add achievement fitness assignment scheme

git-svn-id: svn://scm.gforge.inria.fr/svnroot/paradiseo@308 331e1502-861f-0410-8da2-ba01fb791d7f

branches/paradiseo-moeo-1.0/src/moeoAchievementFitnessAssignment.h

@@ -0,0 +1,144 @@
+// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
+
+//-----------------------------------------------------------------------------
+// moeoAchievementFitnessAssignment.h
+// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
+/*
+    This library...
+
+    Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
+ */
+//-----------------------------------------------------------------------------
+
+#ifndef MOEOACHIEVEMENTFITNESSASSIGNMENT_H_
+#define MOEOACHIEVEMENTFITNESSASSIGNMENT_H_
+
+#include <eoPop.h>
+#include <moeoFitnessAssignment.h>
+
+/**
+ * 
+ */
+template < class MOEOT >
+class moeoAchievementFitnessAssignment : public moeoScalarFitnessAssignment < MOEOT >
+{
+public:
+	
+	/** the objective vector type of the solutions */
+    typedef typename MOEOT::ObjectiveVector ObjectiveVector;
+
+
+	/**
+     * Default ctor
+     * @param _reference reference point vector
+     * @param _lambdas weighted coefficients vector
+     * @param _spn arbitrary small positive number (0 < _spn << 1)
+     */
+	moeoAchievementFitnessAssignment(ObjectiveVector & _reference, vector < double > & _lambdas, double _spn=0.0001) : reference(_reference), lambdas(_lambdas), spn(_spn)
+	{
+		// consistency check
+		if ((spn < 0.0) || (spn > 1.0))
+        {
+            std::cout << "Warning, the arbitrary small positive number should be > 0 and <<1, adjusted to 0.0001\n";
+            spn = 0.0001;
+        }
+	}
+	
+	
+	/**
+     * Ctor with default values for lambdas (1/nObjectives)
+     * @param _reference reference point vector
+     * @param _spn arbitrary small positive number (0 < _spn << 1)
+     */
+	moeoAchievementFitnessAssignment(ObjectiveVector & _reference, double _spn=0.0001) : reference(_reference), spn(_spn)
+	{
+		// compute the default values for lambdas
+		lambdas = vector < double > (ObjectiveVector::nObjectives());
+		for (unsigned i=0 ; i<lambdas.size(); i++)
+		{
+			lambdas[i] = 1.0 / ObjectiveVector::nObjectives();
+		}
+		// consistency check
+		if ((spn < 0.0) || (spn > 1.0))
+        {
+            std::cout << "Warning, the arbitrary small positive number should be > 0 and <<1, adjusted to 0.0001\n";
+            spn = 0.0001;
+        }
+	}
+	
+	
+	/**
+	 * Sets the fitness values for every solution contained in the population _pop
+     * @param _pop the population
+	 */
+	virtual void operator()(eoPop < MOEOT > & _pop)
+	{
+		for (unsigned i=0; i<_pop.size() ; i++)
+		{
+			compute(_pop[i]);
+		}
+	}
+
+
+	/**
+     * Updates the fitness values of the whole population _pop by taking the deletion of the objective vector _objVec into account (nothing to do).
+     * @param _pop the population
+     * @param _objVec the objective vector
+     */
+    void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
+    {
+        // nothing to do ;-)
+    }
+    
+    
+    /**
+     * Sets the reference point
+     * @param _reference the new reference point
+     */
+    void setReference(const ObjectiveVector & _reference)
+	{
+		reference = _reference;
+	}
+	
+	
+private:
+
+	/** the reference point */
+	ObjectiveVector reference;
+	/** the weighted coefficients vector */
+	vector < double > lambdas;
+	/** an arbitrary small positive number (0 < _spn << 1) */
+	double spn;
+	
+	
+    /**
+     * Returns a big value (regarded as infinite)
+     */
+    double inf() const
+    {
+        return std::numeric_limits<double>::max();
+    }
+	
+	
+	/**
+	 * Computes the fitness value for a solution
+	 * @param _moeo the solution
+	 */
+	void compute(MOEOT & _moeo)
+	{
+		unsigned nobj = MOEOT::ObjectiveVector::nObjectives();
+		double temp;
+		double min = inf();
+		double sum = 0; 
+		for (unsigned obj=0; obj<nobj; obj++)
+		{
+			temp = lambdas[obj] * (reference[obj] - _moeo.objectiveVector()[obj]);
+			min = std::min(min, temp);
+			sum += temp;
+		}
+		_moeo.fitness(min + spn*sum);
+	}
+
+};
+
+#endif /*MOEOACHIEVEMENTFITNESSASSIGNMENT_H_*/