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C++实现的基本蚁群算法框架来解决一维套料的示例代码
蚁群算法(Ant Colony Optimization, ACO)是一种启发式搜索算法,灵感来源于真实蚂蚁寻找食物路径的行为。在解决一维套料问题时,蚁群算法可以用来寻找最优的切割模式,使得浪费的材料最少。下面是一个使用C++实现的蚁群算法框架来解决一维套料问题的示例代码:
```cpp
#include <iostream>
#include <vector>
#include <algorithm>
#include <map>
#include <random>
#include <cassert>
const int MAX_ITERATIONS = 100;
const double EVAPORATION_RATE = 0.5;
const double ALPHA = 1.0; // pheromone importance
const double BETA = 5.0; // heuristic information importance
const int NUM_ANTS = 10;
// Define the problem
const int RAW_LENGTH = 100; // length of raw material
const std::vector<int> LENGTHS = {10, 20, 30}; // lengths of pieces to cut
struct PheromoneTrail {
std::map<int, double> trail;
void init() {
for (int length : LENGTHS) {
trail[length] = 1.0; // initial pheromone value
}
}
void evaporate() {
for (auto &entry : trail) {
entry.second *= (1.0 - EVAPORATION_RATE);
}
}
};
class AntColony {
public:
AntColony() : pheromones(), rng(0, LENGTHS.size() - 1) {}
void solve() {
pheromones.init();
for (int iter = 0; iter < MAX_ITERATIONS; ++iter) {
std::vector<std::vector<int>> solutions;
for (int ant = 0; ant < NUM_ANTS; ++ant) {
std::vector<int> solution = ant->constructSolution();
solutions.push_back(solution);
updatePheromones(solution);
}
}
}
private:
PheromoneTrail pheromones;
std::default_random_engine engine;
std::uniform_int_distribution<int> rng;
std::vector<int> constructSolution() {
std::vector<int> solution;
int remainingLength = RAW_LENGTH;
while (remainingLength > 0) {
double total = 0.0;
std::vector<double> probabilities;
for (int length : LENGTHS) {
if (length <= remainingLength) {
double pheromone = pow(pheromones.trail[length], ALPHA);
double heuristic = pow(static_cast<double>(remainingLength) / length, BETA);
probabilities.push_back(pheromone * heuristic);
total += pheromone * heuristic;
} else {
probabilities.push_back(0);
}
}
assert(total > 0); // Ensure there's at least one possible move
// Normalize probabilities
std::transform(probabilities.begin(), probabilities.end(), probabilities.begin(),
[total](double p) { return p / total; });
// Select next piece
double r = static_cast<double>(rand()) / RAND_MAX;
int index = 0;
while (r > 0 && index < probabilities.size()) {
r -= probabilities[index++];
}
--index; // Adjust index because loop exits after incrementing
int length = LENGTHS[index];
solution.push_back(length);
remainingLength -= length;
}
return solution;
}
void updatePheromones(const std::vector<int>& solution) {
for (int length : solution) {
pheromones.trail[length] += 1.0 / solution.size();
}
pheromones.evaporate();
}
};
int main() {
AntColony colony;
colony.solve();
std::cout << "Solution not directly outputted here, as it depends on stochastic nature of algorithm." << std::endl;
return 0;
}
```
需要注意的是,蚁群算法是一种概率型算法,因此每次运行的结果可能会有所不同。此外,蚁群算法的效率和质量很大程度上依赖于参数的选择,包括信息素蒸发率、信息素重要度、启发式信息重要度等。在上面的代码中,这些参数都是常量,但在实践中,可能需要通过实验来微调这些参数以获得最佳效果。
此代码示例中的蚁群算法框架并没有直接输出最优解决方案,而是展示了如何通过构建解决方案并更新信息素来迭代求解问题。在实际应用中,你可能需要对算法进行扩展,以便能够跟踪和输出每一代中的最优解,并在算法结束时输出最终的最优解决方案。
此外,上述代码仅提供了一种基础的蚁群算法实现。在处理复杂问题时,可能还需要引入额外的策略,如精英蚂蚁系统(Elite Ant System, EAS),其中只允许找到最优解的蚂蚁在信息素更新阶段留下信息素,或者最大最小蚂蚁系统(Max-Min Ant System, MMAS),其中信息素浓度被限制在一个预设的范围内,以防止过早收敛。 |
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