/**
* Returns a new {@link DBEA} instance.
*
* @param properties the properties for customizing the new {@code DBEA} instance
* @param problem the problem
* @return a new {@code DBEA} instance
*/
private Algorithm newDBEA(TypedProperties properties, Problem problem) {
int divisionsOuter = 4;
int divisionsInner = 0;
if (properties.contains("divisionsOuter") && properties.contains("divisionsInner")) {
divisionsOuter = (int) properties.getDouble("divisionsOuter", 4);
divisionsInner = (int) properties.getDouble("divisionsInner", 0);
} else if (properties.contains("divisions")) {
divisionsOuter = (int) properties.getDouble("divisions", 4);
} else if (problem.getNumberOfObjectives() == 1) {
divisionsOuter = 100;
} else if (problem.getNumberOfObjectives() == 2) {
divisionsOuter = 99;
} else if (problem.getNumberOfObjectives() == 3) {
divisionsOuter = 12;
} else if (problem.getNumberOfObjectives() == 4) {
divisionsOuter = 8;
} else if (problem.getNumberOfObjectives() == 5) {
divisionsOuter = 6;
} else if (problem.getNumberOfObjectives() == 6) {
divisionsOuter = 4;
divisionsInner = 1;
} else if (problem.getNumberOfObjectives() == 7) {
divisionsOuter = 3;
divisionsInner = 2;
} else if (problem.getNumberOfObjectives() == 8) {
divisionsOuter = 3;
divisionsInner = 2;
} else if (problem.getNumberOfObjectives() == 9) {
divisionsOuter = 3;
divisionsInner = 2;
} else if (problem.getNumberOfObjectives() == 10) {
divisionsOuter = 3;
divisionsInner = 2;
} else {
divisionsOuter = 2;
divisionsInner = 1;
}
int populationSize =
(int)
(CombinatoricsUtils.binomialCoefficient(
problem.getNumberOfObjectives() + divisionsOuter - 1, divisionsOuter)
+ (divisionsInner == 0
? 0
: CombinatoricsUtils.binomialCoefficient(
problem.getNumberOfObjectives() + divisionsInner - 1, divisionsInner)));
Initialization initialization = new RandomInitialization(problem, populationSize);
Variation variation = OperatorFactory.getInstance().getVariation(null, properties, problem);
return new DBEA(problem, initialization, variation, divisionsOuter, divisionsInner);
}
@Test
public void testJacobiEvaluationAt1() {
for (int v = 0; v < 10; ++v) {
for (int w = 0; w < 10; ++w) {
for (int i = 0; i < 10; ++i) {
PolynomialFunction jacobi = PolynomialsUtils.createJacobiPolynomial(i, v, w);
double binomial = CombinatoricsUtils.binomialCoefficient(v + i, i);
Assert.assertTrue(Precision.equals(binomial, jacobi.value(1.0), 1));
}
}
}
}
@Override
public final IFuzzyValue<Boolean> execute(
final IContext p_context,
final boolean p_parallel,
final List<ITerm> p_argument,
final List<ITerm> p_return,
final List<ITerm> p_annotation) {
CCommon.flatcollection(p_argument)
.stream()
.map(i -> CombinatoricsUtils.factorial(i.<Number>raw().intValue()))
.map(CRawTerm::from)
.forEach(p_return::add);
return CFuzzyValue.from(true);
}
/**
* Returns a new {@link RVEA} instance.
*
* @param properties the properties for customizing the new {@code RVEA} instance
* @param problem the problem
* @return a new {@code RVEA} instance
*/
private Algorithm newRVEA(TypedProperties properties, Problem problem) {
int divisionsOuter = 4;
int divisionsInner = 0;
if (problem.getNumberOfObjectives() < 2) {
throw new FrameworkException("RVEA requires at least two objectives");
}
if (properties.contains("divisionsOuter") && properties.contains("divisionsInner")) {
divisionsOuter = (int) properties.getDouble("divisionsOuter", 4);
divisionsInner = (int) properties.getDouble("divisionsInner", 0);
} else if (properties.contains("divisions")) {
divisionsOuter = (int) properties.getDouble("divisions", 4);
} else if (problem.getNumberOfObjectives() == 1) {
divisionsOuter = 100;
} else if (problem.getNumberOfObjectives() == 2) {
divisionsOuter = 99;
} else if (problem.getNumberOfObjectives() == 3) {
divisionsOuter = 12;
} else if (problem.getNumberOfObjectives() == 4) {
divisionsOuter = 8;
} else if (problem.getNumberOfObjectives() == 5) {
divisionsOuter = 6;
} else if (problem.getNumberOfObjectives() == 6) {
divisionsOuter = 4;
divisionsInner = 1;
} else if (problem.getNumberOfObjectives() == 7) {
divisionsOuter = 3;
divisionsInner = 2;
} else if (problem.getNumberOfObjectives() == 8) {
divisionsOuter = 3;
divisionsInner = 2;
} else if (problem.getNumberOfObjectives() == 9) {
divisionsOuter = 3;
divisionsInner = 2;
} else if (problem.getNumberOfObjectives() == 10) {
divisionsOuter = 3;
divisionsInner = 2;
} else {
divisionsOuter = 2;
divisionsInner = 1;
}
// compute number of reference vectors
int populationSize =
(int)
(CombinatoricsUtils.binomialCoefficient(
problem.getNumberOfObjectives() + divisionsOuter - 1, divisionsOuter)
+ (divisionsInner == 0
? 0
: CombinatoricsUtils.binomialCoefficient(
problem.getNumberOfObjectives() + divisionsInner - 1, divisionsInner)));
Initialization initialization = new RandomInitialization(problem, populationSize);
ReferenceVectorGuidedPopulation population =
new ReferenceVectorGuidedPopulation(
problem.getNumberOfObjectives(),
divisionsOuter,
divisionsInner,
properties.getDouble("alpha", 2.0));
if (!properties.contains("sbx.swap")) {
properties.setBoolean("sbx.swap", false);
}
if (!properties.contains("sbx.distributionIndex")) {
properties.setDouble("sbx.distributionIndex", 30.0);
}
if (!properties.contains("pm.distributionIndex")) {
properties.setDouble("pm.distributionIndex", 20.0);
}
Variation variation = OperatorFactory.getInstance().getVariation(null, properties, problem);
int maxGenerations = (int) (properties.getDouble("maxEvaluations", 10000) / populationSize);
int adaptFrequency = (int) properties.getDouble("adaptFrequency", maxGenerations / 10);
return new RVEA(problem, population, variation, initialization, maxGenerations, adaptFrequency);
}
/**
* Returns a new {@link NSGAIII} instance.
*
* @param properties the properties for customizing the new {@code NSGAIII} instance
* @param problem the problem
* @return a new {@code NSGAIII} instance
*/
private Algorithm newNSGAIII(TypedProperties properties, Problem problem) {
int divisionsOuter = 4;
int divisionsInner = 0;
if (properties.contains("divisionsOuter") && properties.contains("divisionsInner")) {
divisionsOuter = (int) properties.getDouble("divisionsOuter", 4);
divisionsInner = (int) properties.getDouble("divisionsInner", 0);
} else if (properties.contains("divisions")) {
divisionsOuter = (int) properties.getDouble("divisions", 4);
} else if (problem.getNumberOfObjectives() == 1) {
divisionsOuter = 100;
} else if (problem.getNumberOfObjectives() == 2) {
divisionsOuter = 99;
} else if (problem.getNumberOfObjectives() == 3) {
divisionsOuter = 12;
} else if (problem.getNumberOfObjectives() == 4) {
divisionsOuter = 8;
} else if (problem.getNumberOfObjectives() == 5) {
divisionsOuter = 6;
} else if (problem.getNumberOfObjectives() == 6) {
divisionsOuter = 4;
divisionsInner = 1;
} else if (problem.getNumberOfObjectives() == 7) {
divisionsOuter = 3;
divisionsInner = 2;
} else if (problem.getNumberOfObjectives() == 8) {
divisionsOuter = 3;
divisionsInner = 2;
} else if (problem.getNumberOfObjectives() == 9) {
divisionsOuter = 3;
divisionsInner = 2;
} else if (problem.getNumberOfObjectives() == 10) {
divisionsOuter = 3;
divisionsInner = 2;
} else {
divisionsOuter = 2;
divisionsInner = 1;
}
int populationSize;
if (properties.contains("populationSize")) {
populationSize = (int) properties.getDouble("populationSize", 100);
} else {
// compute number of reference points
populationSize =
(int)
(CombinatoricsUtils.binomialCoefficient(
problem.getNumberOfObjectives() + divisionsOuter - 1, divisionsOuter)
+ (divisionsInner == 0
? 0
: CombinatoricsUtils.binomialCoefficient(
problem.getNumberOfObjectives() + divisionsInner - 1, divisionsInner)));
// round up to a multiple of 4
populationSize = (int) Math.ceil(populationSize / 4d) * 4;
}
Initialization initialization = new RandomInitialization(problem, populationSize);
ReferencePointNondominatedSortingPopulation population =
new ReferencePointNondominatedSortingPopulation(
problem.getNumberOfObjectives(), divisionsOuter, divisionsInner);
Selection selection = null;
if (problem.getNumberOfConstraints() == 0) {
selection =
new Selection() {
@Override
public Solution[] select(int arity, Population population) {
Solution[] result = new Solution[arity];
for (int i = 0; i < arity; i++) {
result[i] = population.get(PRNG.nextInt(population.size()));
}
return result;
}
};
} else {
selection =
new TournamentSelection(
2,
new ChainedComparator(
new AggregateConstraintComparator(),
new DominanceComparator() {
@Override
public int compare(Solution solution1, Solution solution2) {
return PRNG.nextBoolean() ? -1 : 1;
}
}));
}
// disable swapping variables in SBX operator to remain consistent with
// Deb's implementation (thanks to Haitham Seada for identifying this
// discrepancy)
if (!properties.contains("sbx.swap")) {
properties.setBoolean("sbx.swap", false);
}
if (!properties.contains("sbx.distributionIndex")) {
properties.setDouble("sbx.distributionIndex", 30.0);
}
if (!properties.contains("pm.distributionIndex")) {
properties.setDouble("pm.distributionIndex", 20.0);
}
Variation variation = OperatorFactory.getInstance().getVariation(null, properties, problem);
return new NSGAII(problem, population, null, selection, variation, initialization);
}