容概要:本文介绍了克里金模型(Kriging)与多目标遗传算法NSGA-II相结合的方法,用于求解最优因变量及其对应的最佳自变量组合,并提供了完整的Matlab代码实现。该方法首先利用克里金模型构建高精度的代理模型,逼近复杂的非线性系统响应,减少计算成本;随后结合NSGA-II算法进行多目标优化,索帕累托前沿解集,从而获得多个最优折衷方案。文中详细阐述了代理模型构建、算法集成流程及参数设置,适用于工程设计、参数反演等复杂优化问题。此外,文档还展示了该方法在SCI一区论文中的复现应用,体现了其科学性与实用性。; 适合人群:具备一定Matlab编程基础,熟悉优化算法和数值建模的研究生、科研人员及工程技术人员,尤其适合从事仿真优化、实验设计、代理模型研究的相关领域工作者。; 使用场景及目标:①解决高计算成本的多目标优化问题,通过代理模型降低仿真次数;②在无法解析求导或函数高度非线性的情况下寻找最优变量组合;③复现SCI高水平论文中的优化方法,提升科研可信度与效率;④应用于工程设计、能源系统调度、智能制造等需参数优化的实际场景。; 阅读建议:建议读者结合提供的Matlab代码逐段理解算法实现过程,重点关注克里金模型的构建步骤与NSGA-II的集成方式,建议自行调整测试函数或实际案例验证算法性能,并配合YALMIP等工具包扩展优化求解能力。
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