Beispiel #1
0
void ms::evolve(population &pop) const
{
	// Let's store some useful variables.
	const population::size_type NP = pop.size();

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

	// Local population used in the algorithm iterations.
	population working_pop(pop);

	//ms main loop
	for (int i=0; i< m_starts; ++i)
	{
		working_pop.reinit();
		m_algorithm->evolve(working_pop);
		if (working_pop.problem().compare_fc(working_pop.get_individual(working_pop.get_best_idx()).cur_f,working_pop.get_individual(working_pop.get_best_idx()).cur_c,
			pop.get_individual(pop.get_worst_idx()).cur_f,pop.get_individual(pop.get_worst_idx()).cur_c
		) )
		{
			//update best population replacing its worst individual with the good one just produced.
			pop.set_x(pop.get_worst_idx(),working_pop.get_individual(working_pop.get_best_idx()).cur_x);
			pop.set_v(pop.get_worst_idx(),working_pop.get_individual(working_pop.get_best_idx()).cur_v);
		}
		if (m_screen_output)
		{
			std::cout << i << ". " << "\tCurrent iteration best: " << working_pop.get_individual(working_pop.get_best_idx()).cur_f << "\tOverall champion: " << pop.champion().f << std::endl;
		}
	}
}
Beispiel #2
0
/// Evolve method.
void monte_carlo::evolve(population &pop) const
{
	// Let's store some useful variables.
	const problem::base &prob = pop.problem();
	const problem::base::size_type prob_dimension = prob.get_dimension(), prob_i_dimension = prob.get_i_dimension();
	const decision_vector &lb = prob.get_lb(), &ub = prob.get_ub();
	const population::size_type pop_size = pop.size();
	// Get out if there is nothing to do.
	if (pop_size == 0 || m_max_eval == 0) {
		return;
	}
	// Initialise temporary decision vector, fitness vector and decision vector.
	decision_vector tmp_x(prob_dimension);
	fitness_vector tmp_f(prob.get_f_dimension());
	constraint_vector tmp_c(prob.get_c_dimension());
	// Main loop.
	for (std::size_t i = 0; i < m_max_eval; ++i) {
		// Generate a random decision vector.
		for (problem::base::size_type j = 0; j < prob_dimension - prob_i_dimension; ++j) {
			tmp_x[j] = boost::uniform_real<double>(lb[j],ub[j])(m_drng);
		}
		for (problem::base::size_type j = prob_dimension - prob_i_dimension; j < prob_dimension; ++j) {
			tmp_x[j] = boost::uniform_int<int>(lb[j],ub[j])(m_urng);
		}
		// Compute fitness and constraints.
		prob.objfun(tmp_f,tmp_x);
		prob.compute_constraints(tmp_c,tmp_x);
		// Locate the worst individual.
		const population::size_type worst_idx = pop.get_worst_idx();
		if (prob.compare_fc(tmp_f,tmp_c,pop.get_individual(worst_idx).cur_f,pop.get_individual(worst_idx).cur_c)) {
			pop.set_x(worst_idx,tmp_x);
		}
	}
}
Beispiel #3
0
void ihs::evolve(population &pop) const
{
	// Let's store some useful variables.
	const problem::base &prob = pop.problem();
	const problem::base::size_type prob_dimension = prob.get_dimension(), prob_i_dimension = prob.get_i_dimension();
	const decision_vector &lb = prob.get_lb(), &ub = prob.get_ub();
	const population::size_type pop_size = pop.size();
	// Get out if there is nothing to do.
	if (pop_size == 0 || m_gen == 0) {
		return;
	}
	decision_vector lu_diff(prob_dimension);
	for (problem::base::size_type i = 0; i < prob_dimension; ++i) {
		lu_diff[i] = ub[i] - lb[i];
	}
	// Int distribution to be used when picking random individuals.
	boost::uniform_int<population::size_type> uni_int(0,pop_size - 1);
	const double c = std::log(m_bw_min/m_bw_max) / m_gen;
	// Temporary individual used during evolution.
	population::individual_type tmp;
	tmp.cur_x.resize(prob_dimension);
	tmp.cur_f.resize(prob.get_f_dimension());
	tmp.cur_c.resize(prob.get_c_dimension());
	for (std::size_t g = 0; g < m_gen; ++g) {
		const double ppar_cur = m_ppar_min + ((m_ppar_max - m_ppar_min) * g) / m_gen, bw_cur = m_bw_max * std::exp(c * g);
		// Continuous part.
		for (problem::base::size_type i = 0; i < prob_dimension - prob_i_dimension; ++i) {
			if (m_drng() < m_phmcr) {
				// tmp's i-th chromosome element is the one from a randomly chosen individual.
				tmp.cur_x[i] = pop.get_individual(uni_int(m_urng)).cur_x[i];
				// Do pitch adjustment with ppar_cur probability.
				if (m_drng() < ppar_cur) {
					// Randomly, add or subtract pitch from the current chromosome element.
					if (m_drng() > .5) {
						tmp.cur_x[i] += m_drng() * bw_cur * lu_diff[i];
					} else {
						tmp.cur_x[i] -= m_drng() * bw_cur * lu_diff[i];
					}
					// Handle the case in which we added or subtracted too much and ended up out
					// of boundaries.
					if (tmp.cur_x[i] > ub[i]) {
						tmp.cur_x[i] = boost::uniform_real<double>(lb[i],ub[i])(m_drng);
					} else if (tmp.cur_x[i] < lb[i]) {
						tmp.cur_x[i] = boost::uniform_real<double>(lb[i],ub[i])(m_drng);
					}
				}
			} else {
				// Pick randomly within the bounds.
				tmp.cur_x[i] = boost::uniform_real<double>(lb[i],ub[i])(m_drng);
			}
		}

		//Integer Part
		for (problem::base::size_type i = prob_dimension - prob_i_dimension; i < prob_dimension; ++i) {
			if (m_drng() < m_phmcr) {
				tmp.cur_x[i] = pop.get_individual(uni_int(m_urng)).cur_x[i];
				if (m_drng() < ppar_cur) {
					if (m_drng() > .5) {
						tmp.cur_x[i] += double_to_int::convert(m_drng() * bw_cur * lu_diff[i]);
					} else {
						tmp.cur_x[i] -= double_to_int::convert(m_drng() * bw_cur * lu_diff[i]);
					}
					// Wrap over in case we went past the bounds.
					if (tmp.cur_x[i] > ub[i]) {
						tmp.cur_x[i] = lb[i] + double_to_int::convert(tmp.cur_x[i] - ub[i]) % static_cast<int>(lu_diff[i]);
					} else if (tmp.cur_x[i] < lb[i]) {
						tmp.cur_x[i] = ub[i] - double_to_int::convert(lb[i] - tmp.cur_x[i]) % static_cast<int>(lu_diff[i]);
					}
				}
			} else {
				// Pick randomly within the bounds.
				tmp.cur_x[i] = boost::uniform_int<int>(lb[i],ub[i])(m_urng);
			}
		}
		// And we push him back
		pop.push_back(tmp.cur_x);
		// We locate the worst individual.
		const population::size_type worst_idx = pop.get_worst_idx();
		// And we get rid of him :)
		pop.erase(worst_idx);
	}
}
Beispiel #4
0
/**
 * 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]);

			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: " << pop.champion().f << std::endl;
				std::cout << "    xtol: " << dx << ", ftol: " << mah << std::endl;
				std::cout << "    xtol: " << dx << ", ftol: " << mah << std::endl;
			}
		}
	}
}