ACO refactor
@@ -18,8 +18,8 @@ public class AntColonyOptimization {
private int maxIterations = 1000;
- public int numberOfCities;
- public int numberOfAnts;
+ private int numberOfCities;
+ private int numberOfAnts;
private double graph[][];
private double trails[][];
private List<Ant> ants = new ArrayList<>();
@@ -28,8 +28,8 @@ public class AntColonyOptimization {
private int currentIndex;
- public int[] bestTourOrder;
- public double bestTourLength;
+ private int[] bestTourOrder;
+ private double bestTourLength;
public AntColonyOptimization(int noOfCities) {
graph = generateRandomMatrix(noOfCities);
@@ -43,26 +43,17 @@ public class AntColonyOptimization {
/**
* Generate initial solution
- *
- * @param n
- * @return
*/
public double[][] generateRandomMatrix(int n) {
double[][] randomMatrix = new double[n][n];
- random.setSeed(System.currentTimeMillis());
- IntStream.range(0, n).forEach(i -> {
- IntStream.range(0, n).forEach(j -> {
- Integer r = random.nextInt(100) + 1;
- randomMatrix[i][j] = Math.abs(r);
- });
- });
+ 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
- *
- * @return
*/
public void startAntOptimization() {
IntStream.rangeClosed(1, 3).forEach(i -> {
@@ -73,8 +64,6 @@ public class AntColonyOptimization {
/**
* Use this method to run the main logic
- *
- * @return
*/
public int[] solve() {
setupAnts();
@@ -94,7 +83,7 @@ public class AntColonyOptimization {
*/
private void setupAnts() {
IntStream.range(0, numberOfAnts).forEach(i -> {
- ants.stream().forEach(ant -> {
+ ants.forEach(ant -> {
ant.clear();
ant.visitCity(-1, random.nextInt(numberOfCities));
});
@@ -107,23 +96,18 @@ public class AntColonyOptimization {
*/
private void moveAnts() {
IntStream.range(currentIndex, numberOfCities - 1).forEach(i -> {
- ants.stream().forEach(ant -> {
- ant.visitCity(currentIndex, selectNextCity(ant));
- });
+ ants.forEach(ant -> ant.visitCity(currentIndex, selectNextCity(ant)));
currentIndex++;
});
}
/**
* Select next city for each ant
- *
- * @param ant
- * @return
*/
private int selectNextCity(Ant ant) {
int t = random.nextInt(numberOfCities - currentIndex);
if (random.nextDouble() < randomFactor) {
- IntStream.range(0, numberOfCities).filter(i -> i == t && !ant.visited(i)).findFirst();
+ IntStream.range(0, numberOfCities).filter(i -> i == t && !ant.visited(i)).findFirst(); //TODO unused
}
calculateProbabilities(ant);
double r = random.nextDouble();
@@ -140,8 +124,6 @@ public class AntColonyOptimization {
/**
* Calculate the next city picks probabilites
- *
- * @param ant
*/
public void calculateProbabilities(Ant ant) {
int i = ant.trail[currentIndex];
