/**
* Runs the Inverover algorithm for the number of generations specified in the constructor.
*
* @param[in,out] pop input/output pagmo::population to be evolved.
*/
void inverover::evolve(population &pop) const
{
const problem::base_tsp* prob;
//check if problem is of type pagmo::problem::base_tsp
try {
const problem::base_tsp& tsp_prob = dynamic_cast<const problem::base_tsp &>(pop.problem());
prob = &tsp_prob;
}
catch (const std::bad_cast& e) {
pagmo_throw(value_error,"Problem not of type pagmo::problem::base_tsp");
}
// Let's store some useful variables.
const population::size_type NP = pop.size();
const problem::base::size_type Nv = prob->get_n_cities();
// Initializing the random number generators
boost::uniform_real<double> uniform(0.0, 1.0);
boost::variate_generator<boost::lagged_fibonacci607 &, boost::uniform_real<double> > unif_01(m_drng, uniform);
boost::uniform_int<int> NPless1(0, NP - 2);
boost::variate_generator<boost::mt19937 &, boost::uniform_int<int> > unif_NPless1(m_urng, NPless1);
boost::uniform_int<int> Nv_(0, Nv - 1);
boost::variate_generator<boost::mt19937 &, boost::uniform_int<int> > unif_Nv(m_urng, Nv_);
boost::uniform_int<int> Nvless1(0, Nv - 2);
boost::variate_generator<boost::mt19937 &, boost::uniform_int<int> > unif_Nvless1(m_urng, Nvless1);
//create own local population
std::vector<decision_vector> my_pop(NP, decision_vector(Nv));
//check if some individuals in the population that is passed as a function input are feasible.
bool feasible;
std::vector<int> not_feasible;
for (size_t i = 0; i < NP; i++) {
feasible = prob->feasibility_x(pop.get_individual(i).cur_x);
if(feasible) { //if feasible store it in my_pop
switch(prob->get_encoding()) {
case problem::base_tsp::FULL:
my_pop[i] = prob->full2cities(pop.get_individual(i).cur_x);
break;
case problem::base_tsp::RANDOMKEYS:
my_pop[i] = prob->randomkeys2cities(pop.get_individual(i).cur_x);
break;
case problem::base_tsp::CITIES:
my_pop[i] = pop.get_individual(i).cur_x;
break;
}
} else {
not_feasible.push_back(i);
}
}
//replace the not feasible individuals by feasible ones
int i;
switch (m_ini_type) {
case 0:
{
//random initialization (produces feasible individuals)
for (size_t ii = 0; ii < not_feasible.size(); ii++) {
i = not_feasible[ii];
for (size_t j = 0; j < Nv; j++) {
my_pop[i][j] = j;
}
}
int tmp;
size_t rnd_idx;
for (size_t j = 1; j < Nv-1; j++) {
boost::uniform_int<int> dist_(j, Nv - 1);
boost::variate_generator<boost::mt19937 &, boost::uniform_int<int> > dist(m_urng,dist_);
for (size_t ii = 0; ii < not_feasible.size(); ii++) {
i = not_feasible[ii];
rnd_idx = dist();
tmp = my_pop[i][j];
my_pop[i][j] = my_pop[i][rnd_idx];
my_pop[i][rnd_idx] = tmp;
}
}
break;
}
case 1:
{
//initialize with nearest neighbor algorithm
std::vector<int> starting_notes(std::max(Nv,not_feasible.size()));
for (size_t j = 0; j < starting_notes.size(); j++) {
starting_notes[j] = j;
}
//std::shuffle(starting_notes.begin(), starting_notes.end(), m_urng);
for (size_t ii = 0; ii < not_feasible.size(); ii++) {
i = not_feasible[ii];
pagmo::population one_ind_pop(pop.problem(), 1);
std::cout << starting_notes[i] << ' ';
pagmo::algorithm::nn_tsp algo(starting_notes[i] % Nv);
algo.evolve(one_ind_pop);
switch( prob->get_encoding() ) {
case problem::base_tsp::FULL:
my_pop[i] = prob->full2cities(one_ind_pop.get_individual(0).cur_x);
break;
case problem::base_tsp::RANDOMKEYS:
my_pop[i] = prob->randomkeys2cities(one_ind_pop.get_individual(0).cur_x);
break;
case problem::base_tsp::CITIES:
my_pop[i] = one_ind_pop.get_individual(0).cur_x;
break;
}
std::cout << i << ' ' << one_ind_pop.get_individual(0).cur_f << std::endl;
}
break;
}
default:
pagmo_throw(value_error,"Invalid initialization type");
}
std::vector<fitness_vector> fitness(NP, fitness_vector(1));
for(size_t i=0; i < NP; i++){
switch( prob->get_encoding() ) {
case problem::base_tsp::FULL:
fitness[i] = prob->objfun(prob->full2cities(my_pop[i]));
break;
case problem::base_tsp::RANDOMKEYS:
fitness[i] = prob->objfun(prob->cities2randomkeys(my_pop[i], pop.get_individual(i).cur_x));
break;
case problem::base_tsp::CITIES:
fitness[i] = prob->objfun(my_pop[i]);
break;
}
}
decision_vector tmp_tour(Nv);
bool stop, changed;
size_t rnd_num, i2, pos1_c1, pos1_c2, pos2_c1, pos2_c2; //pos2_c1 denotes the position of city1 in parent2
fitness_vector fitness_tmp;
//InverOver main loop
for(int iter = 0; iter < m_gen; iter++) {
for(size_t i1 = 0; i1 < NP; i1++) {
tmp_tour = my_pop[i1];
pos1_c1 = unif_Nv();
stop = false;
changed = false;
while(!stop){
if(unif_01() < m_ri) {
rnd_num = unif_Nvless1();
pos1_c2 = (rnd_num == pos1_c1? Nv-1:rnd_num);
} else {
i2 = unif_NPless1();
i2 = (i2 == i1? NP-1:i2);
pos2_c1 = std::find(my_pop[i2].begin(),my_pop[i2].end(),tmp_tour[pos1_c1])-my_pop[i2].begin();
pos2_c2 = (pos2_c1 == Nv-1? 0:pos2_c1+1);
pos1_c2 = std::find(tmp_tour.begin(),tmp_tour.end(),my_pop[i2][pos2_c2])-tmp_tour.begin();
}
stop = (abs(pos1_c1-pos1_c2)==1 || static_cast<problem::base::size_type>(abs(pos1_c1-pos1_c2))==Nv-1);
if(!stop) {
changed = true;
if(pos1_c1<pos1_c2) {
for(size_t l=0; l < (double (pos1_c2-pos1_c1-1)/2); l++) {
std::swap(tmp_tour[pos1_c1+1+l],tmp_tour[pos1_c2-l]);
}
pos1_c1 = pos1_c2;
} else {
//inverts the section from c1 to c2 (see documentation Note3)
for(size_t l=0; l < (double (pos1_c1-pos1_c2-1)/2); l++) {
std::swap(tmp_tour[pos1_c2+l],tmp_tour[pos1_c1-l-1]);
}
pos1_c1 = (pos1_c2 == 0? Nv-1:pos1_c2-1);
}
}
} //end of while loop (looping over a single indvidual)
if(changed) {
switch(prob->get_encoding()) {
case problem::base_tsp::FULL:
fitness_tmp = prob->objfun(prob->full2cities(tmp_tour));
break;
case problem::base_tsp::RANDOMKEYS: //using "randomly" index 0 as a temporary template
fitness_tmp = prob->objfun(prob->cities2randomkeys(tmp_tour, pop.get_individual(0).cur_x));
break;
case problem::base_tsp::CITIES:
fitness_tmp = prob->objfun(tmp_tour);
break;
}
if(prob->compare_fitness(fitness_tmp,fitness[i1])) { //replace individual?
my_pop[i1] = tmp_tour;
fitness[i1][0] = fitness_tmp[0];
}
}
} // end of loop over population
} // end of loop over generations
//change representation of tour
for (size_t ii = 0; ii < NP; ii++) {
switch(prob->get_encoding()) {
case problem::base_tsp::FULL:
pop.set_x(ii,prob->cities2full(my_pop[ii]));
break;
case problem::base_tsp::RANDOMKEYS:
pop.set_x(ii,prob->cities2randomkeys(my_pop[ii],pop.get_individual(ii).cur_x));
break;
case problem::base_tsp::CITIES:
pop.set_x(ii,my_pop[ii]);
break;
}
}
} // end of evolve
/**
* 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);
}
}
/**
* Runs the Inverover algorithm for the number of generations specified in the constructor.
*
* @param[in,out] pop input/output pagmo::population to be evolved.
*/
void inverover::evolve(population &pop) const
{
const problem::tsp* prob;
//check if problem is of type pagmo::problem::tsp
try
{
const problem::tsp& tsp_prob = dynamic_cast<const problem::tsp &>(pop.problem());
prob = &tsp_prob;
}
catch (const std::bad_cast& e)
{
pagmo_throw(value_error,"Problem not of type pagmo::problem::tsp");
}
// Let's store some useful variables.
const population::size_type NP = pop.size();
const std::vector<std::vector<double> >& weights = prob->get_weights();
const problem::base::size_type Nv = prob->get_n_cities();
// Get out if there is nothing to do.
if (m_gen == 0) {
return;
}
// Initializing the random number generators
boost::uniform_real<double> uniform(0.0,1.0);
boost::variate_generator<boost::lagged_fibonacci607 &, boost::uniform_real<double> > unif_01(m_drng,uniform);
boost::uniform_int<int> NPless1(0, NP - 2);
boost::variate_generator<boost::mt19937 &, boost::uniform_int<int> > unif_NPless1(m_urng,NPless1);
boost::uniform_int<int> Nv_(0, Nv - 1);
boost::variate_generator<boost::mt19937 &, boost::uniform_int<int> > unif_Nv(m_urng,Nv_);
boost::uniform_int<int> Nvless1(0, Nv - 2);
boost::variate_generator<boost::mt19937 &, boost::uniform_int<int> > unif_Nvless1(m_urng,Nvless1);
//check if we have a symmetric problem (symmetric weight matrix)
bool is_sym = true;
for(size_t i = 0; i < Nv; i++)
{
for(size_t j = i+1; j < Nv; j++)
{
if(weights[i][j] != weights[j][i])
{
is_sym = false;
goto end_loop;
}
}
}
end_loop:
//create own local population
std::vector<decision_vector> my_pop(NP, decision_vector(Nv));
//check if some individuals in the population that is passed as a function input are feasible.
bool feasible;
std::vector<int> not_feasible;
for (size_t i = 0; i < NP; i++) {
feasible = prob->feasibility_x(pop.get_individual(i).cur_x);
if(feasible){ //if feasible store it in my_pop
switch( prob->get_encoding() ) {
case problem::tsp::FULL:
my_pop[i] = prob->full2cities(pop.get_individual(i).cur_x);
break;
case problem::tsp::RANDOMKEYS:
my_pop[i] = prob->randomkeys2cities(pop.get_individual(i).cur_x);
break;
case problem::tsp::CITIES:
my_pop[i] = pop.get_individual(i).cur_x;
break;
}
}
else
{
not_feasible.push_back(i);
}
}
//replace the not feasible individuals by feasible ones
int i;
switch (m_ini_type){
case 0:
{
//random initialization (produces feasible individuals)
for (size_t ii = 0; ii < not_feasible.size(); ii++) {
i = not_feasible[ii];
for (size_t j = 0; j < Nv; j++) {
my_pop[i][j] = j;
}
}
int tmp;
size_t rnd_idx;
for (size_t j = 1; j < Nv-1; j++) {
boost::uniform_int<int> dist_(j, Nv - 1);
boost::variate_generator<boost::mt19937 &, boost::uniform_int<int> > dist(m_urng,dist_);
for (size_t ii = 0; ii < not_feasible.size(); ii++) {
i = not_feasible[ii];
rnd_idx = dist();
tmp = my_pop[i][j];
my_pop[i][j] = my_pop[i][rnd_idx];
my_pop[i][rnd_idx] = tmp;
}
}
break;
}
case 1:
{
//initialize with nearest neighbor algorithm
int nxt_city;
size_t min_idx;
std::vector<int> not_visited(Nv);
for (size_t ii = 0; ii < not_feasible.size(); ii++) {
i = not_feasible[ii];
for (size_t j = 0; j < Nv; j++) {
not_visited[j] = j;
}
my_pop[i][0] = unif_Nv();
std::swap(not_visited[my_pop[i][0]],not_visited[Nv-1]);
for (size_t j = 1; j < Nv-1; j++) {
min_idx = 0;
nxt_city = not_visited[0];
for (size_t l = 1; l < Nv-j; l++) {
if(weights[my_pop[i][j-1]][not_visited[l]] < weights[my_pop[i][j-1]][nxt_city]){
min_idx = l;
nxt_city = not_visited[l];}
}
my_pop[i][j] = nxt_city;
std::swap(not_visited[min_idx],not_visited[Nv-j-1]);
}
my_pop[i][Nv-1] = not_visited[0];
}
break;
}
default:
pagmo_throw(value_error,"Invalid initialization type");
}
//compute fitness of individuals (necessary if weight matrix is not symmetric)
std::vector<double> fitness(NP, 0);
if(!is_sym){
for(size_t i=0; i < NP; i++){
fitness[i] = weights[my_pop[i][Nv-1]][my_pop[i][0]];
for(size_t k=1; k < Nv; k++){
fitness[i] += weights[my_pop[i][k-1]][my_pop[i][k]];
}
}
}
decision_vector tmp_tour(Nv);
bool stop;
size_t rnd_num, i2, pos1_c1, pos1_c2, pos2_c1, pos2_c2; //pos2_c1 denotes the position of city1 in parent2
double fitness_change, fitness_tmp = 0;
//InverOver main loop
for(int iter = 0; iter < m_gen; iter++){
for(size_t i1 = 0; i1 < NP; i1++){
fitness_change = 0;
tmp_tour = my_pop[i1];
pos1_c1 = unif_Nv();
stop = false;
while(!stop){
if(unif_01() < m_ri){
rnd_num = unif_Nvless1();
pos1_c2 = (rnd_num == pos1_c1? Nv-1:rnd_num);
}
else{
i2 = unif_NPless1();
i2 = (i2 == i1? NP-1:i2);
pos2_c1 = std::find(my_pop[i2].begin(),my_pop[i2].end(),tmp_tour[pos1_c1])-my_pop[i2].begin();
pos2_c2 = (pos2_c1 == Nv-1? 0:pos2_c1+1);
pos1_c2 = std::find(tmp_tour.begin(),tmp_tour.end(),my_pop[i2][pos2_c2])-tmp_tour.begin();
}
stop = (abs(pos1_c1-pos1_c2)==1 || abs(pos1_c1-pos1_c2)==Nv-1);
if(!stop){
if(pos1_c1<pos1_c2){
for(size_t l=0; l < (double (pos1_c2-pos1_c1-1)/2); l++){
std::swap(tmp_tour[pos1_c1+1+l],tmp_tour[pos1_c2-l]);}
if(is_sym){
fitness_change -= weights[tmp_tour[pos1_c1]][tmp_tour[pos1_c2]] + weights[tmp_tour[pos1_c1+1]][tmp_tour[pos1_c2+1 - (pos1_c2+1 > Nv-1? Nv:0)]];
fitness_change += weights[tmp_tour[pos1_c1]][tmp_tour[pos1_c1+1]] + weights[tmp_tour[pos1_c2]][tmp_tour[pos1_c2+1 - (pos1_c2+1 > Nv-1? Nv:0)]];
}
}
else{
//inverts the section from c1 to c2 (see documentation Note3)
for(size_t l=0; l < (double (Nv-(pos1_c1-pos1_c2)-1)/2); l++){
std::swap(tmp_tour[pos1_c1+1+l - (pos1_c1+1+l>Nv-1? Nv:0)],tmp_tour[pos1_c2-l + (pos1_c2<l? Nv:0)]);}
if(is_sym){
fitness_change -= weights[tmp_tour[pos1_c1]][tmp_tour[pos1_c2]] + weights[tmp_tour[pos1_c1+1 - (pos1_c1+1 > Nv-1? Nv:0)]][tmp_tour[pos1_c2+1]];
fitness_change += weights[tmp_tour[pos1_c1]][tmp_tour[pos1_c1+1 - (pos1_c1+1 > Nv-1? Nv:0)]] + weights[tmp_tour[pos1_c2]][tmp_tour[pos1_c2+1]];
}
}
pos1_c1 = pos1_c2; //better performance than original Inver-Over (shorter tour in less time)
}
} //end of while loop (looping over a single indvidual)
if(!is_sym){ //compute fitness of the temporary tour
fitness_tmp = weights[tmp_tour[Nv-1]][tmp_tour[0]];
for(size_t k=1; k < Nv; k++){
fitness_tmp += weights[tmp_tour[k-1]][tmp_tour[k]];
}
fitness_change = fitness_tmp - fitness[i1];
}
if(fitness_change < 0){ //replace individual?
my_pop[i1] = tmp_tour;
if(!is_sym){
fitness[i1] = fitness_tmp;
}
}
} //end of loop over population
} //end of loop over generations
//change representation of tour
for (size_t ii = 0; ii < NP; ii++) {
switch( prob->get_encoding() ) {
case problem::tsp::FULL:
pop.set_x(ii,prob->cities2full(my_pop[ii]));
break;
case problem::tsp::RANDOMKEYS:
pop.set_x(ii,prob->cities2randomkeys(my_pop[ii],pop.get_individual(ii).cur_x));
break;
case problem::tsp::CITIES:
pop.set_x(ii,my_pop[ii]);
break;
}
}
} // end of evolve
