i need a flow chart for each method!!!!

Ant.java

public class Ant {

protected int trailSize;

protected int trail[];

protected boolean visited[];

public Ant(int tourSize) {

this.trailSize = tourSize;

this.trail = new int[tourSize];

this.visited = new boolean[tourSize];

}

protected void visitCity(int currentIndex, int city) {

trail[currentIndex + 1] = city;

visited[city] = true;

}

protected boolean visited(int i) {

return visited[i];

}

protected double trailLength(double graph[][]) {

double length = graph[trail[trailSize - 1]][trail[0]];

for (int i = 0; i < trailSize - 1; i++) {

length += graph[trail[i]][trail[i + 1]];

}

return length;

}

protected void clear() {

for (int i = 0; i < trailSize; i++)

visited[i] = false;

}

}

AntColonyOptimization.java

public class AntColonyOptimization {

private double c = 1.0;

private double alpha = 1;

private double beta = 5;

private double evaporation = 0.5;

private double Q = 500;

private double antFactor = 0.8;

private double randomFactor = 0.01;

private int maxIterations = 1000;

private int numberOfCities;

private int numberOfAnts;

private double graph[][];

private double trails[][];

private List ants = new ArrayList<>();

private Random random = new Random();

private double probabilities[];

private int currentIndex;

private int[] bestTourOrder;

private double bestTourLength;

public AntColonyOptimization(int noOfCities) {

graph = generateRandomMatrix(noOfCities);

numberOfCities = graph.length;

numberOfAnts = (int) (numberOfCities * antFactor);

trails = new double[numberOfCities][numberOfCities];

probabilities = new double[numberOfCities];

IntStream.range(0, numberOfAnts).forEach(i -> ants.add(new Ant(numberOfCities)));

}

/**

* Generate initial solution

*/

public double[][] generateRandomMatrix(int n) {

double[][] randomMatrix = new double[n][n];

IntStream.range(0, n).forEach(

i -> IntStream.range(0, n).forEach(j -> randomMatrix[i][j] = Math.abs(random.nextInt(100) + 1)));

return randomMatrix;

}

/**

* Perform ant optimization

*/

public void startAntOptimization() {

IntStream.rangeClosed(1, 3).forEach(i -> {

System.out.println("Attempt #" + i);

solve();

});

}

/**

* Use this method to run the main logic

*/

public int[] solve() {

setupAnts();

clearTrails();

IntStream.range(0, maxIterations).forEach(i -> {

moveAnts();

updateTrails();

updateBest();

});

System.out.println("Best tour length: " + (bestTourLength - numberOfCities));

System.out.println("Best tour order: " + Arrays.toString(bestTourOrder));

return bestTourOrder.clone();

}

/**

* Prepare ants for the simulation

*/

private void setupAnts() {

IntStream.range(0, numberOfAnts).forEach(i -> {

ants.forEach(ant -> {

ant.clear();

ant.visitCity(-1, random.nextInt(numberOfCities));

});

});

currentIndex = 0;

}

/**

* At each iteration, move ants

*/

private void moveAnts() {

IntStream.range(currentIndex, numberOfCities - 1).forEach(i -> {

ants.forEach(ant -> ant.visitCity(currentIndex, selectNextCity(ant)));

currentIndex++;

});

}

/**

* Select next city for each ant

*/

private int selectNextCity(Ant ant) {

int t = random.nextInt(numberOfCities - currentIndex);

if (random.nextDouble() < randomFactor) {

OptionalInt cityIndex = IntStream.range(0, numberOfCities).filter(i -> i == t && !ant.visited(i))

.findFirst();

if (cityIndex.isPresent()) {

return cityIndex.getAsInt();

}

}

calculateProbabilities(ant);

double r = random.nextDouble();

double total = 0;

for (int i = 0; i < numberOfCities; i++) {

total += probabilities[i];

if (total >= r) {

return i;

}

}

throw new RuntimeException("There are no other cities");

}

/**

* Calculate the next city picks probabilites

*/

public void calculateProbabilities(Ant ant) {

int i = ant.trail[currentIndex];

double pheromone = 0.0;

for (int l = 0; l < numberOfCities; l++) {

if (!ant.visited(l)) {

pheromone += Math.pow(trails[i][l], alpha) * Math.pow(1.0 / graph[i][l], beta);

}

}

for (int j = 0; j < numberOfCities; j++) {

if (ant.visited(j)) {

probabilities[j] = 0.0;

} else {

double numerator = Math.pow(trails[i][j], alpha) * Math.pow(1.0 / graph[i][j], beta);

probabilities[j] = numerator / pheromone;

}

}

}

/**

* Update trails that ants used

*/

private void updateTrails() {

for (int i = 0; i < numberOfCities; i++) {

for (int j = 0; j < numberOfCities; j++) {

trails[i][j] *= evaporation;

}

}

for (Ant a : ants) {

double contribution = Q / a.trailLength(graph);

for (int i = 0; i < numberOfCities - 1; i++) {

trails[a.trail[i]][a.trail[i + 1]] += contribution;

}

trails[a.trail[numberOfCities - 1]][a.trail[0]] += contribution;

}

}

/**

* Update the best solution

*/

private void updateBest() {

if (bestTourOrder == null) {

bestTourOrder = ants.get(0).trail;

bestTourLength = ants.get(0).trailLength(graph);

}

for (Ant a : ants) {

if (a.trailLength(graph) < bestTourLength) {

bestTourLength = a.trailLength(graph);

bestTourOrder = a.trail.clone();

}

}

}

/**

* Clear trails after simulation

*/

private void clearTrails() {

IntStream.range(0, numberOfCities).forEach(i -> {

IntStream.range(0, numberOfCities).forEach(j -> trails[i][j] = c);

});

}

}

AntColonyTest.java

public class AntColonyTest {

public static void main(String[] args) {

AntColonyOptimization antColony = new AntColonyOptimization(21);

antColony.startAntOptimization();

}

}

Output

Attempt #1 Best tour length: 204.0 Best tour order: [10, 7, 0, 18, 14, 16, 13, 20, 4, 15, 11, 2, 19, 8, 3, 12, 17, 6, 1, 5, 9] Attempt #2 Best tour length: 204.0 Best tour order: [10, 7, 0, 18, 14, 16, 13, 20, 4, 15, 11, 2, 19, 8, 3, 12, 17, 6, 1, 5, 9] Attempt #3 Best tour length: 204.0 Best tour order: [10, 7, 0, 18, 14, 16, 13, 20, 4, 15, 11, 2, 19, 8, 3, 12, 17, 6, 1, 5, 9]