luksan_vlcek_1.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/integer_traits.hpp>
#include <boost/numeric/conversion/cast.hpp>
#include <cmath>
#include <stdexcept>
#include <string>
#include <vector>

#include "../exceptions.h"
#include "../types.h"
#include "base.h"
#include "luksan_vlcek_1.h"

static int __check__(int N){
        if (N - 2 >= boost::integer_traits<int>::const_max / 2) {
                pagmo_throw(std::overflow_error,"overflow error");
        }
        if (N <= 2)
        {
                pagmo_throw(value_error,"problem dimension needs to be at least 3");
        }
        return N;
}

namespace pagmo { namespace problem {

luksan_vlcek_1::luksan_vlcek_1(int N, const double &clb, const double &cub):base(__check__(N), 0, 1, 2 * (__check__(N) - 2), 2 * (__check__(N) - 2), 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>(N-2),clb);
        m_cub = decision_vector(boost::numeric_cast<decision_vector::size_type>(N-2),cub);
}

base_ptr luksan_vlcek_1::clone() const
{
        return base_ptr(new luksan_vlcek_1(*this));
}

void luksan_vlcek_1::objfun_impl(fitness_vector &f, const decision_vector &x) const
{
        f[0] = 0.;
        for (pagmo::decision_vector::size_type i=0; i<x.size()-1; i++)
        {
                double a1 = x[i]*x[i]-x[i+1];
                double a2 = x[i] - 1.;
                f[0] += 100.*a1*a1 + a2*a2;
        }
}

void luksan_vlcek_1::compute_constraints_impl(constraint_vector &c, const decision_vector &x) const
{
        for (pagmo::decision_vector::size_type i=0; i<x.size()-2; i++)
        {
                c[2 * i] =  (3.*std::pow(x[i+1],3.) + 2.*x[i+2] - 5.
                + std::sin(x[i+1]-x[i+2])*std::sin(x[i+1]+x[i+2]) + 4.*x[i+1]
                - x[i]*std::exp(x[i]-x[i+1]) - 3.) - m_cub[i];
                c[2 * i + 1] = - (3.*std::pow(x[i+1],3.) + 2.*x[i+2] - 5.
                + std::sin(x[i+1]-x[i+2])*std::sin(x[i+1]+x[i+2]) + 4.*x[i+1]
                - x[i]*std::exp(x[i]-x[i+1]) - 3.) + m_clb[i];
        }
}

void luksan_vlcek_1::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_1::get_name() const
{
        return "Luksan-Vlcek 1";
}

} }

BOOST_CLASS_EXPORT_IMPLEMENT(pagmo::problem::luksan_vlcek_1)