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Refactor calculatedObjectives in NSGA2 optimizer #434

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rcurtin merged 3 commits into from
Sep 25, 2025
Merged

Refactor calculatedObjectives in NSGA2 optimizer #434

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c49be3a
Refactor calculatedObjectives in NSGA2 optimizer
XueSongTap Aug 28, 2025
b678088
Use Mat instead of Cube.
rcurtin Sep 23, 2025
b899e1b
Merge remote-tracking branch 'origin/master' into HEAD
rcurtin Sep 23, 2025
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12 changes: 6 additions & 6 deletions include/ensmallen_bits/nsga2/nsga2.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -206,23 +206,23 @@ class NSGA2
* @tparam MatType Type of matrix to optimize.
* @param population The elite population.
* @param objectives The set of objectives.
* @param calculatedObjectives Vector to store calculated objectives.
* @param calculatedObjectives Cube to store calculated objectives (numObjectives x 1 x populationSize).
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*/
template<std::size_t I = 0,
typename MatType,
typename ...ArbitraryFunctionType>
typename std::enable_if<I == sizeof...(ArbitraryFunctionType), void>::type
EvaluateObjectives(std::vector<MatType>&,
std::tuple<ArbitraryFunctionType...>&,
std::vector<arma::Col<typename MatType::elem_type> >&);
arma::Cube<typename MatType::elem_type>&);

template<std::size_t I = 0,
typename MatType,
typename ...ArbitraryFunctionType>
typename std::enable_if<I < sizeof...(ArbitraryFunctionType), void>::type
EvaluateObjectives(std::vector<MatType>& population,
std::tuple<ArbitraryFunctionType...>& objectives,
std::vector<arma::Col<typename MatType::elem_type> >&
arma::Cube<typename MatType::elem_type>&
calculatedObjectives);

/**
Expand Down Expand Up @@ -283,7 +283,7 @@ class NSGA2
void FastNonDominatedSort(
std::vector<std::vector<size_t> >& fronts,
std::vector<size_t>& ranks,
std::vector<arma::Col<typename MatType::elem_type> >& calculatedObjectives);
arma::Cube<typename MatType::elem_type>& calculatedObjectives);

/**
* Operator to check if one candidate Pareto-dominates the other.
Expand All @@ -300,7 +300,7 @@ class NSGA2
*/
template<typename MatType>
bool Dominates(
std::vector<arma::Col<typename MatType::elem_type> >& calculatedObjectives,
arma::Cube<typename MatType::elem_type>& calculatedObjectives,
size_t candidateP,
size_t candidateQ);

Expand All @@ -315,7 +315,7 @@ class NSGA2
template <typename MatType>
void CrowdingDistanceAssignment(
const std::vector<size_t>& front,
std::vector<arma::Col<typename MatType::elem_type>>& calculatedObjectives,
arma::Cube<typename MatType::elem_type>& calculatedObjectives,
std::vector<typename MatType::elem_type>& crowdingDistance);

/**
Expand Down
47 changes: 20 additions & 27 deletions include/ensmallen_bits/nsga2/nsga2_impl.hpp
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Expand Up @@ -104,8 +104,8 @@ typename MatType::elem_type NSGA2::Optimize(
numObjectives = sizeof...(ArbitraryFunctionType);
numVariables = iterate.n_rows;

// Cache calculated objectives.
std::vector<arma::Col<ElemType> > calculatedObjectives(populationSize);
// Cache calculated objectives as a cube: (numObjectives x 1 x populationSize).
arma::Cube<ElemType> calculatedObjectives(numObjectives, 1, populationSize, arma::fill::zeros);

// Population size reserved to 2 * populationSize + 1 to accommodate
// for the size of intermediate candidate population.
Expand Down Expand Up @@ -152,9 +152,8 @@ typename MatType::elem_type NSGA2::Optimize(
BinaryTournamentSelection(population, castedLowerBound, castedUpperBound);

// Evaluate the objectives for the new population.
calculatedObjectives.resize(population.size());
std::fill(calculatedObjectives.begin(), calculatedObjectives.end(),
arma::Col<ElemType>(numObjectives, arma::fill::zeros));
calculatedObjectives.set_size(numObjectives, 1, population.size());
calculatedObjectives.zeros();
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EvaluateObjectives(population, objectives, calculatedObjectives);

// Perform fast non dominated sort on P_t ∪ G_t.
Expand Down Expand Up @@ -207,13 +206,12 @@ typename MatType::elem_type NSGA2::Optimize(
}

// Set the candidates from the Pareto Front as the output.
paretoFront.set_size(calculatedObjectives[0].n_rows, calculatedObjectives[0].n_cols,
fronts[0].size());
paretoFront.set_size(numObjectives, 1, fronts[0].size());
// The Pareto Front is stored, can be obtained via ParetoFront() getter.
for (size_t solutionIdx = 0; solutionIdx < fronts[0].size(); ++solutionIdx)
{
paretoFront.slice(solutionIdx) =
arma::conv_to<arma::mat>::from(calculatedObjectives[fronts[0][solutionIdx]]);
paretoFront.slice(solutionIdx) =
arma::conv_to<arma::mat>::from(calculatedObjectives.slice(fronts[0][solutionIdx]));
}

// Clear rcFront, in case it is later requested by the user for reverse
Expand All @@ -227,10 +225,8 @@ typename MatType::elem_type NSGA2::Optimize(

ElemType performance = std::numeric_limits<ElemType>::max();

for (const arma::Col<ElemType>& objective: calculatedObjectives)
if (arma::accu(objective) < performance)
performance = arma::accu(objective);

for (size_t i = 0; i < calculatedObjectives.n_slices; ++i)
performance = std::min(performance, arma::accu(calculatedObjectives.slice(i)));
return performance;
}

Expand All @@ -242,7 +238,7 @@ typename std::enable_if<I == sizeof...(ArbitraryFunctionType), void>::type
NSGA2::EvaluateObjectives(
std::vector<MatType>&,
std::tuple<ArbitraryFunctionType...>&,
std::vector<arma::Col<typename MatType::elem_type> >&)
arma::Cube<typename MatType::elem_type>&)
{
// Nothing to do here.
}
Expand All @@ -255,11 +251,11 @@ typename std::enable_if<I < sizeof...(ArbitraryFunctionType), void>::type
NSGA2::EvaluateObjectives(
std::vector<MatType>& population,
std::tuple<ArbitraryFunctionType...>& objectives,
std::vector<arma::Col<typename MatType::elem_type> >& calculatedObjectives)
arma::Cube<typename MatType::elem_type>& calculatedObjectives)
{
for (size_t i = 0; i < populationSize; i++)
{
calculatedObjectives[i](I) = std::get<I>(objectives).Evaluate(population[i]);
calculatedObjectives(I, 0, i) = std::get<I>(objectives).Evaluate(population[i]);
EvaluateObjectives<I+1, MatType, ArbitraryFunctionType...>(population, objectives,
calculatedObjectives);
}
Expand Down Expand Up @@ -339,7 +335,7 @@ template<typename MatType>
inline void NSGA2::FastNonDominatedSort(
std::vector<std::vector<size_t> >& fronts,
std::vector<size_t>& ranks,
std::vector<arma::Col<typename MatType::elem_type> >& calculatedObjectives)
arma::Cube<typename MatType::elem_type>& calculatedObjectives)
{
std::map<size_t, size_t> dominationCount;
std::map<size_t, std::set<size_t> > dominated;
Expand Down Expand Up @@ -398,22 +394,22 @@ inline void NSGA2::FastNonDominatedSort(
//! Check if a candidate Pareto dominates another candidate.
template<typename MatType>
inline bool NSGA2::Dominates(
std::vector<arma::Col<typename MatType::elem_type> >& calculatedObjectives,
arma::Cube<typename MatType::elem_type>& calculatedObjectives,
size_t candidateP,
size_t candidateQ)
{
bool allBetterOrEqual = true;
bool atleastOneBetter = false;
size_t n_objectives = calculatedObjectives[0].n_elem;
const size_t n_objectives = calculatedObjectives.n_rows;

for (size_t i = 0; i < n_objectives; i++)
{
// P is worse than Q for the i-th objective function.
if (calculatedObjectives[candidateP](i) > calculatedObjectives[candidateQ](i))
if (calculatedObjectives(i, 0, candidateP) > calculatedObjectives(i, 0, candidateQ))
allBetterOrEqual = false;

// P is better than Q for the i-th objective function.
else if (calculatedObjectives[candidateP](i) < calculatedObjectives[candidateQ](i))
else if (calculatedObjectives(i, 0, candidateP) < calculatedObjectives(i, 0, candidateQ))
atleastOneBetter = true;
}

Expand All @@ -424,7 +420,7 @@ inline bool NSGA2::Dominates(
template <typename MatType>
inline void NSGA2::CrowdingDistanceAssignment(
const std::vector<size_t>& front,
std::vector<arma::Col<typename MatType::elem_type>>& calculatedObjectives,
arma::Cube<typename MatType::elem_type>& calculatedObjectives,
std::vector<typename MatType::elem_type>& crowdingDistance)
{
// Convenience typedefs.
Expand All @@ -438,11 +434,8 @@ inline void NSGA2::CrowdingDistanceAssignment(
{
// Cache fValues of individuals for current objective.
arma::Col<ElemType> fValues(fSize);
std::transform(front.begin(), front.end(), fValues.begin(),
[&](const size_t& individual)
{
return calculatedObjectives[individual](m);
});
for (size_t k = 0; k < fSize; ++k)
fValues(k) = calculatedObjectives(m, 0, front[k]);
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// Sort front indices by ascending fValues for current objective.
std::sort(sortedIdx.begin(), sortedIdx.end(),
Expand Down