286 lines
11 KiB
C++
286 lines
11 KiB
C++
/*
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* <moeoHyperVolumeDifferenceMetric.h>
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* Copyright (C) DOLPHIN Project-Team, INRIA Futurs, 2006-2007
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* (C) OPAC Team, LIFL, 2002-2007
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*
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* Jeremie Humeau
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* Arnaud Liefooghe
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*
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* This software is governed by the CeCILL license under French law and
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* abiding by the rules of distribution of free software. You can use,
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* modify and/ or redistribute the software under the terms of the CeCILL
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* license as circulated by CEA, CNRS and INRIA at the following URL
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* "http://www.cecill.info".
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*
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* As a counterpart to the access to the source code and rights to copy,
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* modify and redistribute granted by the license, users are provided only
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* with a limited warranty and the software's author, the holder of the
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* economic rights, and the successive licensors have only limited liability.
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*
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* In this respect, the user's attention is drawn to the risks associated
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* with loading, using, modifying and/or developing or reproducing the
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* software by the user in light of its specific status of free software,
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* that may mean that it is complicated to manipulate, and that also
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* therefore means that it is reserved for developers and experienced
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* professionals having in-depth computer knowledge. Users are therefore
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* encouraged to load and test the software's suitability as regards their
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* requirements in conditions enabling the security of their systems and/or
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* data to be ensured and, more generally, to use and operate it in the
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* same conditions as regards security.
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* The fact that you are presently reading this means that you have had
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* knowledge of the CeCILL license and that you accept its terms.
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*
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* ParadisEO WebSite : http://paradiseo.gforge.inria.fr
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* Contact: paradiseo-help@lists.gforge.inria.fr
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*
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*/
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//-----------------------------------------------------------------------------
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#ifndef MOEOHYPERVOLUMEDIFFERENCEMETRIC_H_
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#define MOEOHYPERVOLUMEDIFFERENCEMETRIC_H_
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#include <metric/moeoMetric.h>
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#include <metric/moeoHyperVolumeMetric.h>
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/**
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* The contribution metric evaluates the proportion of non-dominated solutions given by a Pareto set relatively to another Pareto set
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* (Meunier, Talbi, Reininger: 'A multiobjective genetic algorithm for radio network optimization', in Proc. of the 2000 Congress on Evolutionary Computation, IEEE Press, pp. 317-324)
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*/
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template < class ObjectiveVector >
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class moeoHyperVolumeDifferenceMetric : public moeoVectorVsVectorBinaryMetric < ObjectiveVector, double >
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{
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public:
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/**
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* Constructor with a coefficient (rho)
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* @param _normalize allow to normalize data (default true)
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* @param _rho coefficient to determine the reference point.
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*/
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moeoHyperVolumeDifferenceMetric(bool _normalize=true, double _rho=1.1): normalize(_normalize), rho(_rho), ref_point(/*NULL*/){
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bounds.resize(ObjectiveVector::Traits::nObjectives());
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// initialize bounds in case someone does not want to use them
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for (unsigned int i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
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{
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bounds[i] = eoRealInterval(0,1);
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}
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}
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/**
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* Constructor with a reference point
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* @param _normalize allow to normalize data (default true)
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* @param _ref_point the reference point
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*/
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moeoHyperVolumeDifferenceMetric(bool _normalize/*=true*/, ObjectiveVector& _ref_point/*=NULL*/): normalize(_normalize), rho(0.0), ref_point(_ref_point){
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bounds.resize(ObjectiveVector::Traits::nObjectives());
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// initialize bounds in case someone does not want to use them
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for (unsigned int i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
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{
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bounds[i] = eoRealInterval(0,1);
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}
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}
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/**
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* calculates and returns the HyperVolume value of a pareto front
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* @param _set1 the vector contains all objective Vector of the first pareto front
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* @param _set2 the vector contains all objective Vector of the second pareto front
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*/
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virtual double operator()(const std::vector < ObjectiveVector > & _set1, const std::vector < ObjectiveVector > & _set2)
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{
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double hypervolume_set1;
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double hypervolume_set2;
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if(rho >= 1.0){
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//determine bounds
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setup(_set1, _set2);
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//determine reference point
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for (unsigned int i=0; i<ObjectiveVector::Traits::nObjectives(); i++){
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if(normalize){
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if (ObjectiveVector::Traits::minimizing(i))
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ref_point[i]= rho;
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else
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ref_point[i]= 1-rho;
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}
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else{
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if (ObjectiveVector::Traits::minimizing(i))
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ref_point[i]= bounds[i].maximum() * rho;
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else
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ref_point[i]= bounds[i].maximum() * (1-rho);
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}
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}
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//if no normalization, reinit bounds to O..1 for
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if(!normalize)
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for (unsigned int i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
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bounds[i] = eoRealInterval(0,1);
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}
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else if(normalize)
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setup(_set1, _set2);
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moeoHyperVolumeMetric <ObjectiveVector> unaryMetric(ref_point, bounds);
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hypervolume_set1 = unaryMetric(_set1);
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hypervolume_set2 = unaryMetric(_set2);
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return hypervolume_set1 - hypervolume_set2;
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}
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/**
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* getter on bounds
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* @return bounds
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*/
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std::vector < eoRealInterval > getBounds(){
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return bounds;
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}
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/**
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* method calculate bounds for the normalization
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* @param _set1 the vector contains all objective Vector of the first pareto front
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* @param _set2 the vector contains all objective Vector of the second pareto front
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*/
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void setup(const std::vector < ObjectiveVector > & _set1, const std::vector < ObjectiveVector > & _set2)
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{
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if(_set1.size() < 1 || _set2.size() < 1) {
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throw("Error in moeoHyperVolumeUnaryMetric::setup -> argument1: vector<ObjectiveVector> size must be greater than 0");
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} else {
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#ifndef NDEBUG
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if( _set1.size() == 1 || _set2.size() == 1 ) {
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eo::log << eo::warnings << "Warning in moeoHyperVolumeUnaryMetric::setup one of the pareto set contains only one point (set1.size="
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<< _set1.size() << ", set2.size=" << _set2.size() << ")"
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<< std::endl;
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}
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#endif
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typename ObjectiveVector::Type min, max;
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unsigned int nbObj=ObjectiveVector::Traits::nObjectives();
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bounds.resize(nbObj);
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for (unsigned int i=0; i<nbObj; i++){
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min = _set1[0][i];
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max = _set1[0][i];
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for (unsigned int j=1; j<_set1.size(); j++){
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min = std::min(min, _set1[j][i]);
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max = std::max(max, _set1[j][i]);
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}
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for (unsigned int j=0; j<_set2.size(); j++){
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min = std::min(min, _set2[j][i]);
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max = std::max(max, _set2[j][i]);
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}
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if( min == max ) {
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bounds[i] = eoRealInterval(min-tiny(), max+tiny());
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} else {
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bounds[i] = eoRealInterval(min, max);
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}
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}
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}
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}
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protected:
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/**
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* Returns a very small value that can be used to avoid extreme cases (where the min bound == the max bound)
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*/
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static double tiny()
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{
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return 1e-6;
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}
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protected:
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/*boolean indicates if data must be normalized or not*/
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bool normalize;
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double rho;
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/*vectors contains bounds for normalization*/
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std::vector < eoRealInterval > bounds;
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ObjectiveVector ref_point;
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};
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template<class ObjectiveVector>
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class moeoDualHyperVolumeDifferenceMetric : public moeoHyperVolumeDifferenceMetric<ObjectiveVector>
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{
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protected:
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using moeoHyperVolumeDifferenceMetric<ObjectiveVector>::rho;
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using moeoHyperVolumeDifferenceMetric<ObjectiveVector>::normalize;
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using moeoHyperVolumeDifferenceMetric<ObjectiveVector>::ref_point;
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using moeoHyperVolumeDifferenceMetric<ObjectiveVector>::bounds;
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public:
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typedef typename ObjectiveVector::Type Type;
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moeoDualHyperVolumeDifferenceMetric( bool _normalize=true, double _rho=1.1)
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: moeoHyperVolumeDifferenceMetric<ObjectiveVector>(_normalize, _rho)
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{
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}
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moeoDualHyperVolumeDifferenceMetric( bool _normalize/*=true*/, ObjectiveVector& _ref_point/*=NULL*/ )
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: moeoHyperVolumeDifferenceMetric<ObjectiveVector>( _normalize, _ref_point )
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{
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}
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/**
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* calculates and returns the HyperVolume value of a pareto front
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* @param _set1 the vector contains all objective Vector of the first pareto front
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* @param _set2 the vector contains all objective Vector of the second pareto front
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*/
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virtual double operator()(const std::vector < ObjectiveVector > & _set1, const std::vector < ObjectiveVector > & _set2)
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{
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#ifndef NDEBUG
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// the two sets must be homogeneous in feasibility
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assert( _set1.size() > 0 );
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for( unsigned int i=1; i<_set1.size(); ++i ) {
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assert( _set1[i].is_feasible() == _set1[0].is_feasible() );
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}
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assert( _set2.size() > 0 );
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for( unsigned int i=1; i<_set2.size(); ++i ) {
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assert( _set2[i].is_feasible() == _set2[0].is_feasible() );
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}
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// and they must have the same feasibility
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assert( _set1[0].is_feasible() == _set2[0].is_feasible() );
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#endif
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bool feasible = _set1[0].is_feasible();
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double hypervolume_set1;
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double hypervolume_set2;
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if(rho >= 1.0){
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//determine bounds
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setup(_set1, _set2);
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//determine reference point
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for (unsigned int i=0; i<ObjectiveVector::Traits::nObjectives(); i++){
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if(normalize){
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if (ObjectiveVector::Traits::minimizing(i))
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ref_point[i]= Type(rho, feasible);
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else
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ref_point[i]= Type(1-rho, feasible);
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}
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else{
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if (ObjectiveVector::Traits::minimizing(i))
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ref_point[i]= Type(bounds[i].maximum() * rho, feasible);
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else
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ref_point[i]= Type(bounds[i].maximum() * (1-rho), feasible);
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}
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}
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//if no normalization, reinit bounds to O..1 for
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if(!normalize)
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for (unsigned int i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
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bounds[i] = eoRealInterval(0,1);
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}
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else if(normalize)
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setup(_set1, _set2);
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moeoHyperVolumeMetric <ObjectiveVector> unaryMetric(ref_point, bounds);
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hypervolume_set1 = unaryMetric(_set1);
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hypervolume_set2 = unaryMetric(_set2);
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return hypervolume_set1 - hypervolume_set2;
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}
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};
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#endif /*MOEOHYPERVOLUMEMETRIC_H_*/
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