package opt.test;
import java.util.Arrays;
import java.util.Random;
import dist.DiscreteDependencyTree;
import dist.DiscreteUniformDistribution;
import dist.Distribution;
import opt.DiscreteChangeOneNeighbor;
import opt.EvaluationFunction;
import opt.GenericHillClimbingProblem;
import opt.HillClimbingProblem;
import opt.NeighborFunction;
import opt.RandomizedHillClimbing;
import opt.SimulatedAnnealing;
import opt.example.*;
import opt.ga.CrossoverFunction;
import opt.ga.DiscreteChangeOneMutation;
import opt.ga.GenericGeneticAlgorithmProblem;
import opt.ga.GeneticAlgorithmProblem;
import opt.ga.MutationFunction;
import opt.ga.StandardGeneticAlgorithm;
import opt.ga.UniformCrossOver;
import opt.prob.GenericProbabilisticOptimizationProblem;
import opt.prob.MIMIC;
import opt.prob.ProbabilisticOptimizationProblem;
import shared.FixedIterationTrainer;
/**
* A test of the knap sack problem
* @author Andrew Guillory gtg008g@mail.gatech.edu
* @version 1.0
*/
public class KnapsackTest {
/** Random number generator */
private static final Random random = new Random();
/** The number of items */
private static final int NUM_ITEMS = 40;
/** The number of copies each */
private static final int COPIES_EACH = 4;
/** The maximum weight for a single element */
private static final double MAX_WEIGHT = 50;
/** The maximum volume for a single element */
private static final double MAX_VOLUME = 50;
/** The volume of the knapsack */
private static final double KNAPSACK_VOLUME =
MAX_VOLUME * NUM_ITEMS * COPIES_EACH * .4;
/**
* The test main
* @param args ignored
*/
public static void main(String[] args) {
int[] copies = new int[NUM_ITEMS];
Arrays.fill(copies, COPIES_EACH);
double[] weights = new double[NUM_ITEMS];
double[] volumes = new double[NUM_ITEMS];
for (int i = 0; i < NUM_ITEMS; i++) {
weights[i] = random.nextDouble() * MAX_WEIGHT;
volumes[i] = random.nextDouble() * MAX_VOLUME;
}
int[] ranges = new int[NUM_ITEMS];
Arrays.fill(ranges, COPIES_EACH + 1);
EvaluationFunction ef = new KnapsackEvaluationFunction(weights, volumes, KNAPSACK_VOLUME, copies);
Distribution odd = new DiscreteUniformDistribution(ranges);
NeighborFunction nf = new DiscreteChangeOneNeighbor(ranges);
MutationFunction mf = new DiscreteChangeOneMutation(ranges);
CrossoverFunction cf = new UniformCrossOver();
Distribution df = new DiscreteDependencyTree(.1, ranges);
HillClimbingProblem hcp = new GenericHillClimbingProblem(ef, odd, nf);
GeneticAlgorithmProblem gap = new GenericGeneticAlgorithmProblem(ef, odd, mf, cf);
ProbabilisticOptimizationProblem pop = new GenericProbabilisticOptimizationProblem(ef, odd, df);
RandomizedHillClimbing rhc = new RandomizedHillClimbing(hcp);
FixedIterationTrainer fit = new FixedIterationTrainer(rhc, 200000);
fit.train();
System.out.println(ef.value(rhc.getOptimal()));
SimulatedAnnealing sa = new SimulatedAnnealing(100, .95, hcp);
fit = new FixedIterationTrainer(sa, 200000);
fit.train();
System.out.println(ef.value(sa.getOptimal()));
StandardGeneticAlgorithm ga = new StandardGeneticAlgorithm(200, 150, 25, gap);
fit = new FixedIterationTrainer(ga, 1000);
fit.train();
System.out.println(ef.value(ga.getOptimal()));
MIMIC mimic = new MIMIC(200, 100, pop);
fit = new FixedIterationTrainer(mimic, 1000);
fit.train();
System.out.println(ef.value(mimic.getOptimal()));
}
}