public static void main(String[] args) {
if (args.length < 2) {
System.out.println("Provide a input size and repeat count");
System.exit(0);
}
int N = Integer.parseInt(args[0]);
if (N < 0) {
System.out.println(" N cannot be negaitve.");
System.exit(0);
}
Random random = new Random();
// create the random points
double[][] points = new double[N][2];
for (int i = 0; i < points.length; i++) {
points[i][0] = random.nextDouble();
points[i][1] = random.nextDouble();
}
int iterations = Integer.parseInt(args[1]);
// for rhc, sa, and ga we use a permutation based encoding
TravelingSalesmanEvaluationFunction ef = new TravelingSalesmanRouteEvaluationFunction(points);
Distribution odd = new DiscretePermutationDistribution(N);
NeighborFunction nf = new SwapNeighbor();
MutationFunction mf = new SwapMutation();
CrossoverFunction cf = new TravelingSalesmanCrossOver(ef);
HillClimbingProblem hcp = new GenericHillClimbingProblem(ef, odd, nf);
GeneticAlgorithmProblem gap = new GenericGeneticAlgorithmProblem(ef, odd, mf, cf);
System.out.println("Randomized Hill Climbing\n---------------------------------");
for (int i = 0; i < iterations; i++) {
RandomizedHillClimbing rhc = new RandomizedHillClimbing(hcp);
long t = System.nanoTime();
FixedIterationTrainer fit = new FixedIterationTrainer(rhc, 200000);
fit.train();
System.out.println(
ef.value(rhc.getOptimal()) + ", " + (((double) (System.nanoTime() - t)) / 1e9d));
}
System.out.println("Simulated Annealing \n---------------------------------");
for (int i = 0; i < iterations; i++) {
SimulatedAnnealing sa = new SimulatedAnnealing(1E12, .95, hcp);
long t = System.nanoTime();
FixedIterationTrainer fit = new FixedIterationTrainer(sa, 200000);
fit.train();
System.out.println(
ef.value(sa.getOptimal()) + ", " + (((double) (System.nanoTime() - t)) / 1e9d));
}
System.out.println("Genetic Algorithm\n---------------------------------");
for (int i = 0; i < iterations; i++) {
StandardGeneticAlgorithm ga = new StandardGeneticAlgorithm(200, 150, 10, gap);
long t = System.nanoTime();
FixedIterationTrainer fit = new FixedIterationTrainer(ga, 1000);
fit.train();
System.out.println(
ef.value(ga.getOptimal()) + ", " + (((double) (System.nanoTime() - t)) / 1e9d));
}
System.out.println("MIMIC \n---------------------------------");
// for mimic we use a sort encoding
int[] ranges = new int[N];
Arrays.fill(ranges, N);
odd = new DiscreteUniformDistribution(ranges);
Distribution df = new DiscreteDependencyTree(.1, ranges);
for (int i = 0; i < iterations; i++) {
ProbabilisticOptimizationProblem pop =
new GenericProbabilisticOptimizationProblem(ef, odd, df);
MIMIC mimic = new MIMIC(200, 60, pop);
long t = System.nanoTime();
FixedIterationTrainer fit = new FixedIterationTrainer(mimic, 1000);
fit.train();
System.out.println(
ef.value(mimic.getOptimal()) + ", " + (((double) (System.nanoTime() - t)) / 1e9d));
}
}
/**
* 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);
for (int baseIteration : new int[] {1000, 10000, 50000, 100000, 150000, 200000}) {
long startTime = System.currentTimeMillis();
RandomizedHillClimbing rhc = new RandomizedHillClimbing(hcp);
FixedIterationTrainer fit = new FixedIterationTrainer(rhc, baseIteration);
fit.train();
long endTime = System.currentTimeMillis();
System.out.println(
"RHC\t"
+ baseIteration
+ "\t"
+ (endTime - startTime)
+ "\t"
+ ef.value(rhc.getOptimal()));
startTime = System.currentTimeMillis();
SimulatedAnnealing sa = new SimulatedAnnealing(100, .95, hcp);
fit = new FixedIterationTrainer(sa, baseIteration);
fit.train();
endTime = System.currentTimeMillis();
System.out.println(
"SA\t" + baseIteration + "\t" + (endTime - startTime) + "\t" + ef.value(sa.getOptimal()));
startTime = System.currentTimeMillis();
StandardGeneticAlgorithm ga = new StandardGeneticAlgorithm(100, 75, 12, gap);
fit = new FixedIterationTrainer(ga, baseIteration / 100);
fit.train();
endTime = System.currentTimeMillis();
System.out.println(
"GA\t" + baseIteration + "\t" + (endTime - startTime) + "\t" + ef.value(ga.getOptimal()));
startTime = System.currentTimeMillis();
MIMIC mimic = new MIMIC(100, 50, pop);
fit = new FixedIterationTrainer(mimic, baseIteration / 100);
fit.train();
endTime = System.currentTimeMillis();
System.out.println(
"MIMIC\t"
+ baseIteration
+ "\t"
+ (endTime - startTime)
+ "\t"
+ ef.value(mimic.getOptimal()));
}
}
