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admin 发表于 2024-3-15 20:54:07

遗传算法 多目标 c++

#include <iostream>
#include <vector>
#include <algorithm>
#include <random>

// 定义染色体结构体
struct Chromosome {
    std::vector<int> genes;
    int fitness;

    Chromosome(int size) : genes(size), fitness(0) {
      std::random采用device rd;
      std::mt19937 gen(rd());
      std::uniform采用int采用distribution<> dis(0, 1);
      for (int& gene : genes) {
            gene = dis(gen);
      }
    }
};

// 定义比较函数，用于选择操作
bool compare(Chromosome& c1, Chromosome& c2) {
    return c1.fitness > c2.fitness;
}

// 计算适应度函数
int calculateFitness(const std::vector<Chromosome>& population) {
    int sum = 0;
    for (const Chromosome& c : population) {
      // 在这里根据具体的问题定义适应度函数
      // 这里只是作为示例，假设适应度函数为基因中1的个数
      int fitness = std::count采用if(c.genes.begin(), c.genes.end(), [](int g) { return g == 1; });
      sum += fitness;
      c.fitness = fitness;
    }
    return sum;
}

// 选择操作
std::vector<Chromosome> selection(const std::vector<Chromosome>& population, int size) {
    std::vector<Chromosome> selected;
    std::random采用device rd;
    std::mt19937 gen(rd());
    std::uniform采用int采用distribution<> dis(0, population.size() - 1);

    while (selected.size() < size) {
      int idx1 = dis(gen);
      int idx2 = dis(gen);
      Chromosome c1 = population;
      Chromosome c2 = population;

      // 使用比较函数进行选择
      if (compare(c1, c2)) {
            selected.push采用back(c1);
      } else {
            selected.push采用back(c2);
      }
    }

    return selected;
}

// 交叉操作
void crossover(const std::vector<Chromosome>& parents, std::vector<Chromosome>& offspring) {
    std::random采用device rd;
    std::mt19937 gen(rd());
    std::uniform采用int采用distribution<> dis(1, parents.genes.size() - 2);

    for (size采用t i = 0; i < parents.size() / 2; i++) {
      size采用t idx = i * 2;
      Chromosome parent1 = parents;
      Chromosome parent2 = parents;
      Chromosome child1, child2;

      size采用t crossoverPoint = dis(gen);
      std::vector<int> genes1(parent1.genes.begin(), parent1.genes.begin() + crossoverPoint);
      std::vector<int> genes2(parent2.genes.begin() + crossoverPoint, parent2.genes.end());

      child1.genes = genes1;
      child1.genes.insert(child1.genes.end(), genes2.begin(), genes2.end());
      child1.fitness = calculateFitness({ parent1, parent2 }) / 2;

      genes1 = parent2.genes.begin(), parent2.genes.begin() + crossoverPoint;
      genes2 = parent1.genes.begin() + crossoverPoint, parent1.genes.end();

      child2.genes = genes1;
      child2.genes.insert(child2.genes.end(), genes2.begin(), genes2.end());
      child2.fitness = calculateFitness({ parent1, parent2 }) / 2;

      offspring.push采用back(child1);
      offspring.push采用back(child2);
    }
}

// 变异操作
void mutation(std::vector<Chromosome>& population, double mutationRate) {
    std::random采用device rd;
    std::mt19937 gen(rd());
    std::uniform采用int采用distribution<> dis(0, 1);

    for (Chromosome& c : population) {
      for (int& gene : c.genes) {
            if (dis(gen) < mutationRate) {
                gene = 1 - gene;
            }
      }
    }
}

// 主函数
int main() {
    const int populationSize = 50;
    const int generationCount = 100;
    const double mutationRate = 0.01;
    const int tournamentSize = 4;
    const int elitismCount = 2;

    std::vector<Chromosome> population(populationSize);

    // 初始化种群
    for (Chromosome& c : population) {
      c = Chromosome(c.genes.size());
    }

    // 进行遗传算法优化
    for (int generation = 0; generation < generationCount; generation++) {
      // 计算种群的适应度
      int sumFitness = calculateFitness(population);

      // 选择操作
      std::vector<Chromosome> selected = selection(population, populationSize);

      // 交叉操作
      std::vector<Chromosome> offspring;
      crossover(selected, offspring);

      // 变异操作
      mutation(offspring, mutationRate);

      // 选择精英个体
      std::vector<Chromosome> elite(population.begin(), population.begin() + elitismCount);
      std::sort(population.begin() + elitismCount, population.end(), compare);
      std::vector<Chromosome> newPopulation(elite.begin(), elite.end());
      std::sort(offspring.begin(), offspring.end(), compare);
      std::copy采用n(offspring.begin(), populationSize - elitismCount, std::back采用inserter(newPopulation));

      population = newPopulation;
    }

    // 输出最优解
    std::cout << "Best solution found: " << std::endl;
    std::cout << "Fitness: " << population.fitness << std::endl;
    std::cout << "Genes: ";
    for (int gene : population.genes) {
      std::cout << gene << " ";
    }
    std::cout << std::endl;

    return 0;
}

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