GSP.h

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/*!
 * \brief       GSP benchmark function for multiobjective optimization
 * 
 * \author      O. Krause
 * \date        2015
 *
 *
 * \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_GSP_H
#define SHARK_OBJECTIVEFUNCTIONS_BENCHMARK_GSP_H

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

namespace shark {
/// \brief Real-valued benchmark function with two objectives.
struct GSP : public MultiObjectiveFunction
{
    GSP(std::size_t numVariables=5) : m_handler(SearchPointType(numVariables,0),SearchPointType(numVariables,10000))  {
        announceConstraintHandler(&m_handler);
    }

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

    std::size_t numberOfObjectives()const{
        return 2;
    }
    
    std::size_t numberOfVariables()const{
        return m_handler.dimensions();
    }
    
    bool hasScalableDimensionality()const{
        return true;
    }
    
    void setNumberOfVariables( std::size_t numberOfVariables ){
        m_handler.setBounds(
            SearchPointType(numberOfVariables,0),
            SearchPointType(numberOfVariables,10000)
        );
    }

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

        ResultType value( 2 );
        double alpha = 1. / ( 2. * m_gamma );

        double sum1 = 0., sum2 = 0.;

        for( std::size_t i = 0; i < x.size(); i++ ) {
            sum1 += sqr( x( i ) );
            sum2 += sqr( 1 - x( i ) );
        }

        double alphaN = 1. / ( std::pow( x.size(), alpha ) );

        value[0] = alphaN * std::pow( sum1, alpha );
        value[1] = alphaN * std::pow( sum2, alpha );

        return( value );
    }
private:
    BoxConstraintHandler<SearchPointType> m_handler;
    double m_gamma;
};

}
#endif