//===========================================================================
/*!
*
*
* \brief Multi-objective optimization benchmark function LZ6.
*
* The function is described in
*
* H. Li and Q. Zhang.
* Multiobjective Optimization Problems with Complicated Pareto Sets, MOEA/D and NSGA-II,
* IEEE Trans on Evolutionary Computation, 2(12):284-302, April 2009.
*
*
*
* \author -
* \date -
*
*
* \par Copyright 1995-2017 Shark Development Team
*
*
* This file is part of Shark.
*
*
* 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 .
*
*/
//===========================================================================
#ifndef SHARK_OBJECTIVEFUNCTIONS_BENCHMARK_LZ6_H
#define SHARK_OBJECTIVEFUNCTIONS_BENCHMARK_LZ6_H
#include
#include
namespace shark {
/*! \brief Multi-objective optimization benchmark function LZ6.
*
* The function is described in
*
* H. Li and Q. Zhang.
* Multiobjective Optimization Problems with Complicated Pareto Sets, MOEA/D and NSGA-II,
* IEEE Trans on Evolutionary Computation, 2(12):284-302, April 2009.
*/
struct LZ6 : public MultiObjectiveFunction
{
LZ6(std::size_t numVariables = 0) : m_handler(SearchPointType(numVariables,-2),SearchPointType(numVariables,2) ){
announceConstraintHandler(&m_handler);
}
/// \brief From INameable: return the class name.
std::string name() const
{ return "LZ6"; }
std::size_t numberOfObjectives()const{
return 2;
}
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 ){
SearchPointType lb(numberOfVariables,-2);
SearchPointType ub(numberOfVariables, 2);
lb(0) = 0;
ub(0) = 1;
m_handler.setBounds(lb, ub);
}
ResultType eval( const SearchPointType & x ) const {
m_evaluationCounter++;
ResultType value( 3, 0 );
std::size_t counter1 = 0, counter2 = 0, counter3 = 0;
for( std::size_t i = 3; i <= x.size(); i++ ) {
if( (i-1) % 3 == 0 ) { //J1
counter1++;
value[0] += sqr(x(i-1)-2*x( 1 )*::sin( 2 * M_PI * x( 0 ) + i*M_PI/x.size() ) );
} else if( (i-2) % 3 == 0 ) { //J2
counter2++;
value[1] += sqr(x(i-1)-2*x( 1 )*::sin( 2 * M_PI * x( 0 ) + i*M_PI/x.size() ) );
} else if( i % 3 == 0 ) {
counter3++;
value[2] += sqr(x(i-1)-2*x( 1 )*::sin( 2 * M_PI * x( 0 ) + i*M_PI/x.size() ) );
}
}
value[0] *= counter1 > 0 ? 2./counter1 : 1;
value[1] *= counter2 > 0 ? 2./counter2 : 1;
value[2] *= counter3 > 0 ? 2./counter3 : 1;
value[0] += ::cos( 0.5*M_PI * x( 0 ) ) * ::cos( 0.5*M_PI * x( 1 ) );
value[1] += ::cos( 0.5*M_PI * x( 0 ) ) * ::sin( 0.5*M_PI * x( 1 ) );
value[2] += ::sin( 0.5*M_PI * x( 0 ) );
return( value );
}
private:
BoxConstraintHandler m_handler;
};
}
#endif