Simulated Annealing algorithm (#900)
* @Async and Spring Security * @Async with SecurityContext propagated * Spring and @Async * Simulated Annealing algorithm * Simulated Annealing algorithm * Rebase * Rebase
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@ -85,6 +85,12 @@
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<artifactId>log4j-over-slf4j</artifactId>
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<version>${org.slf4j.version}</version>
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</dependency>
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<dependency>
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<groupId>org.projectlombok</groupId>
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<artifactId>lombok</artifactId>
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<version>1.16.12</version>
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<scope>provided</scope>
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</dependency>
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<!-- test scoped -->
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@ -0,0 +1,22 @@
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package com.baeldung.algorithms;
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import lombok.Data;
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@Data
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public class City {
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private int x;
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private int y;
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public City() {
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this.x = (int) (Math.random() * 500);
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this.y = (int) (Math.random() * 500);
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}
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public double distanceToCity(City city) {
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int x = Math.abs(getX() - city.getX());
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int y = Math.abs(getY() - city.getY());
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return Math.sqrt(Math.pow(x, 2) + Math.pow(y, 2));
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}
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}
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@ -0,0 +1,41 @@
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package com.baeldung.algorithms;
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public class SimulatedAnnealing {
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private static Travel travel = new Travel(10);
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public static double simulateAnnealing(double startingTemperature, int numberOfIterations, double coolingRate) {
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System.out.println("Starting SA with temperature: " + startingTemperature + ", # of iterations: "
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+ numberOfIterations + " and colling rate: " + coolingRate);
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double t = startingTemperature;
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travel.generateInitialTravel();
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double bestDistance = travel.getDistance();
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System.out.println("Initial distance of travel: " + bestDistance);
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Travel bestSolution = travel;
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Travel currentSolution = bestSolution;
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for (int i = 0; i < numberOfIterations; i++) {
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if (t > 0.1) {
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currentSolution.swapCities();
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double currentDistance = currentSolution.getDistance();
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if (currentDistance == 0)
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continue;
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if (currentDistance < bestDistance) {
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bestDistance = currentDistance;
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} else if (Math.exp((currentDistance - bestDistance) / t) < Math.random()) {
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currentSolution.revertSwap();
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}
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t *= coolingRate;
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}
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if (i % 100 == 0) {
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System.out.println("Iteration #" + i);
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}
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}
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return bestDistance;
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}
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public static void main(String[] args) {
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System.out.println("Optimized distance for travel: " + simulateAnnealing(10, 10000, 0.9));
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}
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}
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@ -0,0 +1,60 @@
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package com.baeldung.algorithms;
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import java.util.ArrayList;
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import java.util.Collections;
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import lombok.Data;
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@Data
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public class Travel {
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private ArrayList<City> travel = new ArrayList<>();
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private ArrayList<City> previousTravel = new ArrayList<>();
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public Travel(int numberOfCities) {
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for (int i = 0; i < numberOfCities; i++) {
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travel.add(new City());
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}
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}
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public void generateInitialTravel() {
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if (travel.isEmpty())
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new Travel(10);
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Collections.shuffle(travel);
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}
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public void swapCities() {
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int a = generateRandomIndex();
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int b = generateRandomIndex();
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previousTravel = travel;
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travel.set(a, travel.get(b));
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}
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public void revertSwap() {
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travel = previousTravel;
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}
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private int generateRandomIndex() {
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return (int) (Math.random() * travel.size());
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}
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public City getCity(int index) {
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return travel.get(index);
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}
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public int getDistance() {
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int distance = 0;
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for (int index = 0; index < travel.size(); index++) {
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City starting = getCity(index);
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City destination;
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if (index + 1 < travel.size()) {
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destination = getCity(index + 1);
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} else {
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destination = getCity(0);
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}
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distance += starting.distanceToCity(destination);
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}
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return distance;
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}
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}
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