C++模拟退火实现多点动态规划
时间: 2024-01-25 21:03:22 浏览: 28
C++模拟退火算法实现多点动态规划的代码如下所示:
```cpp
#include <iostream>
#include <cmath>
#include <vector>
#include <algorithm>
using namespace std;
// 计算两点之间的距离
double distance(pair<int, int> p1, pair<int, int> p2) {
int dx = p1.first - p2.first;
int dy = p1.second - p2.second;
return sqrt(dx * dx + dy * dy);
}
// 计算路径的总长度
double calculateDistance(vector<pair<int, int>>& points, vector<int>& path) {
double totalDistance = 0.0;
for (int i = 0; i < path.size() - 1; i++) {
totalDistance += distance(points[path[i]], points[path[i + 1]]);
}
return totalDistance;
}
// 模拟退火算法求解多点动态规划
vector<int> simulatedAnnealing(vector<pair<int, int>>& points) {
int n = points.size();
vector<int> currentPath(n);
for (int i = 0; i < n; i++) {
currentPath[i] = i;
}
random_shuffle(currentPath.begin(), currentPath.end());
double currentDistance = calculateDistance(points, currentPath);
double temperature = 1000.0;
double coolingRate = 0.999;
while (temperature > 0.1) {
vector<int> newPath = currentPath;
int swapIndex1 = rand() % n;
int swapIndex2 = rand() % n;
swap(newPath[swapIndex1], newPath[swapIndex2]);
double newDistance = calculateDistance(points, newPath);
double deltaDistance = newDistance - currentDistance;
if (deltaDistance < 0 || exp(-deltaDistance / temperature) > (double)rand() / RAND_MAX) {
currentPath = newPath;
currentDistance = newDistance;
}
temperature *= coolingRate;
}
return currentPath;
}
int main() {
vector<pair<int, int>> points = {{0, 0}, {1, 2}, {3, 4}, {5, 6}, {7, 8}};
vector<int> path = simulatedAnnealing(points);
cout << "Optimal path: ";
for (int i = 0; i < path.size(); i++) {
cout << path[i] << " ";
}
cout << endl;
cout << "Optimal distance: " << calculateDistance(points, path) << endl;
return 0;
}
```
这段代码实现了一个简单的多点动态规划问题的模拟退火算法。首先,我们定义了一个计算两点之间距离的函数`distance`,然后定义了一个计算路径总长度的函数`calculateDistance`。接下来,我们使用模拟退火算法来求解最优路径,其中包括初始化路径、计算当前路径长度、设置初始温度和冷却率等步骤。最后,我们输出了求解得到的最优路径和最优距离。
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