void erdos_renyi::connect(const vertices_size_type &n)
{
for (std::pair<v_iterator,v_iterator> vertices = get_vertices(); vertices.first != vertices.second; ++vertices.first) {
// Connect n bidirectionally to the other nodes with probability m_prob. Also, avoid to connect n with itself.
if (*vertices.first != n && m_drng() < m_prob) {
add_edge(n,*vertices.first);
add_edge(*vertices.first,n);
}
}
}
void sa_corana::evolve(population &pop) const {
// Let's store some useful variables.
const problem::base &prob = pop.problem();
const problem::base::size_type D = prob.get_dimension(), prob_i_dimension = prob.get_i_dimension(), prob_c_dimension = prob.get_c_dimension(), prob_f_dimension = prob.get_f_dimension();
const decision_vector &lb = prob.get_lb(), &ub = prob.get_ub();
const population::size_type NP = pop.size();
const problem::base::size_type Dc = D - prob_i_dimension;
//We perform some checks to determine wether the problem/population are suitable for sa_corana
if ( Dc == 0 ) {
pagmo_throw(value_error,"There is no continuous part in the problem decision vector for sa_corana to optimise");
}
if ( prob_c_dimension != 0 ) {
pagmo_throw(value_error,"The problem is not box constrained and sa_corana is not suitable to solve it");
}
if ( prob_f_dimension != 1 ) {
pagmo_throw(value_error,"The problem is not single objective and sa_corana is not suitable to solve it");
}
//Determines the number of temperature adjustment for the annealing procedure
const size_t n_T = m_niter / (m_step_adj * m_bin_size * Dc);
// Get out if there is nothing to do.
if (NP == 0 || m_niter == 0) {
return;
}
if (n_T == 0) {
pagmo_throw(value_error,"n_T is zero, increase niter");
}
//Starting point is the best individual
const int bestidx = pop.get_best_idx();
const decision_vector &x0 = pop.get_individual(bestidx).cur_x;
const fitness_vector &fit0 = pop.get_individual(bestidx).cur_f;
//Determines the coefficient to dcrease the temperature
const double Tcoeff = std::pow(m_Tf/m_Ts,1.0/(double)(n_T));
//Stores the current and new points
decision_vector xNEW = x0, xOLD = xNEW;
fitness_vector fNEW = fit0, fOLD = fNEW;
//Stores the adaptive steps of each component (integer part included but not used)
decision_vector step(D,m_range);
//Stores the number of accepted points per component (integer part included but not used)
std::vector<int> acp(D,0) ;
double ratio = 0, currentT = m_Ts, probab = 0;
//Main SA loops
for (size_t jter = 0; jter < n_T; ++jter) {
for (int mter = 0; mter < m_step_adj; ++mter) {
for (int kter = 0; kter < m_bin_size; ++kter) {
size_t nter = boost::uniform_int<int>(0,Dc-1)(m_urng);
for (size_t numb = 0; numb < Dc ; ++numb) {
nter = (nter + 1) % Dc;
//We modify the current point actsol by mutating its nter component within
//a step that we will later adapt
xNEW[nter] = xOLD[nter] + boost::uniform_real<double>(-1,1)(m_drng) * step[nter] * (ub[nter]-lb[nter]);
// If new solution produced is infeasible ignore it
if ((xNEW[nter] > ub[nter]) || (xNEW[nter] < lb[nter])) {
xNEW[nter]=xOLD[nter];
continue;
}
//And we valuate the objective function for the new point
prob.objfun(fNEW,xNEW);
// We decide wether to accept or discard the point
if (prob.compare_fitness(fNEW,fOLD) ) {
//accept
xOLD[nter] = xNEW[nter];
fOLD = fNEW;
acp[nter]++; //Increase the number of accepted values
} else {
//test it with Boltzmann to decide the acceptance
probab = exp ( - fabs(fOLD[0] - fNEW[0] ) / currentT );
// we compare prob with a random probability.
if (probab > m_drng()) {
xOLD[nter] = xNEW[nter];
fOLD = fNEW;
acp[nter]++; //Increase the number of accepted values
} else {
xNEW[nter] = xOLD[nter];
}
} // end if
} // end for(nter = 0; ...
} // end for(kter = 0; ...
// adjust the step (adaptively)
for (size_t iter = 0; iter < Dc; ++iter) {
ratio = (double)acp[iter]/(double)m_bin_size;
acp[iter] = 0; //reset the counter
if (ratio > .6) {
//too many acceptances, increase the step by a factor 3 maximum
step[iter] = step [iter] * (1 + 2 *(ratio - .6)/.4);
} else {
if (ratio < .4) {
//too few acceptance, decrease the step by a factor 3 maximum
step [iter]= step [iter] / (1 + 2 * ((.4 - ratio)/.4));
};
};
//And if it becomes too large, reset it to its initial value
if ( step[iter] > m_range ) {
step [iter] = m_range;
};
}
}
// Cooling schedule
currentT *= Tcoeff;
}
if ( prob.compare_fitness(fOLD,fit0) ){
pop.set_x(bestidx,xOLD); //new evaluation is possible here......
std::transform(xOLD.begin(), xOLD.end(), pop.get_individual(bestidx).cur_x.begin(), xOLD.begin(),std::minus<double>());
pop.set_v(bestidx,xOLD);
}
}
void clustered_ba::connect(const vertices_size_type &idx)
{
pagmo_assert(get_number_of_vertices() > 0);
const vertices_size_type prev_size = get_number_of_vertices() - 1;
if (prev_size < m_m0) {
// If we had not built the initial m0 nodes, do it.
// We want to connect the newcomer island with high probability, and make sure that
// at least one connection exists (otherwise the island stays isolated).
// NOTE: is it worth to make it a user-tunable parameter?
const double prob = 0.0;
// Flag indicating if at least 1 connection was added.
bool connection_added = false;
// Main loop.
for (std::pair<v_iterator,v_iterator> vertices = get_vertices(); vertices.first != vertices.second; ++vertices.first) {
// Do not consider the new vertex itself.
if (*vertices.first != idx) {
if (m_drng() < prob) {
connection_added = true;
// Add the connections
add_edge(*vertices.first,idx);
add_edge(idx,*vertices.first);
}
}
}
// If no connections were established and this is not the first island being inserted,
// establish at least one connection with a random island other than n.
if ((!connection_added) && (prev_size != 0)) {
// Get a random vertex index between 0 and n_vertices - 1. Keep on repeating the procedure if by
// chance we end up on idx again.
boost::uniform_int<vertices_size_type> uni_int(0,get_number_of_vertices() - 1);
vertices_size_type rnd;
do {
rnd = uni_int(m_urng);
} while (rnd == idx);
// Add connections to the random vertex.
add_edge(rnd,idx);
add_edge(idx,rnd);
}
} else {
// Now we need to add j edges, choosing the nodes with a probability
// proportional to their number of connections. We keep track of the
// connection established in order to avoid connecting twice to the same
// node.
// j is a random integer in the range 1 to m.
boost::uniform_int<edges_size_type> uni_int2(1,m_m);
std::size_t i = 0;
std::size_t j = uni_int2(m_urng);
std::pair<v_iterator,v_iterator> vertices;
std::pair<a_iterator,a_iterator> adj_vertices;
while (i < j) {
// Let's find the current total number of edges.
const edges_size_type n_edges = get_number_of_edges();
pagmo_assert(n_edges > 0);
boost::uniform_int<edges_size_type> uni_int(0,n_edges - 1 - i);
// Here we choose a random number between 0 and n_edges - 1 - i.
const edges_size_type rn = uni_int(m_urng);
edges_size_type n = 0;
// Iterate over all vertices and accumulate the number of edges for each of them. Stop when the accumulated number of edges is greater
// than rn. This is equivalent to giving a chance of connection to vertex v directly proportional to the number of edges departing from v.
// You can think of this process as selecting a random edge among all the existing edges and connecting to the vertex from which the
// selected edge departs.
vertices = get_vertices();
for (; vertices.first != vertices.second; ++vertices.first) {
// Do not consider it_n.
if (*vertices.first != idx) {
adj_vertices = get_adjacent_vertices(*vertices.first);
n += boost::numeric_cast<edges_size_type>(std::distance(adj_vertices.first,adj_vertices.second));
if (n > rn) {
break;
}
}
}
pagmo_assert(vertices.first != vertices.second);
// If the candidate was not already connected, then add it.
if (!are_adjacent(idx,*vertices.first)) {
// Connect to nodes that are already adjacent to idx with probability p.
// This step increases clustering in the network.
adj_vertices = get_adjacent_vertices(idx);
for(;adj_vertices.first != adj_vertices.second; ++adj_vertices.first) {
if(m_drng() < m_p && *adj_vertices.first != *vertices.first && !are_adjacent(*adj_vertices.first,*vertices.first)) {
add_edge(*adj_vertices.first, *vertices.first);
add_edge(*vertices.first, *adj_vertices.first);
}
}
// Connect to idx
add_edge(*vertices.first,idx);
add_edge(idx,*vertices.first);
++i;
}
}
}
}
void ihs::evolve(population &pop) const
{
// Let's store some useful variables.
const problem::base &prob = pop.problem();
const problem::base::size_type prob_dimension = prob.get_dimension(), prob_i_dimension = prob.get_i_dimension();
const decision_vector &lb = prob.get_lb(), &ub = prob.get_ub();
const population::size_type pop_size = pop.size();
// Get out if there is nothing to do.
if (pop_size == 0 || m_gen == 0) {
return;
}
decision_vector lu_diff(prob_dimension);
for (problem::base::size_type i = 0; i < prob_dimension; ++i) {
lu_diff[i] = ub[i] - lb[i];
}
// Int distribution to be used when picking random individuals.
boost::uniform_int<population::size_type> uni_int(0,pop_size - 1);
const double c = std::log(m_bw_min/m_bw_max) / m_gen;
// Temporary individual used during evolution.
population::individual_type tmp;
tmp.cur_x.resize(prob_dimension);
tmp.cur_f.resize(prob.get_f_dimension());
tmp.cur_c.resize(prob.get_c_dimension());
for (std::size_t g = 0; g < m_gen; ++g) {
const double ppar_cur = m_ppar_min + ((m_ppar_max - m_ppar_min) * g) / m_gen, bw_cur = m_bw_max * std::exp(c * g);
// Continuous part.
for (problem::base::size_type i = 0; i < prob_dimension - prob_i_dimension; ++i) {
if (m_drng() < m_phmcr) {
// tmp's i-th chromosome element is the one from a randomly chosen individual.
tmp.cur_x[i] = pop.get_individual(uni_int(m_urng)).cur_x[i];
// Do pitch adjustment with ppar_cur probability.
if (m_drng() < ppar_cur) {
// Randomly, add or subtract pitch from the current chromosome element.
if (m_drng() > .5) {
tmp.cur_x[i] += m_drng() * bw_cur * lu_diff[i];
} else {
tmp.cur_x[i] -= m_drng() * bw_cur * lu_diff[i];
}
// Handle the case in which we added or subtracted too much and ended up out
// of boundaries.
if (tmp.cur_x[i] > ub[i]) {
tmp.cur_x[i] = boost::uniform_real<double>(lb[i],ub[i])(m_drng);
} else if (tmp.cur_x[i] < lb[i]) {
tmp.cur_x[i] = boost::uniform_real<double>(lb[i],ub[i])(m_drng);
}
}
} else {
// Pick randomly within the bounds.
tmp.cur_x[i] = boost::uniform_real<double>(lb[i],ub[i])(m_drng);
}
}
//Integer Part
for (problem::base::size_type i = prob_dimension - prob_i_dimension; i < prob_dimension; ++i) {
if (m_drng() < m_phmcr) {
tmp.cur_x[i] = pop.get_individual(uni_int(m_urng)).cur_x[i];
if (m_drng() < ppar_cur) {
if (m_drng() > .5) {
tmp.cur_x[i] += double_to_int::convert(m_drng() * bw_cur * lu_diff[i]);
} else {
tmp.cur_x[i] -= double_to_int::convert(m_drng() * bw_cur * lu_diff[i]);
}
// Wrap over in case we went past the bounds.
if (tmp.cur_x[i] > ub[i]) {
tmp.cur_x[i] = lb[i] + double_to_int::convert(tmp.cur_x[i] - ub[i]) % static_cast<int>(lu_diff[i]);
} else if (tmp.cur_x[i] < lb[i]) {
tmp.cur_x[i] = ub[i] - double_to_int::convert(lb[i] - tmp.cur_x[i]) % static_cast<int>(lu_diff[i]);
}
}
} else {
// Pick randomly within the bounds.
tmp.cur_x[i] = boost::uniform_int<int>(lb[i],ub[i])(m_urng);
}
}
// And we push him back
pop.push_back(tmp.cur_x);
// We locate the worst individual.
const population::size_type worst_idx = pop.get_worst_idx();
// And we get rid of him :)
pop.erase(worst_idx);
}
}
void bee_colony::evolve(population &pop) const
{
// Let's store some useful variables.
const problem::base &prob = pop.problem();
const problem::base::size_type prob_i_dimension = prob.get_i_dimension(), D = prob.get_dimension(), Dc = D - prob_i_dimension, prob_c_dimension = prob.get_c_dimension();
const decision_vector &lb = prob.get_lb(), &ub = prob.get_ub();
const population::size_type NP = (int) pop.size();
//We perform some checks to determine wether the problem/population are suitable for ABC
if ( Dc == 0 ) {
pagmo_throw(value_error,"There is no continuous part in the problem decision vector for ABC to optimise");
}
if ( prob.get_f_dimension() != 1 ) {
pagmo_throw(value_error,"The problem is not single objective and ABC is not suitable to solve it");
}
if ( prob_c_dimension != 0 ) {
pagmo_throw(value_error,"The problem is not box constrained and ABC is not suitable to solve it");
}
if (NP < 2) {
pagmo_throw(value_error,"for ABC at least 2 individuals in the population are needed");
}
// Get out if there is nothing to do.
if (m_iter == 0) {
return;
}
// Some vectors used during evolution are allocated here.
fitness_vector fnew(prob.get_f_dimension());
decision_vector dummy(D,0); //used for initialisation purposes
std::vector<decision_vector > X(NP,dummy); //set of food sources
std::vector<fitness_vector> fit(NP); //food sources fitness
decision_vector temp_solution(D,0);
std::vector<int> trial(NP,0);
std::vector<double> probability(NP);
population::size_type neighbour = 0;
decision_vector::size_type param2change = 0;
std::vector<double> selectionfitness(NP), cumsum(NP), cumsumTemp(NP);
std::vector <population::size_type> selection(NP);
double r = 0;
// Copy the food sources position and their fitness
for ( population::size_type i = 0; i<NP; i++ ) {
X[i] = pop.get_individual(i).cur_x;
fit[i] = pop.get_individual(i).cur_f;
}
// Main ABC loop
for (int j = 0; j < m_iter; ++j) {
//1- Send employed bees
for (population::size_type ii = 0; ii< NP; ++ii) {
//selects a random component (only of the continuous part) of the decision vector
param2change = boost::uniform_int<decision_vector::size_type>(0,Dc-1)(m_urng);
//randomly chose a solution to be used to produce a mutant solution of solution ii
//randomly selected solution must be different from ii
do{
neighbour = boost::uniform_int<population::size_type>(0,NP-1)(m_urng);
}
while(neighbour == ii);
//copy local solution into temp_solution (the whole decision_vector, also the integer part)
for(population::size_type i=0; i<D; ++i) {
temp_solution[i] = X[ii][i];
}
//mutate temp_solution
temp_solution[param2change] = X[ii][param2change] + boost::uniform_real<double>(-1,1)(m_drng) * (X[ii][param2change] - X[neighbour][param2change]);
//if generated parameter value is out of boundaries, it is shifted onto the boundaries*/
if (temp_solution[param2change]<lb[param2change]) {
temp_solution[param2change] = lb[param2change];
}
if (temp_solution[param2change]>ub[param2change]) {
temp_solution[param2change] = ub[param2change];
}
//Calling void prob.objfun(fitness_vector,decision_vector) is more efficient as no memory allocation occur
//A call to fitness_vector prob.objfun(decision_vector) allocates memory for the return value.
prob.objfun(fnew,temp_solution);
//If the new solution is better than the old one replace it with the mutant one and reset its trial counter
if(prob.compare_fitness(fnew, fit[ii])) {
X[ii][param2change] = temp_solution[param2change];
pop.set_x(ii,X[ii]);
prob.objfun(fit[ii], X[ii]); //update the fitness vector
trial[ii] = 0;
}
else {
trial[ii]++; //if the solution can't be improved incrase its trial counter
}
} //End of loop on the population members
//2 - Send onlooker bees
//We scale all fitness values from 0 (worst) to absolute value of the best fitness
fitness_vector worstfit=fit[0];
for (pagmo::population::size_type i = 1; i < NP;i++) {
if (prob.compare_fitness(worstfit,fit[i])) worstfit=fit[i];
}
for (pagmo::population::size_type i = 0; i < NP; i++) {
selectionfitness[i] = fabs(worstfit[0] - fit[i][0]) + 1.;
}
// We build and normalise the cumulative sum
cumsumTemp[0] = selectionfitness[0];
for (pagmo::population::size_type i = 1; i< NP; i++) {
cumsumTemp[i] = cumsumTemp[i - 1] + selectionfitness[i];
}
for (pagmo::population::size_type i = 0; i < NP; i++) {
cumsum[i] = cumsumTemp[i]/cumsumTemp[NP-1];
}
for (pagmo::population::size_type i = 0; i < NP; i++) {
r = m_drng();
for (pagmo::population::size_type j = 0; j < NP; j++) {
if (cumsum[j] > r) {
selection[i]=j;
break;
}
}
}
for(pagmo::population::size_type t = 0; t < NP; ++t) {
r = m_drng();
pagmo::population::size_type ii = selection[t];
//selects a random component (only of the continuous part) of the decision vector
param2change = boost::uniform_int<decision_vector::size_type>(0,Dc-1)(m_urng);
//randomly chose a solution to be used to produce a mutant solution of solution ii
//randomly selected solution must be different from ii
do{
neighbour = boost::uniform_int<population::size_type>(0,NP-1)(m_urng);
}
while(neighbour == ii);
//copy local solution into temp_solution (also integer part)
for(population::size_type i=0; i<D; ++i) {
temp_solution[i] = X[ii][i];
}
//mutate temp_solution
temp_solution[param2change] = X[ii][param2change] + boost::uniform_real<double>(-1,1)(m_drng) * (X[ii][param2change] - X[neighbour][param2change]);
/*if generated parameter value is out of boundaries, it is shifted onto the boundaries*/
if (temp_solution[param2change]<lb[param2change]) {
temp_solution[param2change] = lb[param2change];
}
if (temp_solution[param2change]>ub[param2change]) {
temp_solution[param2change] = ub[param2change];
}
//Calling void prob.objfun(fitness_vector,decision_vector) is more efficient as no memory allocation occur
//A call to fitness_vector prob.objfun(decision_vector) allocates memory for the return value.
prob.objfun(fnew,temp_solution);
//If the new solution is better than the old one replace it with the mutant one and reset its trial counter
if(prob.compare_fitness(fnew, fit[ii])) {
X[ii][param2change] = temp_solution[param2change];
pop.set_x(ii,X[ii]);
prob.objfun(fit[ii], X[ii]); //update the fitness vector
trial[ii] = 0;
}
else {
trial[ii]++; //if the solution can't be improved incrase its trial counter
}
}
//3 - Send scout bees
int maxtrialindex = 0;
for (population::size_type ii=1; ii<NP; ++ii)
{
if (trial[ii] > trial[maxtrialindex]) {
maxtrialindex = ii;
}
}
if(trial[maxtrialindex] >= m_limit)
{
//select a new random solution
for(problem::base::size_type jj = 0; jj < Dc; ++jj) {
X[maxtrialindex][jj] = boost::uniform_real<double>(lb[jj],ub[jj])(m_drng);
}
trial[maxtrialindex] = 0;
pop.set_x(maxtrialindex,X[maxtrialindex]);
}
} // end of main ABC loop
}
