TSP.java

package tsp;

public class TSP {

    public static void main(String[] args) {
        // Create cities
        int numCities = 100;
        City cities[] = new City[numCities];
        // Loop over new cities
        for(int cityIndex=0; cityIndex < numCities; cityIndex++){
            // Generate x,y position
            int xPos = (int) (100 * Math.random());
            int yPos = (int) (100 * Math.random());
            // Add city
            cities[cityIndex] = new City(xPos, yPos);
        }
        
        // Initial GA
        GeneticAlgorithm ga = new GeneticAlgorithm(50, 0, 0.9, 2, 5);
        
        // Initialise population
        Population population = ga.initPopulation(cities.length);
        
        // Evaluate population
        ga.evalPopulation(population, cities);
        
        Route startRoute = new Route(population.getFittest(0), cities);
        System.out.println("Start Distance: " + startRoute.getDistance());
                
        // Keep track of current generation
        int generation = 1;
        // Start evolution loop
        while (ga.isTerminationConditionMet(generation, 250) == false) {
            // Print fittest individual from population
            Route route = new Route(population.getFittest(0), cities);
            System.out.println("Best distance: " + route.getDistance());
            
            // Apply crossover
            population = ga.orderedCrossover(population);
            
            // Apply mutation
            population = ga.mutatePopulation(population);
            
            // Evaluate population
            ga.evalPopulation(population, cities);
            
            // Increment the current generation
            generation++;
        }
    }
}