luksan_vlcek_2.cpp

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#include <boost/integer_traits.hpp>
#include <boost/numeric/conversion/cast.hpp>
#include <stdexcept>
#include <string>
#include <vector>

#include "../exceptions.h"
#include "../types.h"
#include "base.h"
#include "luksan_vlcek_2.h"

static int __check__(int N){
        if (N - 5 >= boost::integer_traits<int>::const_max / 2) {
                pagmo_throw(std::overflow_error,"overflow error");
        }
        if (N<=15 || N % 2)
        {
                pagmo_throw(value_error,"problem dimension needs to be at least 16 and even");
        }
        return N;
}

namespace pagmo { namespace problem {


luksan_vlcek_2::luksan_vlcek_2(int N, const double &clb, const double &cub):base(__check__(N), 0, 1, 2 * (__check__(N) - 9), 2 * (__check__(N) - 9), 1e-14)
{

        if (clb > cub)
        {
                pagmo_throw(value_error,"constraints lower bound is higher than the upper bound");
        }
        set_lb(-5);
        set_ub(5);
        m_clb = decision_vector(boost::numeric_cast<decision_vector::size_type>(2*(N-9)),clb);
        m_cub = decision_vector(boost::numeric_cast<decision_vector::size_type>(2*(N-9)),cub);
}

base_ptr luksan_vlcek_2::clone() const
{
        return base_ptr(new luksan_vlcek_2(*this));
}

void luksan_vlcek_2::objfun_impl(fitness_vector &f, const decision_vector &x) const
{
        f[0] = 0.;
        for (decision_vector::size_type i=0; i < (x.size()-2)/2; i++)
        {
                double a1 = x[2*i]*x[2*i] - x[2*i+1];
                double a2 = x[2*i] - 1.;
                double a3 = x[2*i+2]*x[2*i+2] - x[2*i+3];
                double a4 = x[2*i+2] - 1.;
                double a5 = x[2*i+1] + x[2*i+3] - 2.;
                double a6 = x[2*i+1] - x[2*i+3];
                f[0] += 100.*a1*a1 + a2*a2 + 90.*a3*a3 + a4*a4 + 10.*a5*a5 + .1*a6*a6;
        }
}

void luksan_vlcek_2::compute_constraints_impl(constraint_vector &c, const decision_vector &x) const
{
        for (decision_vector::size_type i=0; i < x.size()-9; i++)
        {
                c[2*i] = (2.+5.*x[i+5]*x[i+5])*x[i+5] + 1.;
                for (decision_vector::size_type k = (i <= 5) ? 0 : i - 5; k<=i+1; k++) {
                        c[2*i] += x[k]*(x[k]+1.);
                }
                c[2*i] = c[2*i] - m_cub[i];
                c[2*i+1] = (2.+5.*x[i+5]*x[i+5])*x[i+5] + 1.;
                for (decision_vector::size_type k = (i <= 5) ? 0 : i - 5; k<=i+1; k++) {
                        c[2*i+1] += x[k]*(x[k]+1.);
                }
                c[2*i+1] = m_clb[i] - c[2*i+1];
        }
}

void luksan_vlcek_2::set_sparsity(int& lenG, std::vector<int>& iGfun, std::vector<int>& jGvar) const
{
        //Initial point
        decision_vector x0(get_dimension(),1);
        //Numerical procedure
        estimate_sparsity(x0, lenG, iGfun, jGvar);
}

std::string luksan_vlcek_2::get_name() const
{
        return "Luksan-Vlcek 2";
}

}}

BOOST_CLASS_EXPORT_IMPLEMENT(pagmo::problem::luksan_vlcek_2)