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git-svn-id: svn://scm.gforge.inria.fr/svnroot/paradiseo@232 331e1502-861f-0410-8da2-ba01fb791d7f
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51 changed files with 3899 additions and 3898 deletions
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@ -19,7 +19,7 @@
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#include <moeoObjectiveVectorComparator.h>
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/**
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* Fitness assignment sheme based on Pareto-dominance count proposed in:
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* Fitness assignment sheme based on Pareto-dominance count proposed in:
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* N. Srinivas, K. Deb, "Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms", Evolutionary Computation vol. 2, no. 3, pp. 221-248 (1994)
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* and in:
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* K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, "A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II", IEEE Transactions on Evolutionary Computation, vol. 6, no. 2 (2002).
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@ -30,180 +30,180 @@ class moeoFastNonDominatedSortingFitnessAssignment : public moeoParetoBasedFitne
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{
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public:
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/** the objective vector type of the solutions */
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typedef typename MOEOT::ObjectiveVector ObjectiveVector;
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/**
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* Default ctor
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*/
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moeoFastNonDominatedSortingFitnessAssignment() : comparator(paretoComparator)
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{}
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/**
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* Ctor where you can choose your own way to compare objective vectors
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* @param _comparator the functor used to compare objective vectors
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*/
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moeoFastNonDominatedSortingFitnessAssignment(moeoObjectiveVectorComparator < ObjectiveVector > & _comparator) : comparator(_comparator)
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{}
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/**
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* Sets the fitness values for every solution contained in the population _pop
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* @param _pop the population
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*/
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void operator()(eoPop < MOEOT > & _pop)
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{
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// number of objectives for the problem under consideration
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unsigned nObjectives = MOEOT::ObjectiveVector::nObjectives();
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if (nObjectives == 1)
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{
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// one objective
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oneObjective(_pop);
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}
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else if (nObjectives == 2)
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{
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// two objectives (the two objectives function is still to implement)
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mObjectives(_pop);
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}
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else if (nObjectives > 2)
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{
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// more than two objectives
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mObjectives(_pop);
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}
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else
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{
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// problem with the number of objectives
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throw std::runtime_error("Problem with the number of objectives in moeoNonDominatedSortingFitnessAssignment");
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}
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// a higher fitness is better, so the values need to be inverted
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double max = _pop[0].fitness();
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for (unsigned i=1 ; i<_pop.size() ; i++)
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{
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max = std::max(max, _pop[i].fitness());
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}
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for (unsigned i=0 ; i<_pop.size() ; i++)
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{
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_pop[i].fitness(max - _pop[i].fitness());
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}
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}
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/** the objective vector type of the solutions */
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typedef typename MOEOT::ObjectiveVector ObjectiveVector;
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/**
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* @warning NOT IMPLEMENTED, DO NOTHING !
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* Updates the fitness values of the whole population _pop by taking the deletion of the objective vector _objVec into account.
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* @param _pop the population
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* @param _objecVec the objective vector
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* @warning NOT IMPLEMENTED, DO NOTHING !
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*/
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void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
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{
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cout << "WARNING : updateByDeleting not implemented in moeoNonDominatedSortingFitnessAssignment" << endl;
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}
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/**
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* Default ctor
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*/
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moeoFastNonDominatedSortingFitnessAssignment() : comparator(paretoComparator)
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{}
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/**
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* Ctor where you can choose your own way to compare objective vectors
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* @param _comparator the functor used to compare objective vectors
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*/
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moeoFastNonDominatedSortingFitnessAssignment(moeoObjectiveVectorComparator < ObjectiveVector > & _comparator) : comparator(_comparator)
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{}
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/**
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* Sets the fitness values for every solution contained in the population _pop
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* @param _pop the population
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*/
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void operator()(eoPop < MOEOT > & _pop)
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{
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// number of objectives for the problem under consideration
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unsigned nObjectives = MOEOT::ObjectiveVector::nObjectives();
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if (nObjectives == 1)
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{
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// one objective
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oneObjective(_pop);
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}
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else if (nObjectives == 2)
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{
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// two objectives (the two objectives function is still to implement)
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mObjectives(_pop);
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}
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else if (nObjectives > 2)
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{
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// more than two objectives
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mObjectives(_pop);
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}
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else
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{
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// problem with the number of objectives
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throw std::runtime_error("Problem with the number of objectives in moeoNonDominatedSortingFitnessAssignment");
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}
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// a higher fitness is better, so the values need to be inverted
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double max = _pop[0].fitness();
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for (unsigned i=1 ; i<_pop.size() ; i++)
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{
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max = std::max(max, _pop[i].fitness());
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}
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for (unsigned i=0 ; i<_pop.size() ; i++)
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{
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_pop[i].fitness(max - _pop[i].fitness());
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}
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}
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/**
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* @warning NOT IMPLEMENTED, DO NOTHING !
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* Updates the fitness values of the whole population _pop by taking the deletion of the objective vector _objVec into account.
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* @param _pop the population
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* @param _objecVec the objective vector
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* @warning NOT IMPLEMENTED, DO NOTHING !
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*/
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void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
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{
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cout << "WARNING : updateByDeleting not implemented in moeoNonDominatedSortingFitnessAssignment" << endl;
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}
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private:
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/** Functor to compare two objective vectors */
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moeoObjectiveVectorComparator < ObjectiveVector > & comparator;
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/** Functor to compare two objective vectors according to Pareto dominance relation */
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moeoParetoObjectiveVectorComparator < ObjectiveVector > paretoComparator;
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/**
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* Sets the fitness values for mono-objective problems
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* @param _pop the population
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*/
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void oneObjective (eoPop < MOEOT > & _pop)
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{
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// Functor to compare two solutions on the first objective, then on the second, and so on
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moeoObjectiveComparator < MOEOT > objComparator;
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std::sort(_pop.begin(), _pop.end(), objComparator);
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for (unsigned i=0; i<_pop.size(); i++)
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{
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_pop[i].fitness(i+1);
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}
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}
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/**
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* Sets the fitness values for bi-objective problems with a complexity of O(n log n), where n stands for the population size
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* @param _pop the population
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*/
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void twoObjectives (eoPop < MOEOT > & _pop)
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{
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//... TO DO !
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}
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/**
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* Sets the fitness values for problems with more than two objectives with a complexity of O(n² log n), where n stands for the population size
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* @param _pop the population
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*/
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void mObjectives (eoPop < MOEOT > & _pop)
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{
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// S[i] = indexes of the individuals dominated by _pop[i]
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std::vector < std::vector<unsigned> > S(_pop.size());
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// n[i] = number of individuals that dominate the individual _pop[i]
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std::vector < unsigned > n(_pop.size(), 0);
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// fronts: F[i] = indexes of the individuals contained in the ith front
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std::vector < std::vector<unsigned> > F(_pop.size()+1);
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// used to store the number of the first front
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F[1].reserve(_pop.size());
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for (unsigned p=0; p<_pop.size(); p++)
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{
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for (unsigned q=0; q<_pop.size(); q++)
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{
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// if p dominates q
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if ( comparator(_pop[p].objectiveVector(), _pop[q].objectiveVector()) )
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{
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// add q to the set of solutions dominated by p
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S[p].push_back(q);
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}
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// if q dominates p
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else if ( comparator(_pop[q].objectiveVector(), _pop[p].objectiveVector()) )
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{
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// increment the domination counter of p
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n[p]++;
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}
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}
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// if no individual dominates p
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if (n[p] == 0)
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{
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// p belongs to the first front
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_pop[p].fitness(1);
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F[1].push_back(p);
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}
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}
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// front counter
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unsigned counter=1;
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unsigned p,q;
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while (! F[counter].empty())
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{
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// used to store the number of the next front
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F[counter+1].reserve(_pop.size());
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for (unsigned i=0; i<F[counter].size(); i++)
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{
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p = F[counter][i];
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for (unsigned j=0; j<S[p].size(); j++)
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{
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q = S[p][j];
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n[q]--;
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// if no individual dominates q anymore
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if (n[q] == 0)
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{
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// q belongs to the next front
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_pop[q].fitness(counter+1);
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F[counter+1].push_back(q);
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}
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}
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}
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counter++;
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}
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}
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/** Functor to compare two objective vectors */
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moeoObjectiveVectorComparator < ObjectiveVector > & comparator;
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/** Functor to compare two objective vectors according to Pareto dominance relation */
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moeoParetoObjectiveVectorComparator < ObjectiveVector > paretoComparator;
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/**
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* Sets the fitness values for mono-objective problems
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* @param _pop the population
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*/
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void oneObjective (eoPop < MOEOT > & _pop)
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{
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// Functor to compare two solutions on the first objective, then on the second, and so on
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moeoObjectiveComparator < MOEOT > objComparator;
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std::sort(_pop.begin(), _pop.end(), objComparator);
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for (unsigned i=0; i<_pop.size(); i++)
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{
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_pop[i].fitness(i+1);
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}
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}
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/**
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* Sets the fitness values for bi-objective problems with a complexity of O(n log n), where n stands for the population size
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* @param _pop the population
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*/
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void twoObjectives (eoPop < MOEOT > & _pop)
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{
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//... TO DO !
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}
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/**
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* Sets the fitness values for problems with more than two objectives with a complexity of O(n² log n), where n stands for the population size
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* @param _pop the population
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*/
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void mObjectives (eoPop < MOEOT > & _pop)
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{
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// S[i] = indexes of the individuals dominated by _pop[i]
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std::vector < std::vector<unsigned> > S(_pop.size());
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// n[i] = number of individuals that dominate the individual _pop[i]
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std::vector < unsigned > n(_pop.size(), 0);
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// fronts: F[i] = indexes of the individuals contained in the ith front
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std::vector < std::vector<unsigned> > F(_pop.size()+1);
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// used to store the number of the first front
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F[1].reserve(_pop.size());
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for (unsigned p=0; p<_pop.size(); p++)
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{
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for (unsigned q=0; q<_pop.size(); q++)
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{
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// if p dominates q
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if ( comparator(_pop[p].objectiveVector(), _pop[q].objectiveVector()) )
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{
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// add q to the set of solutions dominated by p
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S[p].push_back(q);
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}
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// if q dominates p
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else if ( comparator(_pop[q].objectiveVector(), _pop[p].objectiveVector()) )
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{
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// increment the domination counter of p
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n[p]++;
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}
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}
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// if no individual dominates p
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if (n[p] == 0)
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{
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// p belongs to the first front
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_pop[p].fitness(1);
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F[1].push_back(p);
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}
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}
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// front counter
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unsigned counter=1;
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unsigned p,q;
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while (! F[counter].empty())
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{
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// used to store the number of the next front
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F[counter+1].reserve(_pop.size());
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for (unsigned i=0; i<F[counter].size(); i++)
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{
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p = F[counter][i];
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for (unsigned j=0; j<S[p].size(); j++)
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{
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q = S[p][j];
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n[q]--;
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// if no individual dominates q anymore
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if (n[q] == 0)
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{
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// q belongs to the next front
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_pop[q].fitness(counter+1);
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F[counter+1].push_back(q);
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}
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}
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}
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counter++;
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}
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}
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};
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