C++ population::repair方法代码示例

/**
 * Run the CORE algorithm
 *
 * @param[in,out] pop input/output pagmo::population to be evolved.
 */
void cstrs_core::evolve(population &pop) const
{	
	// store 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 CORE
	if(prob_c_dimension < 1) {
		pagmo_throw(value_error,"The problem is not constrained and CORE is not suitable to solve it");
	}
	if(prob.get_f_dimension() != 1) {
		pagmo_throw(value_error,"The problem is multiobjective and CORE is not suitable to solve it");
	}

	// Get out if there is nothing to do.
	if(pop_size == 0) {
		return;
	}

	// generates the unconstrained problem
	problem::con2uncon prob_unconstrained(prob);

	// associates the population to this problem
	population pop_uncon(prob_unconstrained);

	// fill this unconstrained population
	pop_uncon.clear();
	for(population::size_type i=0; i<pop_size; i++) {
		pop_uncon.push_back(pop.get_individual(i).cur_x);
	}

	// vector containing the infeasibles positions
	std::vector<population::size_type> pop_infeasibles;

	// Main CORE loop
	for(int k=0; k<m_gen; k++) {

		if(k%m_repair_frequency == 0) {
			pop_infeasibles.clear();

			// get the infeasible individuals
			for(population::size_type i=0; i<pop_size; i++) {
				if(!prob.feasibility_c(pop.get_individual(i).cur_c)) {
					pop_infeasibles.push_back(i);
				}
			}

			// random shuffle of infeasibles?
			population::size_type number_of_repair = (population::size_type)(m_repair_ratio * pop_infeasibles.size());

			// repair the infeasible individuals
			for(population::size_type i=0; i<number_of_repair; i++) {
				const population::size_type &current_individual_idx = pop_infeasibles.at(i);

                pop.repair(current_individual_idx, m_repair_algo);
			}

			// the population is repaired, it can be now used in the new unconstrained population
			// only the repaired individuals are put back in the population
			for(population::size_type i=0; i<number_of_repair; i++) {
				population::size_type current_individual_idx = pop_infeasibles.at(i);
				pop_uncon.set_x(current_individual_idx, pop.get_individual(current_individual_idx).cur_x);
			}
		}

		m_original_algo->evolve(pop_uncon);

		// push back the population in the main problem
		pop.clear();
		for(population::size_type i=0; i<pop_size; i++) {
			pop.push_back(pop_uncon.get_individual(i).cur_x);
		}

		// Check the exit conditions (every 40 generations, just as DE)
		if(k % 40 == 0) {
			decision_vector tmp(prob_dimension);

			double dx = 0;
			for(decision_vector::size_type i=0; i<prob_dimension; i++) {
				tmp[i] = pop.get_individual(pop.get_worst_idx()).best_x[i] - pop.get_individual(pop.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(pop.get_individual(pop.get_worst_idx()).best_f[0] - pop.get_individual(pop.get_best_idx()).best_f[0]);
//.........这里部分代码省略.........