merge ParadisEO-MOEO v-1.0

git-svn-id: svn://scm.gforge.inria.fr/svnroot/paradiseo@400 331e1502-861f-0410-8da2-ba01fb791d7f

trunk/paradiseo-moeo/src/diversity/moeoFrontByFrontCrowdingDistanceDiversityAssignment.h

@@ -0,0 +1,133 @@
+// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
+
+//-----------------------------------------------------------------------------
+// moeoFrontByFrontCrowdingDistanceDiversityAssignment.h
+// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
+/*
+    This library...
+
+    Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
+ */
+//-----------------------------------------------------------------------------
+
+#ifndef MOEOFRONTBYFRONTCROWDINGDISTANCEDIVERSITYASSIGNMENT_H_
+#define MOEOFRONTBYFRONTCROWDINGDISTANCEDIVERSITYASSIGNMENT_H_
+
+#include <diversity/moeoCrowdingDistanceDiversityAssignment.h>
+#include <comparator/moeoFitnessThenDiversityComparator.h>
+
+/**
+ * Diversity assignment sheme based on crowding distance proposed in:
+ * 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).
+ * Tis strategy assigns diversity values FRONT BY FRONT. It is, for instance, used in NSGA-II.
+ */
+template < class MOEOT >
+class moeoFrontByFrontCrowdingDistanceDiversityAssignment : public moeoCrowdingDistanceDiversityAssignment < MOEOT >
+{
+public:
+
+    /** the objective vector type of the solutions */
+    typedef typename MOEOT::ObjectiveVector ObjectiveVector;
+
+
+    /**
+     * @warning NOT IMPLEMENTED, DO NOTHING !
+     * Updates the diversity values of the whole population _pop by taking the deletion of the objective vector _objVec into account.
+     * @param _pop the population
+     * @param _objVec the objective vector
+     * @warning NOT IMPLEMENTED, DO NOTHING !
+     */
+    void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
+    {
+        std::cout << "WARNING : updateByDeleting not implemented in moeoFrontByFrontCrowdingDistanceDiversityAssignment" << std::endl;
+    }
+
+
+private:
+
+    using moeoCrowdingDistanceDiversityAssignment < MOEOT >::inf;
+    using moeoCrowdingDistanceDiversityAssignment < MOEOT >::tiny;
+
+    /**
+     * Sets the distance values
+     * @param _pop the population
+     */
+    void setDistances (eoPop < MOEOT > & _pop)
+    {
+        unsigned int a,b;
+        double min, max, distance;
+        unsigned int nObjectives = MOEOT::ObjectiveVector::nObjectives();
+        // set diversity to 0 for every individual
+        for (unsigned int i=0; i<_pop.size(); i++)
+        {
+            _pop[i].diversity(0.0);
+        }
+        // sort the whole pop according to fitness values
+        moeoFitnessThenDiversityComparator < MOEOT > fitnessComparator;
+        std::sort(_pop.begin(), _pop.end(), fitnessComparator);
+        // compute the crowding distance values for every individual "front" by "front" (front : from a to b)
+        a = 0;	        			// the front starts at a
+        while (a < _pop.size())
+        {
+            b = lastIndex(_pop,a);	// the front ends at b
+            // if there is less than 2 individuals in the front...
+            if ((b-a) < 2)
+            {
+                for (unsigned int i=a; i<=b; i++)
+                {
+                    _pop[i].diversity(inf());
+                }
+            }
+            // else...
+            else
+            {
+                // for each objective
+                for (unsigned int obj=0; obj<nObjectives; obj++)
+                {
+                    // sort in the descending order using the values of the objective 'obj'
+                    moeoOneObjectiveComparator < MOEOT > objComp(obj);
+                    std::sort(_pop.begin()+a, _pop.begin()+b+1, objComp);
+                    // min & max
+                    min = _pop[b].objectiveVector()[obj];
+                    max = _pop[a].objectiveVector()[obj];
+                    // avoid extreme case
+                    if (min == max)
+                    {
+                        min -= tiny();
+                        max += tiny();
+                    }
+                    // set the diversity value to infiny for min and max
+                    _pop[a].diversity(inf());
+                    _pop[b].diversity(inf());
+                    // set the diversity values for the other individuals
+                    for (unsigned int i=a+1; i<b; i++)
+                    {
+                        distance = (_pop[i-1].objectiveVector()[obj] - _pop[i+1].objectiveVector()[obj]) / (max-min);
+                        _pop[i].diversity(_pop[i].diversity() + distance);
+                    }
+                }
+            }
+            // go to the next front
+            a = b+1;
+        }
+    }
+
+
+    /**
+     * Returns the index of the last individual having the same fitness value than _pop[_start]
+     * @param _pop the population
+     * @param _start the index to start from
+     */
+    unsigned int lastIndex (eoPop < MOEOT > & _pop, unsigned int _start)
+    {
+        unsigned int i=_start;
+        while ( (i<_pop.size()-1) && (_pop[i].fitness()==_pop[i+1].fitness()) )
+        {
+            i++;
+        }
+        return i;
+    }
+
+};
+
+#endif /*MOEOFRONTBYFRONTCROWDINGDISTANCEDIVERSITYASSIGNMENT_H_*/