algorithm/cstrs_self_adaptive.cpp

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 *   Copyright (C) 2004-2015 The PaGMO development team,                     *
 *   Advanced Concepts Team (ACT), European Space Agency (ESA)               *
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#include <boost/random/uniform_int.hpp>
#include <boost/random/uniform_real.hpp>
#include <string>
#include <vector>

#include "../exceptions.h"
#include "../population.h"
#include "../problem/base.h"
#include "../problem/cstrs_self_adaptive.h"
#include "../types.h"
#include "base.h"
#include "cstrs_self_adaptive.h"

namespace pagmo { namespace algorithm {


cstrs_self_adaptive::cstrs_self_adaptive(const base &original_algo, int gen, double ftol, double xtol):base(),
        m_original_algo(original_algo.clone()),m_gen(gen),m_ftol(ftol),m_xtol(xtol)
{
        if(gen < 0) {
                pagmo_throw(value_error,"number of generations must be nonnegative");
        }
}

cstrs_self_adaptive::cstrs_self_adaptive(const cstrs_self_adaptive &algo):base(algo),m_original_algo(algo.m_original_algo->clone()),m_gen(algo.m_gen),
        m_ftol(algo.m_ftol),m_xtol(algo.m_xtol) {}

base_ptr cstrs_self_adaptive::clone() const
{
    return base_ptr(new cstrs_self_adaptive(*this));
}


void cstrs_self_adaptive::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 Self-Adaptive
        if(prob_c_dimension < 1) {
                pagmo_throw(value_error,"The problem is not constrained and Self-Adaptive is not suitable to solve it");
        }

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

        // Create the new problem;
    problem::cstrs_self_adaptive prob_new(prob,pop);

        // Main Self-Adaptive loop
        for(int k=0; k<m_gen; k++) {
                //std::cout << "current generation: " << k << std::endl;

                // at the first iteration the problem is not changed, 
                // for k>0 the problem is gonna change, the cache need to be reset and the population cleared
                if(k>0){ 
                        prob_new.reset_caches();
                        prob_new.update_penalty_coeff(pop);
                }

                // if the problem has changed it needs to be reassigned to the population
                population pop_new(prob_new,0);

                for(population::size_type i=0; i<pop_size; i++) {
                        // Evaluate according to the new fitness;
                        pop_new.push_back(pop.get_individual(i).cur_x);
                }

                // this constraints handling technique is initially intended to be
                // used as a fitness evaluator for ES, I am not convinced this is the easiest
                // way to implement it. But for DE, PSO in example, there is no fitness evaluator?
                m_original_algo->evolve(pop_new);

                // Reinsert best individual from the previous generation if not already present
                // Uses the constrained problem. If we don't do that, we loose the best individual...
                // Should we impose a certain percentage of the best ones?
                bool exists = false;
                for(population::size_type i=0; i<pop_size; i++) {
                        if(pop_new.get_individual(i).cur_x == pop.champion().x) {
                                exists=true;break;
                        }
                }
                if(!exists){
                        int worst=0;
                        for (pagmo::population::size_type i = 1; i<pop_new.size();i++) {
                                if ( prob.compare_x(pop_new.get_individual(worst).cur_x,pop_new.get_individual(i).cur_x) ) worst=i;
                        }

                        decision_vector dummy = pop.champion().x;
                        std::transform(dummy.begin(), dummy.end(), pop.get_individual(worst).cur_x.begin(), dummy.begin(),std::minus<double>());
                        //updates x and v (cache avoids to recompute the objective function)
                        pop_new.set_x(worst,pop.champion().x);
                        pop_new.set_v(worst,dummy);
                }

                // update the population pop
                pop.clear();
                for(pagmo::population::size_type i=0; i<pop_new.size(); i++) {
                        pop.push_back(pop_new.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;
                        }
                }

        }
}

std::string cstrs_self_adaptive::get_name() const
{
        return m_original_algo->get_name() + "[Self-Adaptive]";
}


base_ptr cstrs_self_adaptive::get_algorithm() const
{
        return m_original_algo->clone();
}


void cstrs_self_adaptive::set_algorithm(const base &algo)
{
        m_original_algo = algo.clone();
}


std::string cstrs_self_adaptive::human_readable_extra() const
{
        std::ostringstream s;
        s << "algorithm: " << m_original_algo->get_name() << ' ';
        s << "\n\tConstraints handled with Self-Adaptive algorithm";
        return s.str();
}

}} //namespaces

BOOST_CLASS_EXPORT_IMPLEMENT(pagmo::algorithm::cstrs_self_adaptive)