套料遗传算法（Bin Packing Genetic Algorithm）
套料遗传算法（Bin Packing Genetic Algorithm）是一种用于解决套料问题的优化算法。套料问题是指将一组物品尽可能有效地放入一组容器中，使得容器的利用率最高。以下是一个简单的套料遗传算法的C++代码示例：
cpp
#include <iostream>
#include <vector>
#include <algorithm>
#include <random>
// 定义物品结构体
struct Item {
int id;
int size;
};
// 定义容器结构体
struct Bin {
int id;
int capacity;
std::vector<Item> items;
};
// 遗传算法参数
const int POPULATION采用SIZE = 100; // 种群大小
const int MAX采用GENERATIONS = 100; // 最大迭代次数
const double MUTATION采用RATE = 0.1; // 变异率
// 随机数生成器
std::random采用device rd;
std::mt19937 gen(rd());
std::uniform采用real采用distribution<> dis(0.0, 1.0);
// 计算容器利用率
double calculateFitness(const Bin& bin) {
int totalSize = 0;
for (const auto& item : bin.items) {
totalSize += item.size;
}
return static采用cast<double>(totalSize) / bin.capacity;
}
// 初始化种群
std::vector<Bin> initializePopulation(int numBins, const std::vector<Item>& items) {
std::vector<Bin> population;
for (int i = 0; i < POPULATION采用SIZE; ++i) {
Bin bin;
bin.id = i;
bin.capacity = numBins;
population.push采用back(bin);
}
return population;
}
// 选择操作
std::vector<Bin> selection(const std::vector<Bin>& population) {
std::vector<Bin> selectedPopulation;
for (int i = 0; i < POPULATION采用SIZE; ++i) {
int index1 = static采用cast<int>(dis(gen) * POPULATION采用SIZE);
int index2 = static采用cast<int>(dis(gen) * POPULATION采用SIZE);
selectedPopulation.push采用back(population.items.size() > population.items.size() ? population : population);
}
return selectedPopulation;
}
// 交叉操作
std::vector<Bin> crossover(const std::vector<Bin>& population) {
std::vector<Bin> offspringPopulation;
for (int i = 0; i < POPULATION采用SIZE; i += 2) {
Bin parent1 = population;
Bin parent2 = population;
Bin offspring1 = parent1;
Bin offspring2 = parent2;
int crossoverPoint = static采用cast<int>(dis(gen) * parent1.items.size());
offspring1.items.insert(offspring1.items.end(), parent2.items.begin() + crossoverPoint, parent2.items.end());
offspring2.items.insert(offspring2.items.end(), parent1.items.begin() + crossoverPoint, parent1.items.end());
offspringPopulation.push采用back(offspring1);
offspringPopulation.push采用back(offspring2);
}
return offspringPopulation;
}
// 变异操作
void mutation(std::vector<Bin>& population) {
for (auto& bin : population) {
for (auto& item : bin.items) {
if (dis(gen) < MUTATION采用RATE) {
item.size = static采用cast<int>(dis(gen) * bin.capacity);
}
}
}
}
// 打印最优解
void printBestSolution(const std::vector<Bin>& population) {
double bestFitness = 0.0;
const Bin* bestBin = nullptr;
for (const auto& bin : population) {
double fitness = calculateFitness(bin);
if (fitness > bestFitness) {
bestFitness = fitness;
bestBin = &bin;
}
}
std::cout << "Best solution: ";
if (bestBin != nullptr) {
for (const auto& item : bestBin->items) {
std::cout << item.id << " ";
}
}
std::cout << std::endl;
}
int main() {
// 初始化物品
std::vector<Item> items = {
{1, 10},
{2, 20},
{3, 30},
// ...
};
// 初始化种群
std::vector<Bin> population = initializePopulation(100, items);
// 迭代
for (int generation = 0; generation < MAX采用GENERATIONS; ++generation) {
// 选择
std::vector<Bin> selectedPopulation = selection(population);
// 交叉
std::vector<Bin> offspringPopulation = crossover(selectedPopulation);
// 变异
mutation(offspringPopulation);
// 更新种群
population = offspringPopulation;
// 输出最优解
printBestSolution(population);
}
return 0;
}
这段代码实现了一个简单的套料遗传算法，其中包括初始化种群、选择、交叉、变异等操作，并输出每一代的最优解。你可以根据实际需求进行修改和扩展。 #include <iostream>
#include <vector>
#include <algorithm>
#include <random>
// 定义材料的结构体
struct Material {
int length;
int demand;
};
// 定义染色体的结构体
struct Chromosome {
std::vector<int> genes;
int fitness;
};
// 初始化种群
std::vector<Chromosome> initializePopulation(int populationSize, int geneSize) {
std::vector<Chromosome> population;
for (int i = 0; i < populationSize; i++) {
Chromosome chromosome;
chromosome.genes.resize(geneSize);
for (int j = 0; j < geneSize; j++) {
chromosome.genes = rand() % 2; // 随机生成0或1
}
chromosome.fitness = 0;
population.push采用back(chromosome);
}
return population;
}
// 计算染色体的适应度
void calculateFitness(Chromosome& chromosome, const std::vector<Material>& materials, int targetLength) {
int totalLength = 0;
int totalDemand = 0;
for (int i = 0; i < chromosome.genes.size(); i++) {
if (chromosome.genes == 1) {
totalLength += materials.length;
totalDemand += materials.demand;
}
}
chromosome.fitness = (totalLength >= targetLength) ? totalDemand : 0;
}
// 选择操作
std::vector<Chromosome> selection(const std::vector<Chromosome>& population, int tournamentSize) {
std::vector<Chromosome> selectedPopulation;
for (int i = 0; i < population.size(); i++) {
std::vector<int> tournamentIndices;
for (int j = 0; j < tournamentSize; j++) {
int index = rand() % population.size();
tournamentIndices.push采用back(index);
}
int maxFitness = -1;
int maxIndex = -1;
for (int j = 0; j < tournamentIndices.size(); j++) {
int index = tournamentIndices;
if (population.fitness > maxFitness) {
maxFitness = population.fitness;
maxIndex = index;
}
}
selectedPopulation.push采用back(population);
}
return selectedPopulation;
}
// 交叉操作
std::vector<Chromosome> crossover(const std::vector<Chromosome>& population, double crossoverRate) {
std::vector<Chromosome> offspringPopulation;
for (int i = 0; i < population.size(); i += 2) {
Chromosome parent1 = population;
Chromosome parent2 = population;
Chromosome offspring1 = parent1;
Chromosome offspring2 = parent2;
if (rand() / double(RAND采用MAX) < crossoverRate) {
int crossoverPoint = rand() % parent1.genes.size();
for (int j = crossoverPoint; j < parent1.genes.size(); j++) {
offspring1.genes = parent2.genes;
offspring2.genes = parent1.genes;
}
}
offspringPopulation.push采用back(offspring1);
offspringPopulation.push采用back(offspring2);
}
return offspringPopulation;
}
// 变异操作
void mutation(std::vector<Chromosome>& population, double mutationRate) {
for (int i = 0; i < population.size(); i++) {
for (int j = 0; j < population.genes.size(); j++) {
if (rand() / double(RAND采用MAX) < mutationRate) {
population.genes = 1 - population.genes; // 变异
}
}
}
}
// 打印最优解
void printBestSolution(const std::vector<Chromosome>& population, const std::vector<Material>& materials, int targetLength) {
int maxFitness = -1;
int maxIndex = -1;
for (int i = 0; i < population.size(); i++) {
if (population.fitness > maxFitness) {
maxFitness = population.fitness;
maxIndex = i;
}
}
std::cout << "Best solution: ";
for (int i = 0; i < population.genes.size(); i++) {
if (population.genes == 1) {
std::cout << "Material " << i + 1 << " (Length: " << materials.length << ", Demand: " << materials.demand << ") ";
}
}
std::cout << std::endl;
std::cout << "Total Length: " << maxFitness << std::endl;
std::cout << "Target Length: " << targetLength << std::endl;
}
int main() {
// 定义材料和目标长度
std::vector<Material> materials = { {10, 5}, {15, 8}, {20, 10}, {25, 12} };
int targetLength = 50;
// 定义遗传算法的参数
int populationSize = 100;
int geneSize = materials.size();
int tournamentSize = 5;
double crossoverRate = 0.8;
double mutationRate = 0.01;
int maxGenerations = 100;
// 初始化种群
std::vector<Chromosome> population = initializePopulation(populationSize, geneSize);
// 迭代进化
for (int generation = 0; generation < maxGenerations; generation++) {
// 计算适应度
for (int i = 0; i < population.size(); i++) {
calculateFitness(population, materials, targetLength);
}
// 打印当前最优解
printBestSolution(population, materials, targetLength);
// 选择操作
std::vector<Chromosome> selectedPopulation = selection(population, tournamentSize);
// 交叉操作
std::vector<Chromosome> offspringPopulation = crossover(selectedPopulation, crossoverRate);
// 变异操作
mutation(offspringPopulation, mutationRate);
// 更新种群
population = offspringPopulation;
}
return 0;
}
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