normalized.cpp

/*****************************************************************************
 *   Copyright (C) 2004-2015 The PaGMO development team,                     *
 *   Advanced Concepts Team (ACT), European Space Agency (ESA)               *
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 *   https://github.com/esa/pagmo                                            *
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 *   act@esa.int                                                             *
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#include <cmath>

#include "../exceptions.h"
#include "../types.h"
#include "../population.h"
#include "base.h"
#include "normalized.h"

namespace pagmo { namespace problem {

normalized::normalized(const base & p):
        base_meta(
                 p,
                 p.get_dimension(),
                 p.get_i_dimension(),
                 p.get_f_dimension(),
                 p.get_c_dimension(),
                 p.get_ic_dimension(),
                 p.get_c_tol()),
                 m_normalization_center(p.get_dimension(),0),
                 m_normalization_scale(p.get_dimension(),0)
{
        configure_new_bounds();
}

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

void normalized::configure_new_bounds()
{
        for(base::size_type i = 0; i < get_lb().size(); ++i){
                double center = (m_original_problem->get_ub()[i] + m_original_problem->get_lb()[i]) / 2;
                double spread = m_original_problem->get_ub()[i] - m_original_problem->get_lb()[i];
                m_normalization_center[i] = center;  // Center to origin
                m_normalization_scale[i] = spread/2; // Scale to [-1, 1] centered at origin
        }
        set_bounds(-1, 1);
}

decision_vector normalized::denormalize(const decision_vector& x) const
{
        decision_vector retval(x.size(), 0);
        for(base::size_type i = 0; i < x.size(); ++i){
                retval[i] = (x[i] * m_normalization_scale[i]) + m_normalization_center[i];
        }
        return retval;
}

void normalized::objfun_impl(fitness_vector &f, const decision_vector &x) const
{
        decision_vector x_original = denormalize(x);
        m_original_problem->objfun(f, x_original);
}

void normalized::compute_constraints_impl(constraint_vector &c, const decision_vector &x) const
{
        decision_vector x_original = denormalize(x);
        m_original_problem->compute_constraints(c, x_original);
}


std::string normalized::get_name() const
{
        return m_original_problem->get_name() + " [Normalized]"; 
}

std::string normalized::human_readable_extra() const
{
        std::ostringstream oss;
        oss << m_original_problem->human_readable_extra() << std::endl;
        return oss.str();
}
}}

BOOST_CLASS_EXPORT_IMPLEMENT(pagmo::problem::normalized)