/**
* Updates the solution infeasibility vector with the population given.
* @param[in] constraint_vector c.
* @param[out] solution infeasibility.
*/
double cstrs_self_adaptive::compute_solution_infeasibility(const constraint_vector &c) const
{
// get the constraints dimension
problem::base::c_size_type prob_c_dimension = m_original_problem->get_c_dimension();
problem::base::c_size_type number_of_eq_constraints =
m_original_problem->get_c_dimension() -
m_original_problem->get_ic_dimension();
double solution_infeasibility = 0.;
const std::vector<double> &c_tol = m_original_problem->get_c_tol();
// computes solution infeasibility with the right definition of the constraints (can be in base problem? currently used
// by con2mo as well)
for(problem::base::c_size_type j=0; j<number_of_eq_constraints; j++) {
// test needed otherwise the c_scaling can be 0, and division by 0 occurs
if(m_c_scaling[j] > 0.) {
solution_infeasibility += std::max(0.,(std::abs(c.at(j)) - c_tol.at(j))) / m_c_scaling[j];
}
}
for(problem::base::c_size_type j=number_of_eq_constraints; j<prob_c_dimension; j++) {
if(m_c_scaling[j] > 0.) {
solution_infeasibility += std::max(0.,c.at(j) - c_tol.at(j)) / m_c_scaling[j];
}
}
solution_infeasibility /= prob_c_dimension;
return solution_infeasibility;
}
double mean_violated_constraints(const constraint_vector &c, problem::base_ptr original_problem) {
double viol = 0;
constraint_vector::size_type c_dim = original_problem->get_c_dimension();
problem::base::c_size_type number_of_eq_constraints =
original_problem->get_c_dimension() -
original_problem->get_ic_dimension();
const std::vector<double> &c_tol = original_problem->get_c_tol();
for(constraint_vector::size_type j=0; j<number_of_eq_constraints; j++) {
viol += std::max(0.,(std::abs(c.at(j)) - c_tol.at(j)));
}
for(constraint_vector::size_type j=number_of_eq_constraints; j<c_dim; j++) {
viol += std::max(0.,c.at(j));
}
viol /= c_dim;
return viol;
}
