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linyiqun
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ACO蚁群算法工具类
ACO蚁群算法工具类
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‎Others/DataMining_ACO/ACOTool.java

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package DataMining_ACO;
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import java.io.BufferedReader;
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import java.io.File;
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import java.io.FileReader;
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import java.io.IOException;
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import java.text.MessageFormat;
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import java.util.ArrayList;
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import java.util.Collections;
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import java.util.HashMap;
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import java.util.Map;
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import java.util.Random;
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/**
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* 蚁群算法工具类
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*
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* @author lyq
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*
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*/
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public class ACOTool {
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// 输入数据类型
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public static final int INPUT_CITY_NAME = 1;
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public static final int INPUT_CITY_DIS = 2;
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// 城市间距离邻接矩阵
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public static double[][] disMatrix;
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// 当前时间
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public static int currentTime;
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// 测试数据地址
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private String filePath;
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// 蚂蚁数量
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private int antNum;
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// 控制参数
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private double alpha;
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private double beita;
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private double p;
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private double Q;
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// 随机数产生器
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private Random random;
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// 城市名称集合,这里为了方便，将城市用数字表示
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private ArrayList<String> totalCitys;
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// 所有的蚂蚁集合
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private ArrayList<Ant> totalAnts;
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// 城市间的信息素浓度矩阵，随着时间的增多而减少
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private double[][] pheromoneMatrix;
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// 目标的最短路径,顺序为从集合的前部往后挪动
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private ArrayList<String> bestPath;
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// 信息素矩阵存储图,key采用的格式(i,j,t)->value
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private Map<String, Double> pheromoneTimeMap;
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public ACOTool(String filePath, int antNum, double alpha, double beita,
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double p, double Q) {
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this.filePath = filePath;
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this.antNum = antNum;
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this.alpha = alpha;
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this.beita = beita;
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this.p = p;
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this.Q = Q;
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this.currentTime = 0;
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readDataFile();
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}
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/**
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* 从文件中读取数据
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*/
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private void readDataFile() {
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File file = new File(filePath);
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ArrayList<String[]> dataArray = new ArrayList<String[]>();
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try {
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BufferedReader in = new BufferedReader(new FileReader(file));
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String str;
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String[] tempArray;
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while ((str = in.readLine()) != null) {
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tempArray = str.split(" ");
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dataArray.add(tempArray);
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}
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in.close();
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} catch (IOException e) {
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e.getStackTrace();
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}
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int flag = -1;
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int src = 0;
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int des = 0;
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int size = 0;
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// 进行城市名称种数的统计
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this.totalCitys = new ArrayList<>();
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for (String[] array : dataArray) {
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if (array[0].equals("#") && totalCitys.size() == 0) {
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flag = INPUT_CITY_NAME;
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continue;
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} else if (array[0].equals("#") && totalCitys.size() > 0) {
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size = totalCitys.size();
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// 初始化距离矩阵
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this.disMatrix = new double[size + 1][size + 1];
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this.pheromoneMatrix = new double[size + 1][size + 1];
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// 初始值-1代表此对应位置无值
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for (int i = 0; i < size; i++) {
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for (int j = 0; j < size; j++) {
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this.disMatrix[i][j] = -1;
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this.pheromoneMatrix[i][j] = -1;
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}
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}
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flag = INPUT_CITY_DIS;
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continue;
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}
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if (flag == INPUT_CITY_NAME) {
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this.totalCitys.add(array[0]);
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} else {
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src = Integer.parseInt(array[0]);
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des = Integer.parseInt(array[1]);
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this.disMatrix[src][des] = Double.parseDouble(array[2]);
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this.disMatrix[des][src] = Double.parseDouble(array[2]);
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}
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}
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}
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/**
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* 计算从蚂蚁城市i到j的概率
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*
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* @param cityI
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* 城市I
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* @param cityJ
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* 城市J
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* @param currentTime
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* 当前时间
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* @return
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*/
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private double calIToJProbably(String cityI, String cityJ, int currentTime) {
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double pro = 0;
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double n = 0;
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double pheromone;
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int i;
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int j;
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i = Integer.parseInt(cityI);
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j = Integer.parseInt(cityJ);
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pheromone = getPheromone(currentTime, cityI, cityJ);
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n = 1.0 / disMatrix[i][j];
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if (pheromone == 0) {
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pheromone = 1;
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}
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pro = Math.pow(n, alpha) * Math.pow(pheromone, beita);
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return pro;
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}
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/**
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* 计算综合概率蚂蚁从I城市走到J城市的概率
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*
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* @return
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*/
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public String selectAntNextCity(Ant ant, int currentTime) {
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double randomNum;
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double tempPro;
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// 总概率指数
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double proTotal;
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String nextCity = null;
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ArrayList<String> allowedCitys;
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// 各城市概率集
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double[] proArray;
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// 如果是刚刚开始的时候，没有路过任何城市，则随机返回一个城市
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if (ant.currentPath.size() == 0) {
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nextCity = String.valueOf(random.nextInt(totalCitys.size()) + 1);
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return nextCity;
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} else if (ant.nonVisitedCitys.isEmpty()) {
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// 如果全部遍历完毕，则再次回到起点
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nextCity = ant.currentPath.get(0);
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return nextCity;
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}
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proTotal = 0;
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allowedCitys = ant.nonVisitedCitys;
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proArray = new double[allowedCitys.size()];
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for (int i = 0; i < allowedCitys.size(); i++) {
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nextCity = allowedCitys.get(i);
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proArray[i] = calIToJProbably(ant.currentPos, nextCity, currentTime);
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proTotal += proArray[i];
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}
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for (int i = 0; i < allowedCitys.size(); i++) {
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// 归一化处理
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proArray[i] /= proTotal;
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}
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// 用随机数选择下一个城市
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randomNum = random.nextInt(100) + 1;
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randomNum = randomNum / 100;
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// 因为1.0是无法判断到的，,总和会无限接近1.0取为0.99做判断
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if (randomNum == 1) {
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randomNum = randomNum - 0.01;
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}
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tempPro = 0;
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// 确定区间
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for (int j = 0; j < allowedCitys.size(); j++) {
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if (randomNum > tempPro && randomNum <= tempPro + proArray[j]) {
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// 采用拷贝的方式避免引用重复
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nextCity = allowedCitys.get(j);
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break;
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} else {
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tempPro += proArray[j];
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}
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}
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return nextCity;
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}
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/**
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* 获取给定时间点上从城市i到城市j的信息素浓度
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*
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* @param t
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* @param cityI
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* @param cityJ
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* @return
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*/
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private double getPheromone(int t, String cityI, String cityJ) {
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double pheromone = 0;
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String key;
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// 上一周期需将时间倒回一周期
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key = MessageFormat.format("{0},{1},{2}", cityI, cityJ, t);
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if (pheromoneTimeMap.containsKey(key)) {
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pheromone = pheromoneTimeMap.get(key);
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}
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return pheromone;
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}
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/**
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* 每轮结束，刷新信息素浓度矩阵
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*
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* @param t
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*/
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private void refreshPheromone(int t) {
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double pheromone = 0;
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// 上一轮周期结束后的信息素浓度，丛信息素浓度图中查找
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double lastTimeP = 0;
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// 本轮信息素浓度增加量
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double addPheromone;
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String key;
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for (String i : totalCitys) {
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for (String j : totalCitys) {
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if (!i.equals(j)) {
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// 上一周期需将时间倒回一周期
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key = MessageFormat.format("{0},{1},{2}", i, j, t - 1);
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if (pheromoneTimeMap.containsKey(key)) {
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lastTimeP = pheromoneTimeMap.get(key);
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} else {
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lastTimeP = 0;
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}
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addPheromone = 0;
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for (Ant ant : totalAnts) {
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// 每只蚂蚁传播的信息素为控制因子除以距离总成本
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addPheromone += Q / ant.calSumDistance();
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}
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// 将上次的结果值加上递增的量，并存入图中
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pheromone = p * lastTimeP + addPheromone;
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key = MessageFormat.format("{0},{1},{2}", i, j, t);
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pheromoneTimeMap.put(key, pheromone);
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}
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}
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}
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}
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public void antStartSearching() {
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// 蚁群寻找的总次数
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int loopCount = 0;
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// 选中的下一个城市
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String selectedCity = "";
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pheromoneTimeMap = new HashMap<String, Double>();
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totalAnts = new ArrayList<>();
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random = new Random();
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while (loopCount < 10) {
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initAnts();
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while (true) {
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for (Ant ant : totalAnts) {
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selectedCity = selectAntNextCity(ant, currentTime);
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ant.goToNextCity(selectedCity);
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}
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// 如果已经遍历完所有城市，则跳出此轮循环
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if (totalAnts.get(0).isBack()) {
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break;
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}
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}
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// 周期时间叠加
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currentTime++;
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refreshPheromone(currentTime);
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}
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// 根据距离成本，选出所花距离最短的一个路径
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Collections.sort(totalAnts);
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bestPath = totalAnts.get(0).currentPath;
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for (String cityName : bestPath) {
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System.out.println(MessageFormat.format("-->{0}", cityName));
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}
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}
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/**
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* 初始化蚁群操作
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*/
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private void initAnts() {
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Ant tempAnt;
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ArrayList<String> nonVisitedCitys;
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totalAnts.clear();
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// 初始化蚁群
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for (int i = 0; i < antNum; i++) {
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nonVisitedCitys = (ArrayList<String>) totalCitys.clone();
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tempAnt = new Ant(pheromoneMatrix, nonVisitedCitys);
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totalAnts.add(tempAnt);
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}
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}
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}

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