void Xmap::run() { prepare_forecast(); // check parameters // setup data structures and compute maximum lib size predicted_stats.clear(); predicted_lib_sizes.clear(); std::vector<size_t> full_lib = which_lib; size_t max_lib_size = full_lib.size(); std::mt19937 rng(seed); // init mersenne twister with seed // need to update with true random seed std::uniform_int_distribution<uint32_t> lib_sampler(0, max_lib_size-1); std::uniform_real_distribution<double> unif_01(0, 1); std::vector<int> idx; size_t m; size_t t; for(auto lib_size: lib_sizes) { if(lib_size >= max_lib_size && (!random_libs || !replace)) // no possible lib variation if using all vectors and // [no random libs OR (random_libs and sampling without replacement)] { which_lib = full_lib; // use all lib vectors forecast(); predicted_stats.push_back(make_stats()); predicted_lib_sizes.push_back(lib_size); break; } else if(random_libs) { which_lib.resize(lib_size, 0); for(size_t k = 0; k < num_samples; ++k) { if(replace) { for(auto& lib: which_lib) { lib = full_lib[lib_sampler(rng)]; } } else { // sample without replacement (algorithm from Knuth) m = 0; t = 0; while(m < lib_size) { if(double(max_lib_size - t) * unif_01(rng) >= double(lib_size - m)) { ++t; } else { which_lib[m] = full_lib[t]; ++t; ++m; } } } forecast(); predicted_stats.push_back(make_stats()); predicted_lib_sizes.push_back(lib_size); } } else // no random libs and using contiguous segments { for(size_t k = 0; k < max_lib_size; ++k) { if((k + lib_size) > max_lib_size) // need to loop around { which_lib.assign(full_lib.begin()+k, full_lib.end()); // k to end which_lib.insert(which_lib.begin(), full_lib.begin(), full_lib.begin() + lib_size - (max_lib_size-k)); } else { which_lib.assign(full_lib.begin()+k, full_lib.begin()+k+lib_size); } forecast(); predicted_stats.push_back(make_stats()); predicted_lib_sizes.push_back(lib_size); } } } which_lib = full_lib; return; }
/** * 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
/** * 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