update metric
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7 changed files with 277 additions and 331 deletions
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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
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//-----------------------------------------------------------------------------
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// moeoAdditiveEpsilonBinaryMetric.h
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// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
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/*
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This library...
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Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
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*/
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//-----------------------------------------------------------------------------
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#ifndef MOEOADDITIVEEPSILONBINARYMETRIC_H_
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#define MOEOADDITIVEEPSILONBINARYMETRIC_H_
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#include <metric/moeoNormalizedSolutionVsSolutionBinaryMetric.h>
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/**
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* Additive epsilon binary metric allowing to compare two objective vectors as proposed in
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* Zitzler E., Thiele L., Laumanns M., Fonseca C. M., Grunert da Fonseca V.:
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* Performance Assessment of Multiobjective Optimizers: An Analysis and Review. IEEE Transactions on Evolutionary Computation 7(2), pp.117–132 (2003).
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*/
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template < class ObjectiveVector >
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class moeoAdditiveEpsilonBinaryMetric : public moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double >
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{
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public:
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/**
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* Returns the minimal distance by which the objective vector _o1 must be translated in all objectives
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* so that it weakly dominates the objective vector _o2
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* @warning don't forget to set the bounds for every objective before the call of this function
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* @param _o1 the first objective vector
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* @param _o2 the second objective vector
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*/
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double operator()(const ObjectiveVector & _o1, const ObjectiveVector & _o2)
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{
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// computation of the epsilon value for the first objective
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double result = epsilon(_o1, _o2, 0);
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// computation of the epsilon value for the other objectives
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double tmp;
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for (unsigned int i=1; i<ObjectiveVector::Traits::nObjectives(); i++)
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{
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tmp = epsilon(_o1, _o2, i);
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result = std::max(result, tmp);
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}
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// returns the maximum epsilon value
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return result;
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}
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private:
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/** the bounds for every objective */
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using moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > :: bounds;
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/**
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* Returns the epsilon value by which the objective vector _o1 must be translated in the objective _obj
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* so that it dominates the objective vector _o2
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* @param _o1 the first objective vector
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* @param _o2 the second objective vector
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* @param _obj the index of the objective
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*/
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double epsilon(const ObjectiveVector & _o1, const ObjectiveVector & _o2, const unsigned int _obj)
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{
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double result;
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// if the objective _obj have to be minimized
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if (ObjectiveVector::Traits::minimizing(_obj))
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{
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// _o1[_obj] - _o2[_obj]
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result = ( (_o1[_obj] - bounds[_obj].minimum()) / bounds[_obj].range() ) - ( (_o2[_obj] - bounds[_obj].minimum()) / bounds[_obj].range() );
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}
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// if the objective _obj have to be maximized
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else
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{
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// _o2[_obj] - _o1[_obj]
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result = ( (_o2[_obj] - bounds[_obj].minimum()) / bounds[_obj].range() ) - ( (_o1[_obj] - bounds[_obj].minimum()) / bounds[_obj].range() );
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}
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return result;
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}
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};
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#endif /*MOEOADDITIVEEPSILONBINARYMETRIC_H_*/
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@ -1,88 +0,0 @@
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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
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//-----------------------------------------------------------------------------
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// moeoBinaryMetricSavingUpdater.h
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// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
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/*
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This library...
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Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
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*/
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//-----------------------------------------------------------------------------
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#ifndef MOEOBINARYMETRICSAVINGUPDATER_H_
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#define MOEOBINARYMETRICSAVINGUPDATER_H_
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#include <fstream>
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#include <string>
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#include <eoPop.h>
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#include <utils/eoUpdater.h>
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#include <metric/moeoMetric.h>
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/**
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* This class allows to save the progression of a binary metric comparing the objective vectors of the current population (or archive)
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* with the objective vectors of the population (or archive) of the generation (n-1) into a file
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*/
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template < class MOEOT >
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class moeoBinaryMetricSavingUpdater : public eoUpdater
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{
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public:
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/**
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* The objective vector type of a solution
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*/
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typedef typename MOEOT::ObjectiveVector ObjectiveVector;
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/**
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* Ctor
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* @param _metric the binary metric comparing two Pareto sets
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* @param _pop the main population
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* @param _filename the target filename
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*/
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moeoBinaryMetricSavingUpdater (moeoVectorVsVectorBinaryMetric < ObjectiveVector, double > & _metric, const eoPop < MOEOT > & _pop, std::string _filename) :
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metric(_metric), pop(_pop), filename(_filename), counter(1)
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{}
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/**
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* Saves the metric's value for the current generation
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*/
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void operator()() {
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if (pop.size()) {
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if (firstGen) {
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firstGen = false;
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}
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else {
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// creation of the two Pareto sets
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std::vector < ObjectiveVector > from;
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std::vector < ObjectiveVector > to;
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for (unsigned i=0; i<pop.size(); i++)
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from.push_back(pop[i].objectiveVector());
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for (unsigned i=0 ; i<oldPop.size(); i++)
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to.push_back(oldPop[i].objectiveVector());
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// writing the result into the file
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std::ofstream f (filename.c_str(), std::ios::app);
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f << counter++ << ' ' << metric(from,to) << std::endl;
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f.close();
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}
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oldPop = pop;
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}
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}
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private:
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/** binary metric comparing two Pareto sets */
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moeoVectorVsVectorBinaryMetric < ObjectiveVector, double > & metric;
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/** main population */
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const eoPop < MOEOT > & pop;
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/** (n-1) population */
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eoPop< MOEOT > oldPop;
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/** target filename */
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std::string filename;
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/** is it the first generation ? */
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bool firstGen;
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/** counter */
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unsigned counter;
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};
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#endif /*MOEOBINARYMETRICSAVINGUPDATER_H_*/
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@ -13,7 +13,7 @@
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#ifndef MOEOCONTRIBUTIONMETRIC_H_
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#define MOEOCONTRIBUTIONMETRIC_H_
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#include <moeoObjectiveVectorComparator.h>
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#include <comparator/moeoParetoObjectiveVectorComparator.h>
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#include <metric/moeoMetric.h>
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/**
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@ -31,11 +31,11 @@ public:
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* @param _set2 the second Pareto set
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*/
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double operator()(const std::vector < ObjectiveVector > & _set1, const std::vector < ObjectiveVector > & _set2) {
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unsigned c = card_C(_set1, _set2);
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unsigned w1 = card_W(_set1, _set2);
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unsigned n1 = card_N(_set1, _set2);
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unsigned w2 = card_W(_set2, _set1);
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unsigned n2 = card_N(_set2, _set1);
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unsigned int c = card_C(_set1, _set2);
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unsigned int w1 = card_W(_set1, _set2);
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unsigned int n1 = card_N(_set1, _set2);
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unsigned int w2 = card_W(_set2, _set1);
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unsigned int n2 = card_N(_set2, _set1);
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return (double) (c / 2.0 + w1 + n1) / (c + w1 + n1 + w2 + n2);
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}
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@ -51,10 +51,10 @@ private:
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* @param _set1 the first Pareto set
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* @param _set2 the second Pareto set
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*/
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unsigned card_C (const std::vector < ObjectiveVector > & _set1, const std::vector < ObjectiveVector > & _set2) {
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unsigned c=0;
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for (unsigned i=0; i<_set1.size(); i++)
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for (unsigned j=0; j<_set2.size(); j++)
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unsigned int card_C (const std::vector < ObjectiveVector > & _set1, const std::vector < ObjectiveVector > & _set2) {
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unsigned int c=0;
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for (unsigned int i=0; i<_set1.size(); i++)
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for (unsigned int j=0; j<_set2.size(); j++)
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if (_set1[i] == _set2[j]) {
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c++;
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break;
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return c;
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}
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/**
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* Returns the number of solutions in '_set1' dominating at least one solution of '_set2'
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* @param _set1 the first Pareto set
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* @param _set2 the second Pareto set
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*/
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unsigned card_W (const std::vector < ObjectiveVector > & _set1, const std::vector < ObjectiveVector > & _set2) {
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unsigned w=0;
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for (unsigned i=0; i<_set1.size(); i++)
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for (unsigned j=0; j<_set2.size(); j++)
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unsigned int card_W (const std::vector < ObjectiveVector > & _set1, const std::vector < ObjectiveVector > & _set2) {
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unsigned int w=0;
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for (unsigned int i=0; i<_set1.size(); i++)
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for (unsigned int j=0; j<_set2.size(); j++)
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if (paretoComparator(_set2[j], _set1[i]))
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{
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w++;
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return w;
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}
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/**
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* Returns the number of solutions in '_set1' having no relation of dominance with those from '_set2'
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* @param _set1 the first Pareto set
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* @param _set2 the second Pareto set
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*/
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unsigned card_N (const std::vector < ObjectiveVector > & _set1, const std::vector < ObjectiveVector > & _set2) {
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unsigned n=0;
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for (unsigned i=0; i<_set1.size(); i++) {
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unsigned int card_N (const std::vector < ObjectiveVector > & _set1, const std::vector < ObjectiveVector > & _set2) {
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unsigned int n=0;
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for (unsigned int i=0; i<_set1.size(); i++) {
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bool domin_rel = false;
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for (unsigned j=0; j<_set2.size(); j++)
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for (unsigned int j=0; j<_set2.size(); j++)
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if ( (paretoComparator(_set2[j], _set1[i])) || (paretoComparator(_set1[i], _set2[j])) )
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{
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domin_rel = true;
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#ifndef MOEOENTROPYMETRIC_H_
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#define MOEOENTROPYMETRIC_H_
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#include <vector>
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#include <comparator/moeoParetoObjectiveVectorComparator.h>
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#include <metric/moeoMetric.h>
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/**
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std::vector< ObjectiveVector > union_set1_star; // rotf again ...
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computeUnion (set1, star, union_set1_star);
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unsigned C = union_set1_star.size();
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unsigned int C = union_set1_star.size();
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float omega=0;
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float entropy=0;
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for (unsigned i=0 ; i<C ; i++) {
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unsigned N_i = howManyInNicheOf (union_set1_star, union_set1_star[i], star.size());
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unsigned n_i = howManyInNicheOf (set1, union_set1_star[i], star.size());
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for (unsigned int i=0 ; i<C ; i++) {
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unsigned int N_i = howManyInNicheOf (union_set1_star, union_set1_star[i], star.size());
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unsigned int n_i = howManyInNicheOf (set1, union_set1_star[i], star.size());
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if (n_i > 0) {
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omega += 1.0 / N_i;
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entropy += (float) n_i / (N_i * C) * log (((float) n_i / C) / log (2.0));
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* @param _f a Pareto set
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*/
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void removeDominated(std::vector < ObjectiveVector > & _f) {
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for (unsigned i=0 ; i<_f.size(); i++) {
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for (unsigned int i=0 ; i<_f.size(); i++) {
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bool dom = false;
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for (unsigned j=0; j<_f.size(); j++)
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for (unsigned int j=0; j<_f.size(); j++)
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if (i != j && paretoComparator(_f[i],_f[j]))
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{
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dom = true;
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vect_min_val.clear();
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vect_max_val.clear();
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for (unsigned char i=0 ; i<ObjectiveVector::nObjectives(); i++) {
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for (unsigned int i=0 ; i<ObjectiveVector::nObjectives(); i++) {
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float min_val = _f.front()[i], max_val = min_val;
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for (unsigned j=1 ; j<_f.size(); j++) {
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for (unsigned int j=1 ; j<_f.size(); j++) {
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if (_f[j][i] < min_val)
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min_val = _f[j][i];
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if (_f[j][i]>max_val)
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* @param _f a Pareto set
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*/
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void normalize (std::vector< ObjectiveVector > & _f) {
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for (unsigned i=0 ; i<ObjectiveVector::nObjectives(); i++)
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for (unsigned j=0; j<_f.size(); j++)
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for (unsigned int i=0 ; i<ObjectiveVector::nObjectives(); i++)
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for (unsigned int j=0; j<_f.size(); j++)
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_f[j][i] = (_f[j][i] - vect_min_val[i]) / (vect_max_val[i] - vect_min_val[i]);
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}
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*/
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void computeUnion(const std::vector< ObjectiveVector > & _f1, const std::vector< ObjectiveVector > & _f2, std::vector< ObjectiveVector > & _f) {
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_f = _f1 ;
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for (unsigned i=0; i<_f2.size(); i++) {
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for (unsigned int i=0; i<_f2.size(); i++) {
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bool b = false;
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for (unsigned j=0; j<_f1.size(); j ++)
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for (unsigned int j=0; j<_f1.size(); j ++)
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if (_f1[j] == _f2[i]) {
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b = true;
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break;
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/**
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* How many in niche
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*/
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unsigned howManyInNicheOf (const std::vector< ObjectiveVector > & _f, const ObjectiveVector & _s, unsigned _size) {
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unsigned n=0;
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for (unsigned i=0 ; i<_f.size(); i++) {
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unsigned int howManyInNicheOf (const std::vector< ObjectiveVector > & _f, const ObjectiveVector & _s, unsigned int _size) {
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unsigned int n=0;
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for (unsigned int i=0 ; i<_f.size(); i++) {
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if (euclidianDistance(_f[i], _s) < (_s.size() / (double) _size))
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n++;
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}
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/**
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* Euclidian distance
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*/
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double euclidianDistance (const ObjectiveVector & _set1, const ObjectiveVector & _to, unsigned _deg = 2) {
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double euclidianDistance (const ObjectiveVector & _set1, const ObjectiveVector & _to, unsigned int _deg = 2) {
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double dist=0;
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for (unsigned i=0; i<_set1.size(); i++)
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for (unsigned int i=0; i<_set1.size(); i++)
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dist += pow(fabs(_set1[i] - _to[i]), (int)_deg);
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return pow(dist, 1.0 / _deg);
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}
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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
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//-----------------------------------------------------------------------------
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// moeoHypervolumeBinaryMetric.h
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// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
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/*
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This library...
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Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
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*/
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//-----------------------------------------------------------------------------
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#ifndef MOEOHYPERVOLUMEBINARYMETRIC_H_
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#define MOEOHYPERVOLUMEBINARYMETRIC_H_
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#include <stdexcept>
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#include <comparator/moeoParetoObjectiveVectorComparator.h>
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#include <metric/moeoNormalizedSolutionVsSolutionBinaryMetric.h>
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/**
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* Hypervolume binary metric allowing to compare two objective vectors as proposed in
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* Zitzler E., Künzli S.: Indicator-Based Selection in Multiobjective Search. In Parallel Problem Solving from Nature (PPSN VIII).
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* Lecture Notes in Computer Science 3242, Springer, Birmingham, UK pp.832–842 (2004).
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* This indicator is based on the hypervolume concept introduced in
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* Zitzler, E., Thiele, L.: Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study.
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* Parallel Problem Solving from Nature (PPSN-V), pp.292-301 (1998).
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*/
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template < class ObjectiveVector >
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class moeoHypervolumeBinaryMetric : public moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double >
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{
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public:
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/**
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* Ctor
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* @param _rho value used to compute the reference point from the worst values for each objective (default : 1.1)
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*/
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moeoHypervolumeBinaryMetric(double _rho = 1.1) : rho(_rho)
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{
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// not-a-maximization problem check
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for (unsigned int i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
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{
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if (ObjectiveVector::Traits::maximizing(i))
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{
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throw std::runtime_error("Hypervolume binary metric not yet implemented for a maximization problem in moeoHypervolumeBinaryMetric");
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}
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}
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// consistency check
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if (rho < 1)
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{
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std::cout << "Warning, value used to compute the reference point rho for the hypervolume calculation must not be smaller than 1" << std::endl;
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std::cout << "Adjusted to 1" << std::endl;
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rho = 1;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* Returns the volume of the space that is dominated by _o2 but not by _o1 with respect to a reference point computed using rho.
|
||||
* @warning don't forget to set the bounds for every objective before the call of this function
|
||||
* @param _o1 the first objective vector
|
||||
* @param _o2 the second objective vector
|
||||
*/
|
||||
double operator()(const ObjectiveVector & _o1, const ObjectiveVector & _o2)
|
||||
{
|
||||
double result;
|
||||
// if _o2 is dominated by _o1
|
||||
if ( paretoComparator(_o2,_o1) )
|
||||
{
|
||||
result = - hypervolume(_o1, _o2, ObjectiveVector::Traits::nObjectives()-1);
|
||||
}
|
||||
else
|
||||
{
|
||||
result = hypervolume(_o2, _o1, ObjectiveVector::Traits::nObjectives()-1);
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
|
||||
private:
|
||||
|
||||
/** value used to compute the reference point from the worst values for each objective */
|
||||
double rho;
|
||||
/** the bounds for every objective */
|
||||
using moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > :: bounds;
|
||||
/** Functor to compare two objective vectors according to Pareto dominance relation */
|
||||
moeoParetoObjectiveVectorComparator < ObjectiveVector > paretoComparator;
|
||||
|
||||
|
||||
/**
|
||||
* Returns the volume of the space that is dominated by _o2 but not by _o1 with respect to a reference point computed using rho for the objective _obj.
|
||||
* @param _o1 the first objective vector
|
||||
* @param _o2 the second objective vector
|
||||
* @param _obj the objective index
|
||||
* @param _flag used for iteration, if _flag=true _o2 is not talen into account (default : false)
|
||||
*/
|
||||
double hypervolume(const ObjectiveVector & _o1, const ObjectiveVector & _o2, const unsigned int _obj, const bool _flag = false)
|
||||
{
|
||||
double result;
|
||||
double range = rho * bounds[_obj].range();
|
||||
double max = bounds[_obj].minimum() + range;
|
||||
// value of _1 for the objective _obj
|
||||
double v1 = _o1[_obj];
|
||||
// value of _2 for the objective _obj (if _flag=true, v2=max)
|
||||
double v2;
|
||||
if (_flag)
|
||||
{
|
||||
v2 = max;
|
||||
}
|
||||
else
|
||||
{
|
||||
v2 = _o2[_obj];
|
||||
}
|
||||
// computation of the volume
|
||||
if (_obj == 0)
|
||||
{
|
||||
if (v1 < v2)
|
||||
{
|
||||
result = (v2 - v1) / range;
|
||||
}
|
||||
else
|
||||
{
|
||||
result = 0;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
if (v1 < v2)
|
||||
{
|
||||
result = ( hypervolume(_o1, _o2, _obj-1, true) * (v2 - v1) / range ) + ( hypervolume(_o1, _o2, _obj-1) * (max - v2) / range );
|
||||
}
|
||||
else
|
||||
{
|
||||
result = hypervolume(_o1, _o2, _obj-1) * (max - v2) / range;
|
||||
}
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
};
|
||||
|
||||
#endif /*MOEOHYPERVOLUMEBINARYMETRIC_H_*/
|
||||
|
|
@ -13,61 +13,55 @@
|
|||
#ifndef MOEOMETRIC_H_
|
||||
#define MOEOMETRIC_H_
|
||||
|
||||
#include <vector>
|
||||
#include <eoFunctor.h>
|
||||
|
||||
/**
|
||||
* Base class for performance metrics (also known as quality indicators).
|
||||
*/
|
||||
class moeoMetric : public eoFunctorBase
|
||||
{};
|
||||
class moeoMetric : public eoFunctorBase {};
|
||||
|
||||
|
||||
/**
|
||||
* Base class for unary metrics.
|
||||
*/
|
||||
template < class A, class R >
|
||||
class moeoUnaryMetric : public eoUF < A, R >, public moeoMetric
|
||||
{};
|
||||
class moeoUnaryMetric : public eoUF < A, R >, public moeoMetric {};
|
||||
|
||||
|
||||
/**
|
||||
* Base class for binary metrics.
|
||||
*/
|
||||
template < class A1, class A2, class R >
|
||||
class moeoBinaryMetric : public eoBF < A1, A2, R >, public moeoMetric
|
||||
{};
|
||||
class moeoBinaryMetric : public eoBF < A1, A2, R >, public moeoMetric {};
|
||||
|
||||
|
||||
/**
|
||||
* Base class for unary metrics dedicated to the performance evaluation of a single solution's objective vector.
|
||||
*/
|
||||
template < class ObjectiveVector, class R >
|
||||
class moeoSolutionUnaryMetric : public moeoUnaryMetric < const ObjectiveVector &, R >
|
||||
{};
|
||||
class moeoSolutionUnaryMetric : public moeoUnaryMetric < const ObjectiveVector &, R > {};
|
||||
|
||||
|
||||
/**
|
||||
* Base class for unary metrics dedicated to the performance evaluation of a Pareto set (a vector of objective vectors)
|
||||
*/
|
||||
template < class ObjectiveVector, class R >
|
||||
class moeoVectorUnaryMetric : public moeoUnaryMetric < const std::vector < ObjectiveVector > &, R >
|
||||
{};
|
||||
class moeoVectorUnaryMetric : public moeoUnaryMetric < const std::vector < ObjectiveVector > &, R > {};
|
||||
|
||||
|
||||
/**
|
||||
* Base class for binary metrics dedicated to the performance comparison between two solutions's objective vectors.
|
||||
*/
|
||||
template < class ObjectiveVector, class R >
|
||||
class moeoSolutionVsSolutionBinaryMetric : public moeoBinaryMetric < const ObjectiveVector &, const ObjectiveVector &, R >
|
||||
{};
|
||||
class moeoSolutionVsSolutionBinaryMetric : public moeoBinaryMetric < const ObjectiveVector &, const ObjectiveVector &, R > {};
|
||||
|
||||
|
||||
/**
|
||||
* Base class for binary metrics dedicated to the performance comparison between two Pareto sets (two vectors of objective vectors)
|
||||
*/
|
||||
template < class ObjectiveVector, class R >
|
||||
class moeoVectorVsVectorBinaryMetric : public moeoBinaryMetric < const std::vector < ObjectiveVector > &, const std::vector < ObjectiveVector > &, R >
|
||||
{};
|
||||
class moeoVectorVsVectorBinaryMetric : public moeoBinaryMetric < const std::vector < ObjectiveVector > &, const std::vector < ObjectiveVector > &, R > {};
|
||||
|
||||
|
||||
#endif /*MOEOMETRIC_H_*/
|
||||
|
|
|
|||
|
|
@ -13,11 +13,10 @@
|
|||
#ifndef MOEONORMALIZEDSOLUTIONVSSOLUTIONBINARYMETRIC_H_
|
||||
#define MOEONORMALIZEDSOLUTIONVSSOLUTIONBINARYMETRIC_H_
|
||||
|
||||
#include <stdexcept>
|
||||
#include <vector>
|
||||
#include <utils/eoRealBounds.h>
|
||||
#include <metric/moeoMetric.h>
|
||||
|
||||
|
||||
/**
|
||||
* Base class for binary metrics dedicated to the performance comparison between two solutions's objective vectors using normalized values.
|
||||
* Then, indicator values lie in the interval [-1,1].
|
||||
|
|
@ -35,7 +34,7 @@ public:
|
|||
{
|
||||
bounds.resize(ObjectiveVector::Traits::nObjectives());
|
||||
// initialize bounds in case someone does not want to use them
|
||||
for (unsigned i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
|
||||
for (unsigned int i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
|
||||
{
|
||||
bounds[i] = eoRealInterval(0,1);
|
||||
}
|
||||
|
|
@ -48,7 +47,7 @@ public:
|
|||
* @param _max upper bound
|
||||
* @param _obj the objective index
|
||||
*/
|
||||
void setup(double _min, double _max, unsigned _obj)
|
||||
void setup(double _min, double _max, unsigned int _obj)
|
||||
{
|
||||
if (_min == _max)
|
||||
{
|
||||
|
|
@ -58,12 +57,13 @@ public:
|
|||
bounds[_obj] = eoRealInterval(_min, _max);
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* Sets the lower bound and the upper bound for the objective _obj using a eoRealInterval object
|
||||
* @param _realInterval the eoRealInterval object
|
||||
* @param _obj the objective index
|
||||
*/
|
||||
virtual void setup(eoRealInterval _realInterval, unsigned _obj)
|
||||
virtual void setup(eoRealInterval _realInterval, unsigned int _obj)
|
||||
{
|
||||
bounds[_obj] = _realInterval;
|
||||
}
|
||||
|
|
@ -85,193 +85,4 @@ protected:
|
|||
|
||||
};
|
||||
|
||||
|
||||
/**
|
||||
* Additive epsilon binary metric allowing to compare two objective vectors as proposed in
|
||||
* Zitzler E., Thiele L., Laumanns M., Fonseca C. M., Grunert da Fonseca V.:
|
||||
* Performance Assessment of Multiobjective Optimizers: An Analysis and Review. IEEE Transactions on Evolutionary Computation 7(2), pp.117–132 (2003).
|
||||
*/
|
||||
template < class ObjectiveVector >
|
||||
class moeoAdditiveEpsilonBinaryMetric : public moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double >
|
||||
{
|
||||
public:
|
||||
|
||||
/**
|
||||
* Returns the minimal distance by which the objective vector _o1 must be translated in all objectives
|
||||
* so that it weakly dominates the objective vector _o2
|
||||
* @warning don't forget to set the bounds for every objective before the call of this function
|
||||
* @param _o1 the first objective vector
|
||||
* @param _o2 the second objective vector
|
||||
*/
|
||||
double operator()(const ObjectiveVector & _o1, const ObjectiveVector & _o2)
|
||||
{
|
||||
// computation of the epsilon value for the first objective
|
||||
double result = epsilon(_o1, _o2, 0);
|
||||
// computation of the epsilon value for the other objectives
|
||||
double tmp;
|
||||
for (unsigned i=1; i<ObjectiveVector::Traits::nObjectives(); i++)
|
||||
{
|
||||
tmp = epsilon(_o1, _o2, i);
|
||||
result = std::max(result, tmp);
|
||||
}
|
||||
// returns the maximum epsilon value
|
||||
return result;
|
||||
}
|
||||
|
||||
|
||||
private:
|
||||
|
||||
/** the bounds for every objective */
|
||||
using moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > :: bounds;
|
||||
|
||||
|
||||
/**
|
||||
* Returns the epsilon value by which the objective vector _o1 must be translated in the objective _obj
|
||||
* so that it dominates the objective vector _o2
|
||||
* @param _o1 the first objective vector
|
||||
* @param _o2 the second objective vector
|
||||
* @param _obj the index of the objective
|
||||
*/
|
||||
double epsilon(const ObjectiveVector & _o1, const ObjectiveVector & _o2, const unsigned _obj)
|
||||
{
|
||||
double result;
|
||||
// if the objective _obj have to be minimized
|
||||
if (ObjectiveVector::Traits::minimizing(_obj))
|
||||
{
|
||||
// _o1[_obj] - _o2[_obj]
|
||||
result = ( (_o1[_obj] - bounds[_obj].minimum()) / bounds[_obj].range() ) - ( (_o2[_obj] - bounds[_obj].minimum()) / bounds[_obj].range() );
|
||||
}
|
||||
// if the objective _obj have to be maximized
|
||||
else
|
||||
{
|
||||
// _o2[_obj] - _o1[_obj]
|
||||
result = ( (_o2[_obj] - bounds[_obj].minimum()) / bounds[_obj].range() ) - ( (_o1[_obj] - bounds[_obj].minimum()) / bounds[_obj].range() );
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
};
|
||||
|
||||
|
||||
/**
|
||||
* Hypervolume binary metric allowing to compare two objective vectors as proposed in
|
||||
* Zitzler E., Künzli S.: Indicator-Based Selection in Multiobjective Search. In Parallel Problem Solving from Nature (PPSN VIII).
|
||||
* Lecture Notes in Computer Science 3242, Springer, Birmingham, UK pp.832–842 (2004).
|
||||
* This indicator is based on the hypervolume concept introduced in
|
||||
* Zitzler, E., Thiele, L.: Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study.
|
||||
* Parallel Problem Solving from Nature (PPSN-V), pp.292-301 (1998).
|
||||
*/
|
||||
template < class ObjectiveVector >
|
||||
class moeoHypervolumeBinaryMetric : public moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double >
|
||||
{
|
||||
public:
|
||||
|
||||
/**
|
||||
* Ctor
|
||||
* @param _rho value used to compute the reference point from the worst values for each objective (default : 1.1)
|
||||
*/
|
||||
moeoHypervolumeBinaryMetric(double _rho = 1.1) : rho(_rho)
|
||||
{
|
||||
// not-a-maximization problem check
|
||||
for (unsigned i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
|
||||
{
|
||||
if (ObjectiveVector::Traits::maximizing(i))
|
||||
{
|
||||
throw std::runtime_error("Hypervolume binary metric not yet implemented for a maximization problem in moeoHypervolumeBinaryMetric");
|
||||
}
|
||||
}
|
||||
// consistency check
|
||||
if (rho < 1)
|
||||
{
|
||||
cout << "Warning, value used to compute the reference point rho for the hypervolume calculation must not be smaller than 1" << endl;
|
||||
cout << "Adjusted to 1" << endl;
|
||||
rho = 1;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* Returns the volume of the space that is dominated by _o2 but not by _o1 with respect to a reference point computed using rho.
|
||||
* @warning don't forget to set the bounds for every objective before the call of this function
|
||||
* @param _o1 the first objective vector
|
||||
* @param _o2 the second objective vector
|
||||
*/
|
||||
double operator()(const ObjectiveVector & _o1, const ObjectiveVector & _o2)
|
||||
{
|
||||
double result;
|
||||
// if _o2 is dominated by _o1
|
||||
if ( paretoComparator(_o2,_o1) )
|
||||
{
|
||||
result = - hypervolume(_o1, _o2, ObjectiveVector::Traits::nObjectives()-1);
|
||||
}
|
||||
else
|
||||
{
|
||||
result = hypervolume(_o2, _o1, ObjectiveVector::Traits::nObjectives()-1);
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
|
||||
private:
|
||||
|
||||
/** value used to compute the reference point from the worst values for each objective */
|
||||
double rho;
|
||||
/** the bounds for every objective */
|
||||
using moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > :: bounds;
|
||||
/** Functor to compare two objective vectors according to Pareto dominance relation */
|
||||
moeoParetoObjectiveVectorComparator < ObjectiveVector > paretoComparator;
|
||||
|
||||
/**
|
||||
* Returns the volume of the space that is dominated by _o2 but not by _o1 with respect to a reference point computed using rho for the objective _obj.
|
||||
* @param _o1 the first objective vector
|
||||
* @param _o2 the second objective vector
|
||||
* @param _obj the objective index
|
||||
* @param _flag used for iteration, if _flag=true _o2 is not talen into account (default : false)
|
||||
*/
|
||||
double hypervolume(const ObjectiveVector & _o1, const ObjectiveVector & _o2, const unsigned _obj, const bool _flag = false)
|
||||
{
|
||||
double result;
|
||||
double range = rho * bounds[_obj].range();
|
||||
double max = bounds[_obj].minimum() + range;
|
||||
// value of _1 for the objective _obj
|
||||
double v1 = _o1[_obj];
|
||||
// value of _2 for the objective _obj (if _flag=true, v2=max)
|
||||
double v2;
|
||||
if (_flag)
|
||||
{
|
||||
v2 = max;
|
||||
}
|
||||
else
|
||||
{
|
||||
v2 = _o2[_obj];
|
||||
}
|
||||
// computation of the volume
|
||||
if (_obj == 0)
|
||||
{
|
||||
if (v1 < v2)
|
||||
{
|
||||
result = (v2 - v1) / range;
|
||||
}
|
||||
else
|
||||
{
|
||||
result = 0;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
if (v1 < v2)
|
||||
{
|
||||
result = ( hypervolume(_o1, _o2, _obj-1, true) * (v2 - v1) / range ) + ( hypervolume(_o1, _o2, _obj-1) * (max - v2) / range );
|
||||
}
|
||||
else
|
||||
{
|
||||
result = hypervolume(_o1, _o2, _obj-1) * (max - v2) / range;
|
||||
}
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
};
|
||||
|
||||
|
||||
#endif /*MOEONORMALIZEDSOLUTIONVSSOLUTIONBINARYMETRIC_H_*/
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue