/**
* Run the co-evolution algorithm
*
* @param[in,out] pop input/output pagmo::population to be evolved.
*/
void cstrs_co_evolution::evolve(population &pop) const
{
// Let's store some useful variables.
const problem::base &prob = pop.problem();
const population::size_type pop_size = pop.size();
const problem::base::size_type prob_dimension = prob.get_dimension();
// get the constraints dimension
problem::base::c_size_type prob_c_dimension = prob.get_c_dimension();
//We perform some checks to determine wether the problem/population are suitable for co-evolution
if(prob_c_dimension < 1) {
pagmo_throw(value_error,"The problem is not constrained and co-evolution is not suitable to solve it");
}
if(prob.get_f_dimension() != 1) {
pagmo_throw(value_error,"The problem is multiobjective and co-evolution is not suitable to solve it");
}
// Get out if there is nothing to do.
if(pop_size == 0) {
return;
}
//get the dimension of the chromosome of P2
unsigned int pop_2_dim = 0;
switch(m_method)
{
case algorithm::cstrs_co_evolution::SIMPLE:
{
pop_2_dim = 2;
break;
}
case algorithm::cstrs_co_evolution::SPLIT_NEQ_EQ:
{
pop_2_dim = 4;
break;
}
case algorithm::cstrs_co_evolution::SPLIT_CONSTRAINTS:
pop_2_dim = 2*prob.get_c_dimension();
break;
default:
pagmo_throw(value_error,"The constraints co-evolutionary method is not valid.");
break;
}
// split the population into two populations
// the population P1 associated to the modified problem with penalized fitness
// and population P2 encoding the penalty weights
// Populations size
population::size_type pop_1_size = pop_size;
population::size_type pop_2_size = m_pop_penalties_size;
//Creates problem associated to P2
problem::cstrs_co_evolution_penalty prob_2(prob,pop_2_dim,pop_2_size);
prob_2.set_bounds(m_pen_lower_bound,m_pen_upper_bound);
//random initialization of the P2 chromosome (needed for the fist generation)
std::vector<decision_vector> pop_2_x(pop_2_size);
std::vector<fitness_vector> pop_2_f(pop_2_size);
for(population::size_type j=0; j<pop_2_size; j++) {
pop_2_x[j] = decision_vector(pop_2_dim,0.);
// choose random coefficients between lower bound and upper bound
for(population::size_type i=0; i<pop_2_dim;i++) {
pop_2_x[j][i] = boost::uniform_real<double>(m_pen_lower_bound,m_pen_upper_bound)(m_drng);
}
}
//vector of the population P1. Initialized with clones of the original population
std::vector<population> pop_1_vector;
for(population::size_type i=0; i<pop_2_size; i++){
pop_1_vector.push_back(population(pop));
}
// Main Co-Evolution loop
for(int k=0; k<m_gen; k++) {
// for each individuals of pop 2, evolve the current population,
// and store the position of the feasible idx
for(population::size_type j=0; j<pop_2_size; j++) {
problem::cstrs_co_evolution prob_1(prob, pop_1_vector.at(j), m_method);
// modify the problem by setting decision vector encoding penalty
// coefficients w1 and w2 in prob 1
prob_1.set_penalty_coeff(pop_2_x.at(j));
// creating the POPULATION 1 instance based on the
// updated prob 1
// prob_1 is a BASE_META???? THE CLONE OF prob_1 IN POP_1 IS AT THE LEVEL OF
// THE BASE CLASS AND NOT AT THE LEVEL OF THE BASE_META, NO?!?
population pop_1(prob_1,0);
// initialize P1 chromosomes. The fitnesses related to problem 1 are computed
for(population::size_type i=0; i<pop_1_size; i++) {
pop_1.push_back(pop_1_vector.at(j).get_individual(i).cur_x);
}
// evolve the P1 instance
m_original_algo->evolve(pop_1);
//updating the original problem population (computation of fitness and constraints)
pop_1_vector.at(j).clear();
for(population::size_type i=0; i<pop_1_size; i++){
pop_1_vector.at(j).push_back(pop_1.get_individual(i).cur_x);
}
// set up penalization variables needs for the population 2
// the constraints has not been evaluated yet.
prob_2.update_penalty_coeff(j,pop_2_x.at(j),pop_1_vector.at(j));
}
// creating the POPULATION 2 instance based on the
// updated prob 2
population pop_2(prob_2,0);
// compute the fitness values of the second population
for(population::size_type i=0; i<pop_2_size; i++) {
pop_2.push_back(pop_2_x[i]);
}
m_original_algo_penalties->evolve(pop_2);
// store the new chromosomes
for(population::size_type i=0; i<pop_2_size; i++) {
pop_2_x[i] = pop_2.get_individual(i).cur_x;
pop_2_f[i] = pop_2.get_individual(i).cur_f;
}
// Check the exit conditions (every 40 generations, just as DE)
if(k % 40 == 0) {
// finds the best population
population::size_type best_idx = 0;
for(population::size_type j=1; j<pop_2_size; j++) {
if(pop_2_f[j][0] < pop_2_f[best_idx][0]) {
best_idx = j;
}
}
const population ¤t_population = pop_1_vector.at(best_idx);
decision_vector tmp(prob_dimension);
double dx = 0;
for(decision_vector::size_type i=0; i<prob_dimension; i++) {
tmp[i] = current_population.get_individual(current_population.get_worst_idx()).best_x[i] -
current_population.get_individual(current_population.get_best_idx()).best_x[i];
dx += std::fabs(tmp[i]);
}
if(dx < m_xtol ) {
if (m_screen_output) {
std::cout << "Exit condition -- xtol < " << m_xtol << std::endl;
}
break;
}
double mah = std::fabs(current_population.get_individual(current_population.get_worst_idx()).best_f[0] -
current_population.get_individual(current_population.get_best_idx()).best_f[0]);
if(mah < m_ftol) {
if(m_screen_output) {
std::cout << "Exit condition -- ftol < " << m_ftol << std::endl;
}
break;
}
// outputs current values
if(m_screen_output) {
std::cout << "Generation " << k << " ***" << std::endl;
std::cout << " Best global fitness: " << current_population.champion().f << std::endl;
std::cout << " xtol: " << dx << ", ftol: " << mah << std::endl;
std::cout << " xtol: " << dx << ", ftol: " << mah << std::endl;
}
}
}
// find the best fitness population in the final pop
population::size_type best_idx = 0;
for(population::size_type j=1; j<pop_2_size; j++) {
if(pop_2_f[j][0] < pop_2_f[best_idx][0]) {
best_idx = j;
}
}
// store the final population in the main population
// can't avoid to recompute the vectors here, otherwise we
// clone the problem stored in the population with the
// pop = operator!
pop.clear();
for(population::size_type i=0; i<pop_1_size; i++) {
pop.push_back(pop_1_vector.at(best_idx).get_individual(i).cur_x);
}
}
void destroy_stack_2()
{
while(!is_empty_2()) pop_2();
}
