用Java编写退火算法代码

很高兴能够帮您回答这个问题！退火算法是一种随机优化算法，用于在大规模的搜索空间中找到最优解。以下是一些使用Java编写退火算法的代码示例：

1.基本的退火算法：

public class SimulatedAnnealing {
  public static void main(String[] args) {
    // Initialize variables
    double temperature = 100000;
    double coolingRate = 0.003;
    int[] currentSolution = {0, 1, 2, 3, 4, 5, 6, 7};
    int[] bestSolution = {0, 1, 2, 3, 4, 5, 6, 7};

    // Loop until system has cooled
    while (temperature > 1) {
      // Create new neighbor solution
      int[] newSolution = Arrays.copyOf(currentSolution, currentSolution.length);
      int pos1 = (int) (newSolution.length * Math.random());
      int pos2 = (int) (newSolution.length * Math.random());
      int temp = newSolution[pos1];
      newSolution[pos1] = newSolution[pos2];
      newSolution[pos2] = temp;

      // Get energy for new solution
      int currentEnergy = calculateEnergy(currentSolution);
      int neighborEnergy = calculateEnergy(newSolution);

      // Decide if we should accept the new solution
      if (acceptanceProbability(currentEnergy, neighborEnergy, temperature) > Math.random()) {
        currentSolution = newSolution;
      }

      // Keep track of the best solution found
      if (calculateEnergy(currentSolution) < calculateEnergy(bestSolution)) {
        bestSolution = currentSolution;
      }

      // Cool system
      temperature *= 1 - coolingRate;
    }

    // Print results
    System.out.println("Best solution found: " + Arrays.toString(bestSolution));
    System.out.println("Energy: " + calculateEnergy(bestSolution));
  }

  // Calculate the energy of the solution
  public static int calculateEnergy(int[] solution) {
    int count = 0;
    for (int i = 0; i < solution.length - 1; i++) {
      for (int j = i + 1; j < solution.length; j++) {
        if (solution[i] == solution[j] || Math.abs(solution[i] - solution[j]) == j - i) {
          count++;
        }
      }
    }
    return count;
  }

  // Calculate the acceptance probability of a new solution
  public static double acceptanceProbability(int currentEnergy, int neighborEnergy, double temperature) {
    if (neighborEnergy < currentEnergy) {
      return 1.0;
    }
    return Math.exp((currentEnergy - neighborEnergy) / temperature);
  }
}

2.带有自适应温度的退火算法：

public class AdaptiveSimulatedAnnealing {
  public static void main(String[] args) {
    // Initialize variables
    double temperature = 100000;
    double coolingRate = 0.003;
    double alpha = 0.95;
    double delta = 0.001;
    int[] currentSolution = {0, 1, 2, 3, 4, 5, 6, 7};
    int[] bestSolution = {0, 1, 2, 3, 4, 5, 6, 7};

    // Loop until system has cooled
    while (temperature > 1) {
      // Create new neighbor solution
      int[] newSolution = Arrays.copyOf(currentSolution, currentSolution.length);
      int pos1 = (int) (newSolution.length * Math.random());
      int pos2 = (int) (newSolution.length * Math.random());
      int temp = newSolution[pos1];
      newSolution[pos1] = newSolution[pos2];
      newSolution[pos2] = temp;

      // Get energy for new solution
      int currentEnergy = calculateEnergy(currentSolution);
      int neighborEnergy = calculateEnergy(newSolution);

      // Decide if we should accept the new solution
      if (acceptanceProbability(currentEnergy, neighborEnergy, temperature) > Math.random()) {
        currentSolution = newSolution;
      }

      // Keep track of the best solution found
      if (calculateEnergy(currentSolution) < calculateEnergy(bestSolution)) {
        bestSolution = currentSolution;
      }

      // Adjust temperature
      if (Math.abs(acceptanceProbability(currentEnergy, neighborEnergy, temperature) - alpha) < delta) {
        temperature *= alpha;
      } else {
        temperature /= alpha;
      }
    }

    // Print results
    System.out.println("Best solution found: " + Arrays.toString(bestSolution));
    System.out.println("Energy: " + calculateEnergy(bestSolution));
  }

  // Calculate the energy of the solution
  public static int calculateEnergy(int[] solution) {
    int count = 0;
    for (int i = 0; i < solution.length - 1; i++) {
      for (int j = i + 1; j < solution.length; j++) {
        if (solution[i] == solution[j] || Math.abs(solution[i] - solution[j]) == j - i) {
          count++;
        }
      }
    }
    return count;
  }

  // Calculate the acceptance probability of a new solution
  public static double acceptanceProbability(int currentEnergy, int neighborEnergy, double temperature) {
    if (neighborEnergy < currentEnergy) {
      return 1.0;
    }
    return Math.exp((currentEnergy - neighborEnergy) / temperature);
  }
}

希望能够帮到您！