APS的高级计划的遗传算法,java代码示例

以下是一个使用遗传算法求解TSP（旅行商问题）的Java代码示例：

import java.util.Random;

public class GeneticAlgorithm {
    private int populationSize;
    private int[][] population;
    private double[] fitness;
    private int[] bestTour;
    private double bestFitness;
    private Random random;
    private double mutationRate;

    public GeneticAlgorithm(int populationSize, int[][] initialPopulation, double mutationRate) {
        this.populationSize = populationSize;
        this.population = initialPopulation;
        this.fitness = new double[populationSize];
        this.random = new Random();
        this.mutationRate = mutationRate;
    }

    public void run(int numGenerations) {
        for (int i = 0; i < numGenerations; i++) {
            evaluateFitness();
            selectParents();
            recombine();
            mutate();
        }
    }

    public int[] getBestTour() {
        return bestTour;
    }

    public double getBestFitness() {
        return bestFitness;
    }

    private void evaluateFitness() {
        double sumFitness = 0;
        double minFitness = Double.POSITIVE_INFINITY;
        int minIndex = 0;
        for (int i = 0; i < populationSize; i++) {
            double fitnessValue = calculateFitness(population[i]);
            fitness[i] = fitnessValue;
            sumFitness += fitnessValue;
            if (fitnessValue < minFitness) {
                minFitness = fitnessValue;
                minIndex = i;
            }
        }
        bestTour = population[minIndex];
        bestFitness = minFitness;
    }

    private double calculateFitness(int[] tour) {
        // 计算TSP问题的路径长度作为适应度值
        double fitnessValue = 0;
        int n = tour.length;
        for (int i = 0; i < n - 1; i++) {
            int city1 = tour[i];
            int city2 = tour[i + 1];
            fitnessValue += distance(city1, city2);
        }
        int city1 = tour[n - 1];
        int city2 = tour[0];
        fitnessValue += distance(city1, city2);
        return fitnessValue;
    }

    private void selectParents() {
        // 轮盘赌选择
        double[] cumulativeFitness = new double[populationSize];
        double sumFitness = 0;
        for (int i = 0; i < populationSize; i++) {
            sumFitness += fitness[i];
            cumulativeFitness[i] = sumFitness;
        }
        int[] parent1 = null;
        int[] parent2 = null;
        for (int i = 0; i < populationSize; i++) {
            double randomValue = random.nextDouble() * sumFitness;
            for (int j = 0; j < populationSize; j++) {
                if (randomValue <= cumulativeFitness[j]) {
                    if (parent1 == null) {
                        parent1 = population[j];
                    } else {
                        parent2 = population[j];
                        break;
                    }
                }
            }
            if (parent2 != null) {
                break;
            }
        }
        crossover(parent1, parent2);
    }

    private void crossover(int[] parent1, int[] parent2) {
        // 顺序交叉算子
        int n = parent1.length;
        int[] child = new int[n];
        int startIndex = random.nextInt(n);
        int endIndex = random.nextInt(n);
        if (startIndex > endIndex) {
            int temp = startIndex;
            startIndex = endIndex;
            endIndex = temp;
        }
        for (int i = 0; i < n; i++) {
            child[i] = -1;
        }
        for (int i = startIndex; i <= endIndex; i++) {
            child[i] = parent1[i];
        }
        int index = endIndex + 1;
        for (int i = 0; i < n; i++) {
            if (index == n) {
                index = 0;
            }
            int city = parent2[i];
            if (!contains(child, city)) {
                child[index] = city;
                index++;
            }
        }
        population[random.nextInt(populationSize)] = child;
    }

    private void mutate() {
        // 交换变异算子
        for (int i = 0; i < populationSize; i++) {
            if (random.nextDouble() < mutationRate) {
                int n = population[i].length;
                int index1 = random.nextInt(n);
                int index2 = random.nextInt(n);
                int temp = population[i][index1];
                population[i][index1] = population[i][index2];
                population[i][index2] = temp;
            }
        }
    }

    private void recombine() {
        // 保留最优个体，其他个体按概率进行选择复制
        int[][] newPopulation = new int[populationSize][];
        double sumFitness = 0;
        for (int i = 0; i < populationSize; i++) {
            sumFitness += fitness[i];
        }
        double[] probabilities = new double[populationSize];
        for (int i = 0; i < populationSize; i++) {
            probabilities[i] = fitness[i] / sumFitness;
        }
        int index = 0;
        for (int i = 0; i < populationSize; i++) {
            if (random.nextDouble() < probabilities[i]) {
                newPopulation[index] = population[i];
                index++;
            }
            if (index == populationSize - 1) {
                break;
            }
        }
        newPopulation[populationSize - 1] = bestTour;
        population = newPopulation;
    }

    private boolean contains(int[] array, int value) {
        for (int i = 0; i < array.length; i++) {
            if (array[i] == value) {
                return true;
            }
        }
        return false;
    }

    private double distance(int city1, int city2) {
        // TSP问题的距离函数
        // ...
    }
}

在这个例子中，我们使用顺序交叉算子和交换变异算子进行遗传算法的操作。其中，evaluateFitness()函数计算每个个体的适应度值，selectParents()函数使用轮盘赌选择两个父代个体，crossover()函数对两个父代个体进行交叉操作，mutate()函数对所有个体进行变异操作，recombine()函数选择一部分个体进行复制，保留最优个体。最终我们可以得到最优的路径和路径长度。