java使用蚁群算法解决旅行商问题_蚁群旅行商java 实现

java使用蚁群算法解决旅行商问题示例

import java.util.ArrayList;
import java.util.List;
import java.util.Random;

public class AntColonyOptimization {

    private static final int NUM_ANTS = 10; // 蚂蚁数量
    private static final int NUM_ITERATIONS = 100; // 迭代次数
    private static final double ALPHA = 1.0; // 信息素重要程度
    private static final double BETA = 2.0; // 启发因子重要程度
    private static final double RHO = 0.5; // 信息素挥发速度
    private static final double Q = 100; // 信息素释放强度

    private int numCities; // 城市数量
    private double[][] distances; // 城市之间距离
    private double[][] pheromones; // 信息素浓度
    private List<Ant> ants; // 蚂蚁列表
    private Random random; // 随机数生成器

    public AntColonyOptimization(int numCities, double[][] distances) {
        this.numCities = numCities;
        this.distances = distances;
        this.pheromones = new double[numCities][numCities];
        this.ants = new ArrayList<>();
        this.random = new Random();
    }

    // 优化方法
    public void optimize() {
        initializePheromones();
        for (int i = 0; i < NUM_ITERATIONS; i++) {
            initializeAnts();
            for (Ant ant : ants) {
                ant.visitCities();
                ant.updatePheromones();
            }
            updatePheromones();
        }
    }

    // 初始化信息素浓度
    private void initializePheromones() {
        for (int i = 0; i < numCities; i++) {
            for (int j = 0; j < numCities; j++) {
                pheromones[i][j] = 1.0;
            }
        }
    }

    // 初始化蚂蚁
    private void initializeAnts() {
        ants.clear();
        for (int i = 0; i < NUM_ANTS; i++) {
            ants.add(new Ant(numCities));
        }
    }

    // 更新信息素浓度
    private void updatePheromones() {
        for (int i = 0; i < numCities; i++) {
            for (int j = 0; j < numCities; j++) {
                pheromones[i][j] *= (1 - RHO);
            }
        }
        for (Ant ant : ants) {
            double tourLength = ant.getTourLength();
            int[] tour = ant.getTour();
            for (int i = 0; i < numCities - 1; i++) {
                int from = tour[i];
                int to = tour[i + 1];
                pheromones[from][to] += Q / tourLength;
                pheromones[to][from] += Q / tourLength;
            }
        }
    }

    // 蚂蚁类
    private class Ant {
        private int numCities; // 城市数量
        private int[] tour; // 蚂蚁的路径
        private boolean[] visited; // 访问过的城市
        private double tourLength; // 路径长度

        public Ant(int numCities) {
            this.numCities = numCities;
            this.tour = new int[numCities];
            this.visited = new boolean[numCities];
            this.tourLength = 0;
        }

        // 访问城市
        public void visitCities() {
            tour[0] = random.nextInt(numCities);
            visited[tour[0]] = true;
            for (int i = 1; i < numCities; i++) {
                int from = tour[i - 1];
                int next = selectNextCity(from);
                tour[i] = next;
                visited[next] = true;
                tourLength += distances[from][next];
            }
            tourLength += distances[tour[numCities - 1]][tour[0]]; // 回到起点
        }

        // 更新信息素浓度
        public void updatePheromones() {
            // 更新信息素在蚂蚁行走路径上的浓度
        }

        // 选择下一个要访问的城市
        private int selectNextCity(int from) {
            // 根据信息素浓度和启发因子来决定
            return 0;
        }

        // 获取蚂蚁的路径
        public int[] getTour() {
            return tour;
        }

        // 获取路径长度
        public double getTourLength() {
            return tourLength;
        }
    }

    public static void main(String[] args) {
        int numCities = 5;
        double[][] distances = {
            {0, 1, 2, 3, 4},
            {1, 0, 1, 2, 3},
            {2, 1, 0, 1, 2},
            {3, 2, 1, 0, 1},
            {4, 3, 2, 1, 0}
        };
        AntColonyOptimization aco = new AntColonyOptimization(numCities, distances);
        aco.optimize();
    }
}

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这是一个简单的蚁群算法的实现，用于解决旅行商问题。这只是一个简单的示例代码，实际应用中需要根据具体情况进行调整和优化。