DTLZ5.h

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//===========================================================================
/*!
 * 
 *
 * \brief       Objective function DTLZ5
 * 
 * 
 *
 * \author      T.Voss, T. Glasmachers, O.Krause
 * \date        2010-2011
 *
 *
 * \par Copyright 1995-2017 Shark Development Team
 * 
 * <BR><HR>
 * This file is part of Shark.
 * <http://shark-ml.org/>
 * 
 * Shark is free software: you can redistribute it and/or modify
 * it under the terms of the GNU Lesser General Public License as published 
 * by the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 * 
 * Shark is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU Lesser General Public License for more details.
 * 
 * You should have received a copy of the GNU Lesser General Public License
 * along with Shark.  If not, see <http://www.gnu.org/licenses/>.
 *
 */
//===========================================================================
#ifndef SHARK_OBJECTIVEFUNCTIONS_BENCHMARK_DTLZ5_H
#define SHARK_OBJECTIVEFUNCTIONS_BENCHMARK_DTLZ5_H

#include <shark/ObjectiveFunctions/AbstractObjectiveFunction.h>
#include <shark/ObjectiveFunctions/BoxConstraintHandler.h>

namespace shark {
/**
* \brief Implements the benchmark function DTLZ5.
*
* See: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.18.7531&rep=rep1&type=pdf
* The benchmark function exposes the following features:
*   - Scalable w.r.t. the searchspace and w.r.t. the objective space.
*   - Highly multi-modal.
*/
struct DTLZ5 : public MultiObjectiveFunction
{
    DTLZ5(std::size_t numVariables = 0) : m_objectives(2), m_handler(SearchPointType(numVariables,0),SearchPointType(numVariables,1) ){
        announceConstraintHandler(&m_handler);
    }

    /// \brief From INameable: return the class name.
    std::string name() const
    { return "DTLZ5"; }

    std::size_t numberOfObjectives()const{
        return m_objectives;
    }
    bool hasScalableObjectives()const{
        return true;
    }
    void setNumberOfObjectives( std::size_t numberOfObjectives ){
        m_objectives = numberOfObjectives;
    }
    
    std::size_t numberOfVariables()const{
        return m_handler.dimensions();
    }
    
    bool hasScalableDimensionality()const{
        return true;
    }

    /// \brief Adjusts the number of variables if the function is scalable.
    /// \param [in] numberOfVariables The new dimension.
    void setNumberOfVariables( std::size_t numberOfVariables ){
        m_handler.setBounds(
            SearchPointType(numberOfVariables,0),
            SearchPointType(numberOfVariables,1)
        );
    }

    ResultType eval( const SearchPointType & x ) const {
        m_evaluationCounter++;

        ResultType value( numberOfObjectives() );

        std::vector<double> phi(numberOfObjectives());

        std::size_t k = numberOfVariables() - numberOfObjectives() + 1 ;
        double g = 0.0 ;

        for (std::size_t i = numberOfVariables() - k + 1; i <= numberOfVariables(); i++)
            g += sqr( x(i-1) - 0.5 );

        double t = M_PI  / (4 * (1 + g));

        phi[0] = x( 0 ) * M_PI / 2;
        for (std::size_t i = 2; i <= (numberOfObjectives() - 1); i++)
            phi[i-1] = t * (1 + 2 * g * x( i-1 ) );

        for (std::size_t i = 1; i <= numberOfObjectives(); i++) {
            double f = (1 + g);

            for (std::size_t j = numberOfObjectives() - i; j >= 1; j--)
                f *= std::cos(phi[j-1]);

            if (i > 1)
                f *= std::sin(phi[( numberOfObjectives() - i + 1 ) - 1]);

            value[i-1] = f ;
        }

        return value;
    }

private:
    std::size_t m_objectives;
    BoxConstraintHandler<SearchPointType> m_handler;
};

}
#endif