//=========================================================================== /*! * * * \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