void FireflyAlgorithm::startState(Problem &P, const EndCriteria &endCriteria) {
N_ = P.currentValue().size();
x_.reserve(M_);
xI_.reserve(M_);
xRW_.reserve(M_);
values_.reserve(M_);
uX_ = P.constraint().upperBound(P.currentValue());
lX_ = P.constraint().lowerBound(P.currentValue());
Array bounds = uX_ - lX_;
//Random initialization is done by Sobol sequence
SobolRsg sobol(N_);
//Prepare containers
for (Size i = 0; i < M_; i++) {
const SobolRsg::sample_type::value_type &sample = sobol.nextSequence().value;
x_.push_back(Array(N_, 0.0));
xI_.push_back(Array(N_, 0.0));
xRW_.push_back(Array(N_, 0.0));
Array& x = x_.back();
for (Size j = 0; j < N_; j++) {
//Assign X=lb+(ub-lb)*random
x[j] = lX_[j] + bounds[j] * sample[j];
}
//Evaluate point
values_.push_back(std::make_pair(P.value(x), i));
}
//init intensity & randomWalk
intensity_->init(this);
randomWalk_->init(this);
}
EndCriteria::Type DifferentialEvolution::minimize(Problem& p, const EndCriteria& endCriteria) {
EndCriteria::Type ecType;
upperBound_ = p.constraint().upperBound(p.currentValue());
lowerBound_ = p.constraint().lowerBound(p.currentValue());
currGenSizeWeights_ = Array(configuration().populationMembers,
configuration().stepsizeWeight);
currGenCrossover_ = Array(configuration().populationMembers,
configuration().crossoverProbability);
std::vector<Candidate> population(configuration().populationMembers,
Candidate(p.currentValue().size()));
fillInitialPopulation(population, p);
std::partial_sort(population.begin(), population.begin() + 1, population.end(),
sort_by_cost());
bestMemberEver_ = population.front();
Real fxOld = population.front().cost;
Size iteration = 0, stationaryPointIteration = 0;
// main loop - calculate consecutive emerging populations
while (!endCriteria.checkMaxIterations(iteration++, ecType)) {
calculateNextGeneration(population, p.costFunction());
std::partial_sort(population.begin(), population.begin() + 1, population.end(),
sort_by_cost());
if (population.front().cost < bestMemberEver_.cost)
bestMemberEver_ = population.front();
Real fxNew = population.front().cost;
if (endCriteria.checkStationaryFunctionValue(fxOld, fxNew, stationaryPointIteration,
ecType))
break;
fxOld = fxNew;
};
p.setCurrentValue(bestMemberEver_.values);
p.setFunctionValue(bestMemberEver_.cost);
return ecType;
}
Real Simplex::extrapolate(Problem& P,
Size iHighest,
Real &factor) const {
Array pTry;
do {
Size dimensions = values_.size() - 1;
Real factor1 = (1.0 - factor)/dimensions;
Real factor2 = factor1 - factor;
#if defined(QL_ARRAY_EXPRESSIONS)
pTry = sum_*factor1 - vertices_[iHighest]*factor2;
#else
// composite expressions fail to compile with gcc 3.4 on windows
pTry = sum_*factor1;
pTry -= vertices_[iHighest]*factor2;
#endif
factor *= 0.5;
} while (!P.constraint().test(pTry) && std::fabs(factor) > QL_EPSILON);
if (std::fabs(factor) <= QL_EPSILON) {
return values_[iHighest];
}
factor *= 2.0;
Real vTry = P.value(pTry);
if (vTry < values_[iHighest]) {
values_[iHighest] = vTry;
#if defined(QL_ARRAY_EXPRESSIONS)
sum_ += pTry - vertices_[iHighest];
#else
sum_ += pTry;
sum_ -= vertices_[iHighest];
#endif
vertices_[iHighest] = pTry;
}
return vTry;
}
Real GoldsteinLineSearch::operator()(Problem& P,
EndCriteria::Type& ecType,
const EndCriteria& endCriteria,
const Real t_ini)
{
Constraint& constraint = P.constraint();
succeed_=true;
bool maxIter = false;
Real /*qtold,*/ t = t_ini; // see below, this is never used ?
Size loopNumber = 0;
Real q0 = P.functionValue();
Real qp0 = P.gradientNormValue();
Real tl = 0.0;
Real tr = 0.0;
qt_ = q0;
qpt_ = (gradient_.empty()) ? qp0 : -DotProduct(gradient_,searchDirection_);
// Initialize gradient
gradient_ = Array(P.currentValue().size());
// Compute new point
xtd_ = P.currentValue();
t = update(xtd_, searchDirection_, t, constraint);
// Compute function value at the new point
qt_ = P.value (xtd_);
while ((qt_ - q0) < -beta_*t*qpt_ || (qt_ - q0) > -alpha_*t*qpt_) {
if ((qt_ - q0) > -alpha_*t*qpt_)
tr = t;
else
tl = t;
++loopNumber;
// calculate the new step
if (close_enough(tr, 0.0))
t *= extrapolation_;
else
t = (tl + tr) / 2.0;
// Store old value of the function
// qtold = qt_; // this is never used ?
// New point value
xtd_ = P.currentValue();
t = update(xtd_, searchDirection_, t, constraint);
// Compute function value at the new point
qt_ = P.value (xtd_);
P.gradient (gradient_, xtd_);
// and it squared norm
maxIter = endCriteria.checkMaxIterations(loopNumber, ecType);
if (maxIter)
break;
}
if (maxIter)
succeed_ = false;
// Compute new gradient
P.gradient(gradient_, xtd_);
// and it squared norm
qpt_ = DotProduct(gradient_, gradient_);
// Return new step value
return t;
}
EndCriteria::Type Simplex::minimize(Problem& P,
const EndCriteria& endCriteria) {
// set up of the problem
//Real ftol = endCriteria.functionEpsilon(); // end criteria on f(x) (see Numerical Recipes in C++, p.410)
Real xtol = endCriteria.rootEpsilon(); // end criteria on x (see GSL v. 1.9, http://www.gnu.org/software/gsl/)
Size maxStationaryStateIterations_
= endCriteria.maxStationaryStateIterations();
EndCriteria::Type ecType = EndCriteria::None;
P.reset();
Array x_ = P.currentValue();
Integer iterationNumber_=0;
// Initialize vertices of the simplex
bool end = false;
Size n = x_.size(), i;
vertices_ = std::vector<Array>(n+1, x_);
for (i=0; i<n; i++) {
Array direction(n, 0.0);
direction[i] = 1.0;
P.constraint().update(vertices_[i+1], direction, lambda_);
}
// Initialize function values at the vertices of the simplex
values_ = Array(n+1, 0.0);
for (i=0; i<=n; i++)
values_[i] = P.value(vertices_[i]);
// Loop looking for minimum
do {
sum_ = Array(n, 0.0);
Size i;
for (i=0; i<=n; i++)
sum_ += vertices_[i];
// Determine the best (iLowest), worst (iHighest)
// and 2nd worst (iNextHighest) vertices
Size iLowest = 0;
Size iHighest, iNextHighest;
if (values_[0]<values_[1]) {
iHighest = 1;
iNextHighest = 0;
} else {
iHighest = 0;
iNextHighest = 1;
}
for (i=1;i<=n; i++) {
if (values_[i]>values_[iHighest]) {
iNextHighest = iHighest;
iHighest = i;
} else {
if ((values_[i]>values_[iNextHighest]) && i!=iHighest)
iNextHighest = i;
}
if (values_[i]<values_[iLowest])
iLowest = i;
}
// Now compute accuracy, update iteration number and check end criteria
//// Numerical Recipes exit strategy on fx (see NR in C++, p.410)
//Real low = values_[iLowest];
//Real high = values_[iHighest];
//Real rtol = 2.0*std::fabs(high - low)/
// (std::fabs(high) + std::fabs(low) + QL_EPSILON);
//++iterationNumber_;
//if (rtol < ftol ||
// endCriteria.checkMaxIterations(iterationNumber_, ecType)) {
// GSL exit strategy on x (see GSL v. 1.9, http://www.gnu.org/software/gsl
Real simplexSize = computeSimplexSize(vertices_);
++iterationNumber_;
if (simplexSize < xtol ||
endCriteria.checkMaxIterations(iterationNumber_, ecType)) {
endCriteria.checkStationaryPoint(0.0, 0.0,
maxStationaryStateIterations_, ecType); // PC this is probably not meant like this ? Use separate counter ?
endCriteria.checkMaxIterations(iterationNumber_, ecType);
x_ = vertices_[iLowest];
Real low = values_[iLowest];
P.setFunctionValue(low);
P.setCurrentValue(x_);
return ecType;
}
// If end criteria is not met, continue
Real factor = -1.0;
Real vTry = extrapolate(P, iHighest, factor);
if ((vTry <= values_[iLowest]) && (factor == -1.0)) {
factor = 2.0;
extrapolate(P, iHighest, factor);
} else if (std::fabs(factor) > QL_EPSILON) {
if (vTry >= values_[iNextHighest]) {
Real vSave = values_[iHighest];
factor = 0.5;
vTry = extrapolate(P, iHighest, factor);
if (vTry >= vSave && std::fabs(factor) > QL_EPSILON) {
for (Size i=0; i<=n; i++) {
if (i!=iLowest) {
#if defined(QL_ARRAY_EXPRESSIONS)
vertices_[i] =
0.5*(vertices_[i] + vertices_[iLowest]);
#else
vertices_[i] += vertices_[iLowest];
vertices_[i] *= 0.5;
#endif
values_[i] = P.value(vertices_[i]);
}
}
}
}
}
// If can't extrapolate given the constraints, exit
if (std::fabs(factor) <= QL_EPSILON) {
x_ = vertices_[iLowest];
Real low = values_[iLowest];
P.setFunctionValue(low);
P.setCurrentValue(x_);
return EndCriteria::StationaryFunctionValue;
}
} while (end == false);
QL_FAIL("optimization failed: unexpected behaviour");
}
EndCriteria::Type FireflyAlgorithm::minimize(Problem &P, const EndCriteria &endCriteria) {
QL_REQUIRE(!P.constraint().empty(), "Firefly Algorithm is a constrained optimizer");
EndCriteria::Type ecType = EndCriteria::None;
P.reset();
Size iteration = 0;
Size iterationStat = 0;
Size maxIteration = endCriteria.maxIterations();
Size maxIStationary = endCriteria.maxStationaryStateIterations();
startState(P, endCriteria);
bool isFA = Mfa_ > 0 ? true : false;
//Variables for DE
Array z(N_, 0.0);
Size indexBest, indexR1, indexR2;
//Set best value & position
Real bestValue = values_[0].first;
Size bestPosition = 0;
for (Size i = 1; i < M_; i++) {
if (values_[i].first < bestValue) {
bestPosition = i;
bestValue = values_[i].first;
}
}
Array bestX = x_[bestPosition];
//Run optimization
do {
iteration++;
iterationStat++;
//Check if stopping criteria is met
if (iteration > maxIteration || iterationStat > maxIStationary)
break;
//Divide into two subpopulations
//First sort values
std::sort(values_.begin(), values_.end());
//Differential evolution
if(Mfa_ < M_){
Size indexBest = values_[0].second;
Array& xBest = x_[indexBest];
for (Size i = Mfa_; i < M_; i++) {
if (!isFA) {
//Pure DE requires random index
indexBest = drawIndex_();
xBest = x_[indexBest];
}
do {
indexR1 = drawIndex_();
} while(indexR1 == indexBest);
do {
indexR2 = drawIndex_();
} while(indexR2 == indexBest || indexR2 == indexR1);
Size index = values_[i].second;
Array& x = x_[index];
Array& xR1 = x_[indexR1];
Array& xR2 = x_[indexR2];
for (Size j = 0; j < N_; j++) {
if (rng_.nextReal() <= crossover_) {
//Change x[j] according to crossover
z[j] = xBest[j] + mutation_*(xR1[j] - xR2[j]);
} else {
z[j] = x[j];
}
}
Real val = P.value(z);
if (val < values_[index].first) {
//Accept new point
x = z;
values_[index].first = val;
//mark best
if (val < bestValue) {
bestValue = val;
bestX = x;
iterationStat = 0;
}
}
}
}
//Firefly algorithm
if(isFA){
//According to the intensity, determine best global position
intensity_->findBrightest();
//Prepare random walk
randomWalk_->walk();
//Loop over particles
for (Size i = 0; i < Mfa_; i++) {
Size index = values_[i].second;
Array& x = x_[index];
Array& xI = xI_[index];
Array& xRW = xRW_[index];
//Loop over dimensions
for (Size j = 0; j < N_; j++) {
//Update position
x[j] += xI[j] + xRW[j];
//Enforce bounds on positions
if (x[j] < lX_[j]) {
x[j] = lX_[j];
}
else if (x[j] > uX_[j]) {
x[j] = uX_[j];
}
}
//Evaluate x & mark best
values_[index].first = P.value(x);
if (values_[index].first < bestValue) {
bestValue = values_[index].first;
bestX = x;
iterationStat = 0;
}
}
}
} while (true);
if (iteration > maxIteration)
ecType = EndCriteria::MaxIterations;
else
ecType = EndCriteria::StationaryPoint;
//Set result to best point
P.setCurrentValue(bestX);
P.setFunctionValue(bestValue);
return ecType;
}
