@Test
public void testInitChain() throws Exception {
SimulatedAnnealing simulatedAnnealing = new SimulatedAnnealing();
Knapsack knapsack = new Knapsack("9035 4 100 4 236 68 237 74 121 22 112");
Configuration configuration = knapsack.getRandomConfiguration();
double fitness = knapsack.getDefaultFitness().getValue(configuration);
simulatedAnnealing.init(new BaseObjectiveProblem(knapsack), configuration);
assert simulatedAnnealing.getBestFitness() == fitness
: "Expected fitness to be " + fitness + ", got " + simulatedAnnealing.getBestFitness();
assert simulatedAnnealing.getBestConfiguration() == configuration
: "Expected solution to be "
+ configuration
+ ", got "
+ simulatedAnnealing.getBestConfiguration();
// try to perform optimize step - SA should not raise an exception
simulatedAnnealing.optimize();
}
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));
}
}
