/**
* Returns the penalized objective value.
*/
void cstrs_self_adaptive::objfun_impl(fitness_vector &f, const decision_vector &x) const
{
std::map<std::size_t, fitness_vector>::const_iterator it_f;
std::map<std::size_t, constraint_vector>::const_iterator it_c;
double solution_infeasibility;
it_f = m_map_fitness.find(m_decision_vector_hash(x));
if(it_f != m_map_fitness.end()) {
// we suppose that the constraints is also available
it_c = m_map_constraint.find(m_decision_vector_hash(x));
f = it_f->second;
solution_infeasibility = compute_solution_infeasibility(it_c->second);
} else {
// we compute the function f
m_original_problem->objfun(f, x);
// we compute the constraints
constraint_vector c(m_original_problem->get_c_dimension(), 0.);
m_original_problem->compute_constraints(c,x);
solution_infeasibility = compute_solution_infeasibility(c);
}
if(solution_infeasibility > 0.) {
// apply penalty 1
if(m_apply_penalty_1) {
double inf_tilde = 0.;
inf_tilde = (solution_infeasibility - m_i_hat_down) /
(m_i_hat_up - m_i_hat_down);
f[0] += inf_tilde * (m_f_hat_down[0] - m_f_hat_up[0]);
}
// apply penalty 2
f[0] += m_scaling_factor * std::fabs(f[0]) * ( (std::exp(2. * solution_infeasibility) - 1.) / (std::exp(2.) - 1.) );
}
}
/// Implementation of the constraints computation.
/// Add noises to the computed constraint vector.
void noisy::compute_constraints_impl(constraint_vector &c, const decision_vector &x) const
{
//1 - Initialize a temporary constraint vector storing one trial result
//and we use it also to init the return value
constraint_vector tmp(c.size(),0.0);
c=tmp;
//2 - We set the seed
m_drng.seed(m_seed+m_decision_vector_hash(x));
//3 - We average upon multiple runs
for (unsigned int j=0; j< m_trials; ++j) {
m_original_problem->compute_constraints(tmp, x);
inject_noise_c(tmp);
for (constraint_vector::size_type i=0; i<c.size();++i) {
c[i] = c[i] + tmp[i] / (double)m_trials;
}
}
}
/// Implementation of the objective function.
/// Add noises to the computed fitness vector.
void noisy::objfun_impl(fitness_vector &f, const decision_vector &x) const
{
//1 - Initialize a temporary fitness vector storing one trial result
//and we use it also to init the return value
fitness_vector tmp(f.size(),0.0);
f=tmp;
//2 - We set the seed
m_drng.seed(m_seed+m_decision_vector_hash(x));
//3 - We average upon multiple runs
for (unsigned int j=0; j< m_trials; ++j) {
m_original_problem->objfun(tmp, x);
inject_noise_f(tmp);
for (fitness_vector::size_type i=0; i<f.size();++i) {
f[i] = f[i] + tmp[i] / (double)m_trials;
}
}
}
/**
* Updates the penalty coefficients with the given population.
* @param[in] population pop.
*/
void cstrs_self_adaptive::update_penalty_coeff(const population &pop)
{
if(*m_original_problem != pop.problem()) {
pagmo_throw(value_error,"The problem linked to the population is not the same as the problem given in argument.");
}
// Let's store some useful variables.
const population::size_type pop_size = pop.size();
// Get out if there is nothing to do.
if (pop_size == 0) {
return;
}
m_map_fitness.clear();
m_map_constraint.clear();
// store f and c in maps depending on the the x hash
for(population::size_type i=0; i<pop_size; i++) {
const population::individual_type ¤t_individual = pop.get_individual(i);
// m_map_fitness.insert(std::pair<std::size_t, fitness_vector>(m_decision_vector_hash(current_individual.cur_x),current_individual.cur_f));
m_map_fitness[m_decision_vector_hash(current_individual.cur_x)]=current_individual.cur_f;
m_map_constraint[m_decision_vector_hash(current_individual.cur_x)]=current_individual.cur_c;
}
std::vector<population::size_type> feasible_idx(0);
std::vector<population::size_type> infeasible_idx(0);
// store indexes of feasible and non feasible individuals
for(population::size_type i=0; i<pop_size; i++) {
const population::individual_type ¤t_individual = pop.get_individual(i);
if(m_original_problem->feasibility_c(current_individual.cur_c)) {
feasible_idx.push_back(i);
} else {
infeasible_idx.push_back(i);
}
}
// if the population is only feasible, then nothing is done
if(infeasible_idx.size() == 0) {
update_c_scaling(pop);
m_apply_penalty_1 = false;
m_scaling_factor = 0.;
return;
}
m_apply_penalty_1 = false;
m_scaling_factor = 0.;
// updates the c_scaling, needed for solution infeasibility computation
update_c_scaling(pop);
// evaluate solutions infeasibility
//compute_pop_solution_infeasibility(solution_infeasibility, pop);
std::vector<double> solution_infeasibility(pop_size);
std::fill(solution_infeasibility.begin(),solution_infeasibility.end(),0.);
// evaluate solutions infeasibility
solution_infeasibility.resize(pop_size);
std::fill(solution_infeasibility.begin(),solution_infeasibility.end(),0.);
for(population::size_type i=0; i<pop_size; i++) {
const population::individual_type ¤t_individual = pop.get_individual(i);
// compute the infeasibility of the constraint
solution_infeasibility[i] = compute_solution_infeasibility(current_individual.cur_c);
}
// search position of x_hat_down, x_hat_up and x_hat_round
population::size_type hat_down_idx = -1;
population::size_type hat_up_idx = -1;
population::size_type hat_round_idx = -1;
// first case, the population contains at least one feasible solution
if(feasible_idx.size() > 0) {
// initialize hat_down_idx
hat_down_idx = feasible_idx.at(0);
// x_hat_down = feasible individual with lowest objective value in p
for(population::size_type i=0; i<feasible_idx.size(); i++) {
const population::size_type current_idx = feasible_idx.at(i);
const population::individual_type ¤t_individual = pop.get_individual(current_idx);
if(m_original_problem->compare_fitness(current_individual.cur_f, pop.get_individual(hat_down_idx).cur_f)) {
hat_down_idx = current_idx;
}
}
// hat down is now available
fitness_vector f_hat_down = pop.get_individual(hat_down_idx).cur_f;
// x_hat_up value depends if the population contains infeasible individual with objective
// function better than f_hat_down
bool pop_contains_infeasible_f_better_x_hat_down = false;
for(population::size_type i=0; i<infeasible_idx.size(); i++) {
const population::size_type current_idx = infeasible_idx.at(i);
const population::individual_type ¤t_individual = pop.get_individual(current_idx);
if(m_original_problem->compare_fitness(current_individual.cur_f, f_hat_down)) {
pop_contains_infeasible_f_better_x_hat_down = true;
// initialize hat_up_idx
hat_up_idx = current_idx;
break;
}
}
if(pop_contains_infeasible_f_better_x_hat_down) {
// hat_up_idx is already initizalized
// gets the individual with maximum infeasibility and objfun lower than f_hat_down
for(population::size_type i=0; i<infeasible_idx.size(); i++) {
const population::size_type current_idx = infeasible_idx.at(i);
const population::individual_type ¤t_individual = pop.get_individual(current_idx);
if(m_original_problem->compare_fitness(current_individual.cur_f, f_hat_down) &&
(solution_infeasibility.at(current_idx) >= solution_infeasibility.at(hat_up_idx)) ) {
if(solution_infeasibility.at(current_idx) == solution_infeasibility.at(hat_up_idx)) {
if(m_original_problem->compare_fitness(current_individual.cur_f, pop.get_individual(hat_up_idx).cur_f)) {
hat_up_idx = current_idx;
}
} else {
hat_up_idx = current_idx;
}
}
}
// apply penalty 1
m_apply_penalty_1 = true;
} else {
// all the infeasible soutions have an objective function value greater than f_hat_down
// the worst is the one that has the maximum infeasibility
// initialize hat_up_idx
hat_up_idx = infeasible_idx.at(0);
for(population::size_type i=0; i<infeasible_idx.size(); i++) {
const population::size_type current_idx = infeasible_idx.at(i);
const population::individual_type ¤t_individual = pop.get_individual(current_idx);
if(solution_infeasibility.at(current_idx) >= solution_infeasibility.at(hat_up_idx)) {
if(solution_infeasibility.at(current_idx) == solution_infeasibility.at(hat_up_idx)) {
if(m_original_problem->compare_fitness(pop.get_individual(hat_up_idx).cur_f, current_individual.cur_f)) {
hat_up_idx = current_idx;
}
} else {
hat_up_idx = current_idx;
}
}
}
// do not apply penalty 1
m_apply_penalty_1 = false;
}
} else { // case where there is no feasible solution in the population
// best is the individual with the lowest infeasibility
hat_down_idx = 0;
hat_up_idx = 0;
for(population::size_type i=0; i<pop_size; i++) {
const population::individual_type ¤t_individual = pop.get_individual(i);
if(solution_infeasibility.at(i) <= solution_infeasibility.at(hat_down_idx)) {
if(solution_infeasibility.at(i) == solution_infeasibility.at(hat_down_idx)) {
if(m_original_problem->compare_fitness(current_individual.cur_f, pop.get_individual(hat_down_idx).cur_f)) {
hat_down_idx = i;
}
} else {
hat_down_idx = i;
}
}
}
// worst individual
for(population::size_type i=0; i<pop_size; i++) {
const population::individual_type ¤t_individual = pop.get_individual(i);
if(solution_infeasibility.at(i) >= solution_infeasibility.at(hat_up_idx)) {
if(solution_infeasibility.at(i) == solution_infeasibility.at(hat_up_idx)) {
if(m_original_problem->compare_fitness(pop.get_individual(hat_up_idx).cur_f, current_individual.cur_f)) {
hat_up_idx = i;
}
} else {
hat_up_idx = i;
}
}
}
// apply penalty 1 to the population
m_apply_penalty_1 = true;
}
// stores the hat round idx, i.e. the solution with highest objective
// function value in the population
hat_round_idx = 0;
for(population::size_type i=0; i<pop_size; i++) {
const population::individual_type ¤t_individual = pop.get_individual(i);
if(m_original_problem->compare_fitness(pop.get_individual(hat_round_idx).cur_f, current_individual.cur_f)) {
hat_round_idx = i;
}
}
// get the objective function values of the three individuals
m_f_hat_round = pop.get_individual(hat_round_idx).cur_f;
m_f_hat_down = pop.get_individual(hat_down_idx).cur_f;
m_f_hat_up = pop.get_individual(hat_up_idx).cur_f;
// get the solution infeasibility values of the three individuals
m_i_hat_round = solution_infeasibility.at(hat_round_idx);
m_i_hat_down = solution_infeasibility.at(hat_down_idx);
m_i_hat_up = solution_infeasibility.at(hat_up_idx);
// computes the scaling factor
m_scaling_factor = 0.;
// evaluates scaling factor
if(m_original_problem->compare_fitness(m_f_hat_down, m_f_hat_up)) {
m_scaling_factor = (m_f_hat_round[0] - m_f_hat_up[0]) / m_f_hat_up[0];
} else {
m_scaling_factor = (m_f_hat_round[0] - m_f_hat_down[0]) / m_f_hat_down[0];
}
if(m_f_hat_up[0] == m_f_hat_round[0]) {
m_scaling_factor = 0.;
}
}
