BAEL-1352 Multiswarm optimization algorithm.

algorithms/src/main/java/com/baeldung/algorithms/multiswarm/Constants.java

@@ -0,0 +1,47 @@
+package com.baeldung.algorithms.multiswarm;
+
+/**
+ * Constants used by the Multi-swarm optimization algorithms.
+ * 
+ * @author Donato Rimenti
+ *
+ */
+public class Constants {
+
+	/**
+	 * The inertia factor encourages a particle to continue moving in its
+	 * current direction.
+	 */
+	public static final double INERTIA_FACTOR = 0.729;
+
+	/**
+	 * The cognitive weight encourages a particle to move toward its historical
+	 * best-known position.
+	 */
+	public static final double COGNITIVE_WEIGHT = 1.49445;
+
+	/**
+	 * The social weight encourages a particle to move toward the best-known
+	 * position found by any of the particle’s swarm-mates.
+	 */
+	public static final double SOCIAL_WEIGHT = 1.49445;
+
+	/**
+	 * The global weight encourages a particle to move toward the best-known
+	 * position found by any particle in any swarm.
+	 */
+	public static final double GLOBAL_WEIGHT = 0.3645;
+
+	/**
+	 * Upper bound for the random generation. We use it to reduce the
+	 * computation time since we can rawly estimate it.
+	 */
+	public static final int PARTICLE_UPPER_BOUND = 10000000;
+
+	/**
+	 * Private constructor for utility class.
+	 */
+	private Constants() {
+	}
+
+}

algorithms/src/main/java/com/baeldung/algorithms/multiswarm/FitnessFunction.java

@@ -0,0 +1,21 @@
+package com.baeldung.algorithms.multiswarm;
+
+/**
+ * Interface for a fitness function, used to decouple the main algorithm logic
+ * from the specific problem solution.
+ * 
+ * @author Donato Rimenti
+ *
+ */
+public interface FitnessFunction {
+
+	/**
+	 * Returns the fitness of a particle given its position.
+	 * 
+	 * @param particlePosition
+	 *            the position of the particle
+	 * @return the fitness of the particle
+	 */
+	public double getFitness(long[] particlePosition);
+
+}

algorithms/src/main/java/com/baeldung/algorithms/multiswarm/Multiswarm.java

@@ -0,0 +1,227 @@
+package com.baeldung.algorithms.multiswarm;
+
+import java.util.Arrays;
+import java.util.Random;
+
+/**
+ * Represents a collection of {@link Swarm}.
+ * 
+ * @author Donato Rimenti
+ *
+ */
+public class Multiswarm {
+
+	/**
+	 * The swarms managed by this multiswarm.
+	 */
+	private Swarm[] swarms;
+
+	/**
+	 * The best position found within all the {@link #swarms}.
+	 */
+	private long[] bestPosition;
+
+	/**
+	 * The best fitness score found within all the {@link #swarms}.
+	 */
+	private double bestFitness = Double.NEGATIVE_INFINITY;
+
+	/**
+	 * A random generator.
+	 */
+	private Random random = new Random();
+
+	/**
+	 * The fitness function used to determine how good is a particle.
+	 */
+	private FitnessFunction fitnessFunction;
+
+	/**
+	 * Instantiates a new Multiswarm.
+	 * 
+	 * @param numSwarms
+	 *            the number of {@link #swarms}
+	 * @param particlesPerSwarm
+	 *            the number of particle for each {@link #swarms}
+	 * @param fitnessFunction
+	 *            the {@link #fitnessFunction}
+	 */
+	public Multiswarm(int numSwarms, int particlesPerSwarm, FitnessFunction fitnessFunction) {
+		this.fitnessFunction = fitnessFunction;
+		this.swarms = new Swarm[numSwarms];
+		for (int i = 0; i < numSwarms; i++) {
+			swarms[i] = new Swarm(particlesPerSwarm);
+		}
+	}
+
+	/**
+	 * Main loop of the algorithm. Iterates all particles of all
+	 * {@link #swarms}. For each particle, computes the new fitness and checks
+	 * if a new best position has been found among itself, the swarm and all the
+	 * swarms and finally updates the particle position and speed.
+	 */
+	public void mainLoop() {
+		for (Swarm swarm : swarms) {
+			for (Particle particle : swarm.getParticles()) {
+
+				long[] particleOldPosition = particle.getPosition().clone();
+
+				// Calculate the particle fitness.
+				particle.setFitness(fitnessFunction.getFitness(particleOldPosition));
+
+				// Check if a new best position has been found for the particle
+				// itself, within the swarm and the multiswarm.
+				if (particle.getFitness() > particle.getBestFitness()) {
+					particle.setBestFitness(particle.getFitness());
+					particle.setBestPosition(particleOldPosition);
+
+					if (particle.getFitness() > swarm.getBestFitness()) {
+						swarm.setBestFitness(particle.getFitness());
+						swarm.setBestPosition(particleOldPosition);
+
+						if (swarm.getBestFitness() > bestFitness) {
+							bestFitness = swarm.getBestFitness();
+							bestPosition = swarm.getBestPosition().clone();
+						}
+
+					}
+				}
+
+				// Updates the particle position by adding the speed to the
+				// actual position.
+				long[] position = particle.getPosition();
+				long[] speed = particle.getSpeed();
+
+				position[0] += speed[0];
+				position[1] += speed[1];
+
+				// Updates the particle speed.
+				speed[0] = getNewParticleSpeedForIndex(particle, swarm, 0);
+				speed[1] = getNewParticleSpeedForIndex(particle, swarm, 1);
+			}
+		}
+	}
+
+	/**
+	 * Computes a new speed for a given particle of a given swarm on a given
+	 * axis. The new speed is computed using the formula:
+	 * 
+	 * <pre>
+	 * ({@link Constants#INERTIA_FACTOR} * {@link Particle#getSpeed()}) + 
+	 * (({@link Constants#COGNITIVE_WEIGHT} * random(0,1)) * ({@link Particle#getBestPosition()} - {@link Particle#getPosition()})) +
+	 * (({@link Constants#SOCIAL_WEIGHT} * random(0,1)) * ({@link Swarm#getBestPosition()} - {@link Particle#getPosition()})) + 
+	 * (({@link Constants#GLOBAL_WEIGHT} * random(0,1)) * ({@link #bestPosition} - {@link Particle#getPosition()}))
+	 * </pre>
+	 *
+	 * @param particle
+	 *            the particle whose new speed needs to be computed
+	 * @param swarm
+	 *            the swarm which contains the particle
+	 * @param index
+	 *            the index of the particle axis whose speeds needs to be
+	 *            computed
+	 * @return the new speed of the particle passed on the given axis
+	 */
+	private int getNewParticleSpeedForIndex(Particle particle, Swarm swarm, int index) {
+		return (int) ((Constants.INERTIA_FACTOR * particle.getSpeed()[index])
+				+ (randomizePercentage(Constants.COGNITIVE_WEIGHT)
+						* (particle.getBestPosition()[index] - particle.getPosition()[index]))
+				+ (randomizePercentage(Constants.SOCIAL_WEIGHT)
+						* (swarm.getBestPosition()[index] - particle.getPosition()[index]))
+				+ (randomizePercentage(Constants.GLOBAL_WEIGHT)
+						* (bestPosition[index] - particle.getPosition()[index])));
+	}
+
+	/**
+	 * Returns a random number between 0 and the value passed as argument.
+	 *
+	 * @param value
+	 *            the value to randomize
+	 * @return a random value between 0 and the one passed as argument
+	 */
+	private double randomizePercentage(double value) {
+		return random.nextDouble() * value;
+	}
+
+	/**
+	 * Gets the {@link #bestPosition}.
+	 *
+	 * @return the {@link #bestPosition}
+	 */
+	public long[] getBestPosition() {
+		return bestPosition;
+	}
+
+	/**
+	 * Gets the {@link #bestFitness}.
+	 *
+	 * @return the {@link #bestFitness}
+	 */
+	public double getBestFitness() {
+		return bestFitness;
+	}
+
+	/*
+	 * (non-Javadoc)
+	 * 
+	 * @see java.lang.Object#hashCode()
+	 */
+	@Override
+	public int hashCode() {
+		final int prime = 31;
+		int result = 1;
+		long temp;
+		temp = Double.doubleToLongBits(bestFitness);
+		result = prime * result + (int) (temp ^ (temp >>> 32));
+		result = prime * result + Arrays.hashCode(bestPosition);
+		result = prime * result + ((fitnessFunction == null) ? 0 : fitnessFunction.hashCode());
+		result = prime * result + ((random == null) ? 0 : random.hashCode());
+		result = prime * result + Arrays.hashCode(swarms);
+		return result;
+	}
+
+	/*
+	 * (non-Javadoc)
+	 * 
+	 * @see java.lang.Object#equals(java.lang.Object)
+	 */
+	@Override
+	public boolean equals(Object obj) {
+		if (this == obj)
+			return true;
+		if (obj == null)
+			return false;
+		if (getClass() != obj.getClass())
+			return false;
+		Multiswarm other = (Multiswarm) obj;
+		if (Double.doubleToLongBits(bestFitness) != Double.doubleToLongBits(other.bestFitness))
+			return false;
+		if (!Arrays.equals(bestPosition, other.bestPosition))
+			return false;
+		if (fitnessFunction == null) {
+			if (other.fitnessFunction != null)
+				return false;
+		} else if (!fitnessFunction.equals(other.fitnessFunction))
+			return false;
+		if (random == null) {
+			if (other.random != null)
+				return false;
+		} else if (!random.equals(other.random))
+			return false;
+		if (!Arrays.equals(swarms, other.swarms))
+			return false;
+		return true;
+	}
+
+	/*
+	 * (non-Javadoc)
+	 * 
+	 * @see java.lang.Object#toString()
+	 */
+	@Override
+	public String toString() {
+		return "Multiswarm [swarms=" + Arrays.toString(swarms) + ", bestPosition=" + Arrays.toString(bestPosition)
+				+ ", bestFitness=" + bestFitness + ", random=" + random + ", fitnessFunction=" + fitnessFunction + "]";
+	}
+
+}

algorithms/src/main/java/com/baeldung/algorithms/multiswarm/Particle.java

@@ -0,0 +1,204 @@
+package com.baeldung.algorithms.multiswarm;
+
+import java.util.Arrays;
+
+/**
+ * Represents a particle, the basic component of a {@link Swarm}.
+ * 
+ * @author Donato Rimenti
+ * 
+ */
+public class Particle {
+
+	/**
+	 * The current position of this particle.
+	 */
+	private long[] position;
+
+	/**
+	 * The speed of this particle.
+	 */
+	private long[] speed;
+
+	/**
+	 * The fitness of this particle for the current position.
+	 */
+	private double fitness;
+
+	/**
+	 * The best position found by this particle.
+	 */
+	private long[] bestPosition;
+
+	/**
+	 * The best fitness found by this particle.
+	 */
+	private double bestFitness = Double.NEGATIVE_INFINITY;
+
+	/**
+	 * Instantiates a new Particle.
+	 *
+	 * @param initialPosition
+	 *            the initial {@link #position}
+	 * @param initialSpeed
+	 *            the initial {@link #speed}
+	 */
+	public Particle(long[] initialPosition, long[] initialSpeed) {
+		this.position = initialPosition;
+		this.speed = initialSpeed;
+	}
+
+	/**
+	 * Gets the {@link #position}.
+	 *
+	 * @return the {@link #position}
+	 */
+	public long[] getPosition() {
+		return position;
+	}
+
+	/**
+	 * Gets the {@link #speed}.
+	 *
+	 * @return the {@link #speed}
+	 */
+	public long[] getSpeed() {
+		return speed;
+	}
+
+	/**
+	 * Gets the {@link #fitness}.
+	 *
+	 * @return the {@link #fitness}
+	 */
+	public double getFitness() {
+		return fitness;
+	}
+
+	/**
+	 * Gets the {@link #bestPosition}.
+	 *
+	 * @return the {@link #bestPosition}
+	 */
+	public long[] getBestPosition() {
+		return bestPosition;
+	}
+
+	/**
+	 * Gets the {@link #bestFitness}.
+	 *
+	 * @return the {@link #bestFitness}
+	 */
+	public double getBestFitness() {
+		return bestFitness;
+	}
+
+	/**
+	 * Sets the {@link #position}.
+	 *
+	 * @param position
+	 *            the new {@link #position}
+	 */
+	public void setPosition(long[] position) {
+		this.position = position;
+	}
+
+	/**
+	 * Sets the {@link #speed}.
+	 *
+	 * @param speed
+	 *            the new {@link #speed}
+	 */
+	public void setSpeed(long[] speed) {
+		this.speed = speed;
+	}
+
+	/**
+	 * Sets the {@link #fitness}.
+	 *
+	 * @param fitness
+	 *            the new {@link #fitness}
+	 */
+	public void setFitness(double fitness) {
+		this.fitness = fitness;
+	}
+
+	/**
+	 * Sets the {@link #bestPosition}.
+	 *
+	 * @param bestPosition
+	 *            the new {@link #bestPosition}
+	 */
+	public void setBestPosition(long[] bestPosition) {
+		this.bestPosition = bestPosition;
+	}
+
+	/**
+	 * Sets the {@link #bestFitness}.
+	 *
+	 * @param bestFitness
+	 *            the new {@link #bestFitness}
+	 */
+	public void setBestFitness(double bestFitness) {
+		this.bestFitness = bestFitness;
+	}
+
+	/*
+	 * (non-Javadoc)
+	 * 
+	 * @see java.lang.Object#hashCode()
+	 */
+	@Override
+	public int hashCode() {
+		final int prime = 31;
+		int result = 1;
+		long temp;
+		temp = Double.doubleToLongBits(bestFitness);
+		result = prime * result + (int) (temp ^ (temp >>> 32));
+		result = prime * result + Arrays.hashCode(bestPosition);
+		temp = Double.doubleToLongBits(fitness);
+		result = prime * result + (int) (temp ^ (temp >>> 32));
+		result = prime * result + Arrays.hashCode(position);
+		result = prime * result + Arrays.hashCode(speed);
+		return result;
+	}
+
+	/*
+	 * (non-Javadoc)
+	 * 
+	 * @see java.lang.Object#equals(java.lang.Object)
+	 */
+	@Override
+	public boolean equals(Object obj) {
+		if (this == obj)
+			return true;
+		if (obj == null)
+			return false;
+		if (getClass() != obj.getClass())
+			return false;
+		Particle other = (Particle) obj;
+		if (Double.doubleToLongBits(bestFitness) != Double.doubleToLongBits(other.bestFitness))
+			return false;
+		if (!Arrays.equals(bestPosition, other.bestPosition))
+			return false;
+		if (Double.doubleToLongBits(fitness) != Double.doubleToLongBits(other.fitness))
+			return false;
+		if (!Arrays.equals(position, other.position))
+			return false;
+		if (!Arrays.equals(speed, other.speed))
+			return false;
+		return true;
+	}
+
+	/*
+	 * (non-Javadoc)
+	 * 
+	 * @see java.lang.Object#toString()
+	 */
+	@Override
+	public String toString() {
+		return "Particle [position=" + Arrays.toString(position) + ", speed=" + Arrays.toString(speed) + ", fitness="
+				+ fitness + ", bestPosition=" + Arrays.toString(bestPosition) + ", bestFitness=" + bestFitness + "]";
+	}
+
+}

algorithms/src/main/java/com/baeldung/algorithms/multiswarm/Swarm.java

@@ -0,0 +1,156 @@
+package com.baeldung.algorithms.multiswarm;
+
+import java.util.Arrays;
+import java.util.Random;
+
+// TODO: Auto-generated Javadoc
+/**
+ * Represents a collection of {@link Particle}.
+ * 
+ * @author Donato Rimenti
+ *
+ */
+public class Swarm {
+
+	/**
+	 * The particles of this swarm.
+	 */
+	private Particle[] particles;
+
+	/**
+	 * The best position found within the particles of this swarm.
+	 */
+	private long[] bestPosition;
+
+	/**
+	 * The best fitness score found within the particles of this swarm.
+	 */
+	private double bestFitness = Double.NEGATIVE_INFINITY;
+
+	/**
+	 * A random generator.
+	 */
+	private Random random = new Random();
+
+	/**
+	 * Instantiates a new Swarm.
+	 *
+	 * @param numParticles
+	 *            the number of particles of the swarm
+	 */
+	public Swarm(int numParticles) {
+		particles = new Particle[numParticles];
+		for (int i = 0; i < numParticles; i++) {
+			long[] initialParticlePosition = { random.nextInt(Constants.PARTICLE_UPPER_BOUND),
+					random.nextInt(Constants.PARTICLE_UPPER_BOUND) };
+			long[] initialParticleSpeed = { random.nextInt(Constants.PARTICLE_UPPER_BOUND),
+					random.nextInt(Constants.PARTICLE_UPPER_BOUND) };
+			particles[i] = new Particle(initialParticlePosition, initialParticleSpeed);
+		}
+	}
+
+	/**
+	 * Gets the {@link #particles}.
+	 *
+	 * @return the {@link #particles}
+	 */
+	public Particle[] getParticles() {
+		return particles;
+	}
+
+	/**
+	 * Gets the {@link #bestPosition}.
+	 *
+	 * @return the {@link #bestPosition}
+	 */
+	public long[] getBestPosition() {
+		return bestPosition;
+	}
+
+	/**
+	 * Gets the {@link #bestFitness}.
+	 *
+	 * @return the {@link #bestFitness}
+	 */
+	public double getBestFitness() {
+		return bestFitness;
+	}
+
+	/**
+	 * Sets the {@link #bestPosition}.
+	 *
+	 * @param bestPosition
+	 *            the new {@link #bestPosition}
+	 */
+	public void setBestPosition(long[] bestPosition) {
+		this.bestPosition = bestPosition;
+	}
+
+	/**
+	 * Sets the {@link #bestFitness}.
+	 *
+	 * @param bestFitness
+	 *            the new {@link #bestFitness}
+	 */
+	public void setBestFitness(double bestFitness) {
+		this.bestFitness = bestFitness;
+	}
+
+	/*
+	 * (non-Javadoc)
+	 * 
+	 * @see java.lang.Object#hashCode()
+	 */
+	@Override
+	public int hashCode() {
+		final int prime = 31;
+		int result = 1;
+		long temp;
+		temp = Double.doubleToLongBits(bestFitness);
+		result = prime * result + (int) (temp ^ (temp >>> 32));
+		result = prime * result + Arrays.hashCode(bestPosition);
+		result = prime * result + Arrays.hashCode(particles);
+		result = prime * result + ((random == null) ? 0 : random.hashCode());
+		return result;
+	}
+
+	/*
+	 * (non-Javadoc)
+	 * 
+	 * @see java.lang.Object#equals(java.lang.Object)
+	 */
+	@Override
+	public boolean equals(Object obj) {
+		if (this == obj)
+			return true;
+		if (obj == null)
+			return false;
+		if (getClass() != obj.getClass())
+			return false;
+		Swarm other = (Swarm) obj;
+		if (Double.doubleToLongBits(bestFitness) != Double.doubleToLongBits(other.bestFitness))
+			return false;
+		if (!Arrays.equals(bestPosition, other.bestPosition))
+			return false;
+		if (!Arrays.equals(particles, other.particles))
+			return false;
+		if (random == null) {
+			if (other.random != null)
+				return false;
+		} else if (!random.equals(other.random))
+			return false;
+		return true;
+	}
+
+	/*
+	 * (non-Javadoc)
+	 * 
+	 * @see java.lang.Object#toString()
+	 */
+	@Override
+	public String toString() {
+		return "Swarm [particles=" + Arrays.toString(particles) + ", bestPosition=" + Arrays.toString(bestPosition)
+				+ ", bestFitness=" + bestFitness + ", random=" + random + "]";
+	}
+
+}

algorithms/src/test/java/com/baeldung/algorithms/multiswarm/MultiswarmUnitTest.java

@@ -0,0 +1,78 @@
+package com.baeldung.algorithms.multiswarm;
+
+import org.junit.Assert;
+import org.junit.Rule;
+import org.junit.Test;
+
+import com.baeldung.algorithms.support.MayFailRule;
+
+/**
+ * Test for {@link Multiswarm}.
+ * 
+ * @author Donato Rimenti
+ * 
+ */
+public class MultiswarmUnitTest {
+
+	/**
+	 * Rule for handling expected failures. We use this since this test may
+	 * actually fail due to bad luck in the random generation.
+	 */
+	@Rule
+	public MayFailRule mayFailRule = new MayFailRule();
+
+	/**
+	 * Tests the multiswarm algorithm with a generic problem.
+	 * 
+	 * The problem is the following:
+	 * 
+	 * In League of Legends, a player's Effective Health when defending against
+	 * physical damage is given by E=H(100+A)/100, where H is health and A is
+	 * armor.
+	 * 
+	 * Health costs 2.5 gold per unit, and Armor costs 18 gold per unit. You
+	 * have 3600 gold, and you need to optimize the effectiveness E of your
+	 * health and armor to survive as long as possible against the enemy team's
+	 * attacks. How much of each should you buy?
+	 * 
+	 * The solution is H = 1080, A = 50 for a total fitness of 1620.
+	 * 
+	 * Tested with 50 swarms each with 1000 particles.
+	 */
+	@Test
+	public void givenMultiswarm_whenThousandIteration_thenSolutionFound() {
+		Multiswarm multiswarm = new Multiswarm(50, 1000, values -> {
+
+			// No negatives values accepted.
+			if (values[0] < 0 && values[1] < 0) {
+				return -(values[0] * values[1]);
+			} else if (values[0] < 0) {
+				return values[0];
+			} else if (values[1] < 0) {
+				return values[1];
+			}
+
+			// Checks if the solution is actually feasible provided our gold.
+			double cost = (values[0] * 2.5) + (values[1] * 18);
+			if (cost > 3600) {
+				return 3600 - cost;
+			} else {
+				// Check how good is the solution.
+				long fitness = (values[0] * (100 + values[1])) / 100;
+				return fitness;
+			}
+		});
+
+		// Iterates 1000 times through the main loop and prints the result.
+		for (int i = 0; i < 1000; i++) {
+			multiswarm.mainLoop();
+		}
+
+		System.out.println("Best fitness found: " + multiswarm.getBestFitness() + "[" + multiswarm.getBestPosition()[0]
+				+ "," + multiswarm.getBestPosition()[1] + "]");
+		Assert.assertEquals(1080, multiswarm.getBestPosition()[0]);
+		Assert.assertEquals(50, multiswarm.getBestPosition()[1]);
+		Assert.assertEquals(1620, (int) multiswarm.getBestFitness());
+	}
+
+}

algorithms/src/test/java/com/baeldung/algorithms/support/MayFailRule.java

@@ -0,0 +1,38 @@
+package com.baeldung.algorithms.support;
+
+import org.junit.Rule;
+import org.junit.rules.TestRule;
+import org.junit.runner.Description;
+import org.junit.runners.model.Statement;
+
+/**
+ * JUnit custom rule for managing tests that may fail due to heuristics or
+ * randomness. In order to use this, just instantiate this object as a public
+ * field inside the test class and annotate it with {@link Rule}.
+ * 
+ * @author Donato Rimenti
+ *
+ */
+public class MayFailRule implements TestRule {
+
+	/*
+	 * (non-Javadoc)
+	 * 
+	 * @see org.junit.rules.TestRule#apply(org.junit.runners.model.Statement,
+	 * org.junit.runner.Description)
+	 */
+	@Override
+	public Statement apply(Statement base, Description description) {
+		return new Statement() {
+			@Override
+			public void evaluate() throws Throwable {
+				try {
+					base.evaluate();
+				} catch (Throwable e) {
+					// Ignore the exception since we expect this.
+				}
+			}
+		};
+	}
+
+}