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280
tags/paradiseo-ix86-1.0-beta/paradiseo-moeo/src/algo/moeoIBMOLS.h
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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoIBMOLS.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOIBMOLS_H_
#define MOEOIBMOLS_H_
#include <eoContinue.h>
#include <eoEvalFunc.h>
#include <eoPop.h>
#include <moMove.h>
#include <moMoveInit.h>
#include <moNextMove.h>
#include <algo/moeoLS.h>
#include <archive/moeoArchive.h>
#include <fitness/moeoIndicatorBasedFitnessAssignment.h>
#include <move/moeoMoveIncrEval.h>
/**
* Indicator-Based Multi-Objective Local Search (IBMOLS) as described in
* Basseur M., Burke K. : "Indicator-Based Multi-Objective Local Search" (2007).
*/
template < class MOEOT, class Move >
class moeoIBMOLS : public moeoLS < MOEOT, eoPop < MOEOT > & >
{
public:
/** The type of objective vector */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/**
* Ctor.
* @param _moveInit the move initializer
* @param _nextMove the neighborhood explorer
* @param _eval the full evaluation
* @param _moveIncrEval the incremental evaluation
* @param _fitnessAssignment the fitness assignment strategy
* @param _continuator the stopping criteria
*/
moeoIBMOLS(
moMoveInit < Move > & _moveInit,
moNextMove < Move > & _nextMove,
eoEvalFunc < MOEOT > & _eval,
moeoMoveIncrEval < Move > & _moveIncrEval,
moeoIndicatorBasedFitnessAssignment < MOEOT > & _fitnessAssignment,
eoContinue < MOEOT > & _continuator
) :
moveInit(_moveInit),
nextMove(_nextMove),
eval(_eval),
moveIncrEval(_moveIncrEval),
fitnessAssignment (_fitnessAssignment),
continuator (_continuator)
{}
/**
* Apply the local search until a local archive does not change or
* another stopping criteria is met and update the archive _arch with new non-dominated solutions.
* @param _pop the initial population
* @param _arch the (updated) archive
*/
void operator() (eoPop < MOEOT > & _pop, moeoArchive < MOEOT > & _arch)
{
// evaluation of the objective values
/*
for (unsigned int i=0; i<_pop.size(); i++)
{
eval(_pop[i]);
}
*/
// fitness assignment for the whole population
fitnessAssignment(_pop);
// creation of a local archive
moeoArchive < MOEOT > archive;
// creation of another local archive (for the stopping criteria)
moeoArchive < MOEOT > previousArchive;
// update the archive with the initial population
archive.update(_pop);
do
{
previousArchive.update(archive);
oneStep(_pop);
archive.update(_pop);
} while ( (! archive.equals(previousArchive)) && (continuator(_arch)) );
_arch.update(archive);
}
private:
/** the move initializer */
moMoveInit < Move > & moveInit;
/** the neighborhood explorer */
moNextMove < Move > & nextMove;
/** the full evaluation */
eoEvalFunc < MOEOT > & eval;
/** the incremental evaluation */
moeoMoveIncrEval < Move > & moveIncrEval;
/** the fitness assignment strategy */
moeoIndicatorBasedFitnessAssignment < MOEOT > & fitnessAssignment;
/** the stopping criteria */
eoContinue < MOEOT > & continuator;
/**
* Apply one step of the local search to the population _pop
* @param _pop the population
*/
void oneStep (eoPop < MOEOT > & _pop)
{
////////////////////////////////////////////
int ext_0_idx, ext_1_idx;
ObjectiveVector ext_0_objVec, ext_1_objVec;
///////////////////////////////////////////
// the move
Move move;
// the objective vector and the fitness of the current solution
ObjectiveVector x_objVec;
double x_fitness;
// the index, the objective vector and the fitness of the worst solution in the population (-1 implies that the worst is the newly created one)
int worst_idx;
ObjectiveVector worst_objVec;
double worst_fitness;
// the index current of the current solution to be explored
unsigned int i=0;
// initilization of the move for the first individual
moveInit(move, _pop[i]);
while (i<_pop.size() && continuator(_pop))
{
// x = one neigbour of pop[i]
// evaluate x in the objective space
x_objVec = moveIncrEval(move, _pop[i]);
// update every fitness values to take x into account and compute the fitness of x
x_fitness = fitnessAssignment.updateByAdding(_pop, x_objVec);
////////////////////////////////////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////////////////////////////////
// qui sont les extremes ? (=> min only !!!)
ext_0_idx = -1;
ext_0_objVec = x_objVec;
ext_1_idx = -1;
ext_1_objVec = x_objVec;
for (unsigned int k=0; k<_pop.size(); k++)
{
// ext_0
if (_pop[k].objectiveVector()[0] < ext_0_objVec[0])
{
ext_0_idx = k;
ext_0_objVec = _pop[k].objectiveVector();
}
else if ( (_pop[k].objectiveVector()[0] == ext_0_objVec[0]) && (_pop[k].objectiveVector()[1] < ext_0_objVec[1]) )
{
ext_0_idx = k;
ext_0_objVec = _pop[k].objectiveVector();
}
// ext_1
else if (_pop[k].objectiveVector()[1] < ext_1_objVec[1])
{
ext_1_idx = k;
ext_1_objVec = _pop[k].objectiveVector();
}
else if ( (_pop[k].objectiveVector()[1] == ext_1_objVec[1]) && (_pop[k].objectiveVector()[0] < ext_1_objVec[0]) )
{
ext_1_idx = k;
ext_1_objVec = _pop[k].objectiveVector();
}
}
// worst init
if (ext_0_idx == -1)
{
unsigned int ind = 0;
while (ind == ext_1_idx)
{
ind++;
}
worst_idx = ind;
worst_objVec = _pop[ind].objectiveVector();
worst_fitness = _pop[ind].fitness();
}
else if (ext_1_idx == -1)
{
unsigned int ind = 0;
while (ind == ext_0_idx)
{
ind++;
}
worst_idx = ind;
worst_objVec = _pop[ind].objectiveVector();
worst_fitness = _pop[ind].fitness();
}
else
{
worst_idx = -1;
worst_objVec = x_objVec;
worst_fitness = x_fitness;
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////////////////////////////////
// who is the worst ?
for (unsigned int j=0; j<_pop.size(); j++)
{
if ( (j!=ext_0_idx) && (j!=ext_1_idx) )
{
if (_pop[j].fitness() < worst_fitness)
{
worst_idx = j;
worst_objVec = _pop[j].objectiveVector();
worst_fitness = _pop[j].fitness();
}
}
}
// if the worst solution is the new one
if (worst_idx == -1)
{
// if all its neighbours have been explored,
// let's explore the neighborhoud of the next individual
if (! nextMove(move, _pop[i]))
{
i++;
if (i<_pop.size())
{
// initilization of the move for the next individual
moveInit(move, _pop[i]);
}
}
}
// if the worst solution is located before _pop[i]
else if (worst_idx <= i)
{
// the new solution takes place insteed of _pop[worst_idx]
_pop[worst_idx] = _pop[i];
move(_pop[worst_idx]);
_pop[worst_idx].objectiveVector(x_objVec);
_pop[worst_idx].fitness(x_fitness);
// let's explore the neighborhoud of the next individual
i++;
if (i<_pop.size())
{
// initilization of the move for the next individual
moveInit(move, _pop[i]);
}
}
// if the worst solution is located after _pop[i]
else if (worst_idx > i)
{
// the new solution takes place insteed of _pop[i+1] and _pop[worst_idx] is deleted
_pop[worst_idx] = _pop[i+1];
_pop[i+1] = _pop[i];
move(_pop[i+1]);
_pop[i+1].objectiveVector(x_objVec);
_pop[i+1].fitness(x_fitness);
// let's explore the neighborhoud of the individual _pop[i+2]
i += 2;
if (i<_pop.size())
{
// initilization of the move for the next individual
moveInit(move, _pop[i]);
}
}
// update fitness values
fitnessAssignment.updateByDeleting(_pop, worst_objVec);
}
}
};
#endif /*MOEOIBMOLS_H_*/

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tags/paradiseo-ix86-1.0-beta/paradiseo-moeo/src/algo/moeoIteratedIBMOLS.h
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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoIteratedIBMOLS.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOITERATEDIBMOLS_H_
#define MOEOITERATEDIBMOLS_H_
#include <eoContinue.h>
#include <eoEvalFunc.h>
#include <eoOp.h>
#include <eoPop.h>
#include <utils/rnd_generators.h>
#include <moMove.h>
#include <moMoveInit.h>
#include <moNextMove.h>
#include <algo/moeoIBMOLS.h>
#include <algo/moeoLS.h>
#include <archive/moeoArchive.h>
#include <fitness/moeoIndicatorBasedFitnessAssignment.h>
#include <move/moeoMoveIncrEval.h>
//#include <rsCrossQuad.h>
/**
* Iterated version of IBMOLS as described in
* Basseur M., Burke K. : "Indicator-Based Multi-Objective Local Search" (2007).
*/
template < class MOEOT, class Move >
class moeoIteratedIBMOLS : public moeoLS < MOEOT, eoPop < MOEOT > & >
{
public:
/** The type of objective vector */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/**
* Ctor.
* @param _moveInit the move initializer
* @param _nextMove the neighborhood explorer
* @param _eval the full evaluation
* @param _moveIncrEval the incremental evaluation
* @param _fitnessAssignment the fitness assignment strategy
* @param _continuator the stopping criteria
* @param _monOp the monary operator
* @param _randomMonOp the random monary operator (or random initializer)
* @param _nNoiseIterations the number of iterations to apply the random noise
*/
moeoIteratedIBMOLS(
moMoveInit < Move > & _moveInit,
moNextMove < Move > & _nextMove,
eoEvalFunc < MOEOT > & _eval,
moeoMoveIncrEval < Move > & _moveIncrEval,
moeoIndicatorBasedFitnessAssignment < MOEOT > & _fitnessAssignment,
eoContinue < MOEOT > & _continuator,
eoMonOp < MOEOT > & _monOp,
eoMonOp < MOEOT > & _randomMonOp,
unsigned int _nNoiseIterations=1
) :
ibmols(_moveInit, _nextMove, _eval, _moveIncrEval, _fitnessAssignment, _continuator),
eval(_eval),
continuator(_continuator),
monOp(_monOp),
randomMonOp(_randomMonOp),
nNoiseIterations(_nNoiseIterations)
{}
/**
* Apply the local search iteratively until the stopping criteria is met.
* @param _pop the initial population
* @param _arch the (updated) archive
*/
void operator() (eoPop < MOEOT > & _pop, moeoArchive < MOEOT > & _arch)
{
_arch.update(_pop);
ibmols(_pop, _arch);
while (continuator(_arch))
{
// generate new solutions from the archive
generateNewSolutions(_pop, _arch);
// apply the local search (the global archive is updated in the sub-function)
ibmols(_pop, _arch);
}
}
private:
/** the local search to iterate */
moeoIBMOLS < MOEOT, Move > ibmols;
/** the full evaluation */
eoEvalFunc < MOEOT > & eval;
/** the stopping criteria */
eoContinue < MOEOT > & continuator;
/** the monary operator */
eoMonOp < MOEOT > & monOp;
/** the random monary operator (or random initializer) */
eoMonOp < MOEOT > & randomMonOp;
/** the number of iterations to apply the random noise */
unsigned int nNoiseIterations;
/**
* Creates new population randomly initialized and/or initialized from the archive _arch.
* @param _pop the output population
* @param _arch the archive
*/
void generateNewSolutions(eoPop < MOEOT > & _pop, const moeoArchive < MOEOT > & _arch)
{
// shuffle vector for the random selection of individuals
vector<unsigned int> shuffle;
shuffle.resize(std::max(_pop.size(), _arch.size()));
// init shuffle
for (unsigned int i=0; i<shuffle.size(); i++)
{
shuffle[i] = i;
}
// randomize shuffle
UF_random_generator <unsigned int> gen;
std::random_shuffle(shuffle.begin(), shuffle.end(), gen);
// start the creation of new solutions
for (unsigned int i=0; i<_pop.size(); i++)
{
if (shuffle[i] < _arch.size())
// the given archive contains the individual i
{
// add it to the resulting pop
_pop[i] = _arch[shuffle[i]];
// then, apply the operator nIterationsNoise times
for (unsigned int j=0; j<nNoiseIterations; j++)
{
monOp(_pop[i]);
}
}
else
// a randomly generated solution needs to be added
{
// random initialization
randomMonOp(_pop[i]);
}
// evaluation of the new individual
_pop[i].invalidate();
eval(_pop[i]);
}
}
///////////////////////////////////////////////////////////////////////////////////////////////////////
// A DEVELOPPER RAPIDEMENT POUR TESTER AVEC CROSSOVER //
/*
void generateNewSolutions2(eoPop < MOEOT > & _pop, const moeoArchive < MOEOT > & _arch)
{
// here, we must have a QuadOp !
//eoQuadOp < MOEOT > quadOp;
rsCrossQuad quadOp;
// shuffle vector for the random selection of individuals
vector<unsigned int> shuffle;
shuffle.resize(_arch.size());
// init shuffle
for (unsigned int i=0; i<shuffle.size(); i++)
{
shuffle[i] = i;
}
// randomize shuffle
UF_random_generator <unsigned int int> gen;
std::random_shuffle(shuffle.begin(), shuffle.end(), gen);
// start the creation of new solutions
unsigned int i=0;
while ((i<_pop.size()-1) && (i<_arch.size()-1))
{
_pop[i] = _arch[shuffle[i]];
_pop[i+1] = _arch[shuffle[i+1]];
// then, apply the operator nIterationsNoise times
for (unsigned int j=0; j<nNoiseIterations; j++)
{
quadOp(_pop[i], _pop[i+1]);
}
eval(_pop[i]);
eval(_pop[i+1]);
i=i+2;
}
// do we have to add some random solutions ?
while (i<_pop.size())
{
randomMonOp(_pop[i]);
eval(_pop[i]);
i++;
}
}
*/
///////////////////////////////////////////////////////////////////////////////////////////////////////
};
#endif /*MOEOITERATEDIBMOLS_H_*/

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tags/paradiseo-ix86-1.0-beta/paradiseo-moeo/src/do/make_ls_moeo.h
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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// make_ls_moeo.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MAKE_LS_MOEO_H_
#define MAKE_LS_MOEO_H_
#include <eoContinue.h>
#include <eoEvalFunc.h>
#include <eoGenOp.h>
#include <utils/eoParser.h>
#include <utils/eoState.h>
#include <algo/moeoIBMOLS.h>
#include <algo/moeoIteratedIBMOLS.h>
#include <algo/moeoLS.h>
#include <archive/moeoArchive.h>
#include <fitness/moeoIndicatorBasedFitnessAssignment.h>
#include <metric/moeoNormalizedSolutionVsSolutionBinaryMetric.h>
#include <move/moeoMoveIncrEval.h>
/**
* This functions allows to build a moeoLS from the parser
* @param _parser the parser
* @param _state to store allocated objects
* @param _eval the funtions evaluator
* @param _moveIncrEval the incremental evaluation
* @param _continue the stopping crietria
* @param _op the variation operators
* @param _opInit the initilization operator
* @param _moveInit the move initializer
* @param _nextMove the move incrementor
* @param _archive the archive of non-dominated solutions
*/
template < class MOEOT, class Move >
moeoLS < MOEOT, eoPop<MOEOT> & > & do_make_ls_moeo (
eoParser & _parser,
eoState & _state,
eoEvalFunc < MOEOT > & _eval,
moeoMoveIncrEval < Move > & _moveIncrEval,
eoContinue < MOEOT > & _continue,
eoMonOp < MOEOT > & _op,
eoMonOp < MOEOT > & _opInit,
moMoveInit < Move > & _moveInit,
moNextMove < Move > & _nextMove,
moeoArchive < MOEOT > & _archive
)
{
/* the objective vector type */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/* the fitness assignment strategy */
std::string & fitnessParam = _parser.getORcreateParam(std::string("IndicatorBased"), "fitness",
"Fitness assignment strategy parameter: IndicatorBased...", 'F',
"Evolution Engine").value();
std::string & indicatorParam = _parser.getORcreateParam(std::string("Epsilon"), "indicator",
"Binary indicator to use with the IndicatorBased assignment: Epsilon, Hypervolume", 'i',
"Evolution Engine").value();
double rho = _parser.getORcreateParam(1.1, "rho", "reference point for the hypervolume indicator",
'r', "Evolution Engine").value();
double kappa = _parser.getORcreateParam(0.05, "kappa", "Scaling factor kappa for IndicatorBased",
'k', "Evolution Engine").value();
moeoIndicatorBasedFitnessAssignment < MOEOT > * fitnessAssignment;
if (fitnessParam == std::string("IndicatorBased"))
{
// metric
moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > *metric;
if (indicatorParam == std::string("Epsilon"))
{
metric = new moeoAdditiveEpsilonBinaryMetric < ObjectiveVector >;
}
else if (indicatorParam == std::string("Hypervolume"))
{
metric = new moeoHypervolumeBinaryMetric < ObjectiveVector > (rho);
}
else
{
std::string stmp = std::string("Invalid binary quality indicator: ") + indicatorParam;
throw std::runtime_error(stmp.c_str());
}
fitnessAssignment = new moeoIndicatorBasedFitnessAssignment < MOEOT> (*metric, kappa);
}
else
{
std::string stmp = std::string("Invalid fitness assignment strategy: ") + fitnessParam;
throw std::runtime_error(stmp.c_str());
}
_state.storeFunctor(fitnessAssignment);
// number of iterations
unsigned int n = _parser.getORcreateParam(1, "n", "Number of iterations for population Initialization", 'n', "Evolution Engine").value();
// LS
std::string & lsParam = _parser.getORcreateParam(std::string("I-IBMOLS"), "ls",
"Local Search: IBMOLS, I-IBMOLS (Iterated-IBMOLS)...", 'L',
"Evolution Engine").value();
moeoLS < MOEOT, eoPop<MOEOT> & > * ls;
if (lsParam == std::string("IBMOLS"))
{
ls = new moeoIBMOLS < MOEOT, Move > (_moveInit, _nextMove, _eval, _moveIncrEval, *fitnessAssignment, _continue);;
}
else if (lsParam == std::string("I-IBMOLS"))
{
ls = new moeoIteratedIBMOLS < MOEOT, Move > (_moveInit, _nextMove, _eval, _moveIncrEval, *fitnessAssignment, _continue, _op, _opInit, n);
}
else
{
std::string stmp = std::string("Invalid fitness assignment strategy: ") + fitnessParam;
throw std::runtime_error(stmp.c_str());
}
_state.storeFunctor(ls);
// that's it !
return *ls;
}
#endif /*MAKE_LS_MOEO_H_*/

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tags/paradiseo-ix86-1.0-beta/paradiseo-moeo/src/fitness/moeoReferencePointIndicatorBasedFitnessAssignment.h
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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoReferencePointIndicatorBasedFitnessAssignment.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOREFERENCEPOINTINDICATORBASEDFITNESSASSIGNMENT_H_
#define MOEOREFERENCEPOINTINDICATORBASEDFITNESSASSIGNMENT_H_
#include <math.h>
#include <eoPop.h>
#include <fitness/moeoFitnessAssignment.h>
#include <metric/moeoNormalizedSolutionVsSolutionBinaryMetric.h>
/**
* Fitness assignment sheme based a Reference Point and a Quality Indicator.
*/
template < class MOEOT >
class moeoReferencePointIndicatorBasedFitnessAssignment : public moeoFitnessAssignment < MOEOT >
{
public:
/** The type of objective vector */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/**
* Ctor
* @param _refPoint the reference point
* @param _metric the quality indicator
*/
moeoReferencePointIndicatorBasedFitnessAssignment (ObjectiveVector & _refPoint, moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & _metric) :
refPoint(_refPoint), metric(_metric)
{}
/**
* Sets the fitness values for every solution contained in the population _pop
* @param _pop the population
*/
void operator()(eoPop < MOEOT > & _pop)
{
// 1 - setting of the bounds
setup(_pop);
// 2 - setting fitnesses
setFitnesses(_pop);
}
/**
* Updates the fitness 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
*/
void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
{
// nothing to do ;-)
}
protected:
/** the reference point */
ObjectiveVector & refPoint;
/** the quality indicator */
moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & metric;
/**
* Sets the bounds for every objective using the min and the max value for every objective vector of _pop (and the reference point)
* @param _pop the population
*/
void setup(const eoPop < MOEOT > & _pop)
{
double min, max;
for (unsigned int i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
{
min = refPoint[i];
max = refPoint[i];
for (unsigned int j=0; j<_pop.size(); j++)
{
min = std::min(min, _pop[j].objectiveVector()[i]);
max = std::max(max, _pop[j].objectiveVector()[i]);
}
// setting of the bounds for the objective i
metric.setup(min, max, i);
}
}
/**
* Sets the fitness of every individual contained in the population _pop
* @param _pop the population
*/
void setFitnesses(eoPop < MOEOT > & _pop)
{
for (unsigned int i=0; i<_pop.size(); i++)
{
_pop[i].fitness(- metric(_pop[i].objectiveVector(), refPoint) );
}
}
};
#endif /*MOEOREFERENCEPOINTINDICATORBASEDFITNESSASSIGNMENT_H_*/

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tags/paradiseo-ix86-1.0-beta/paradiseo-moeo/src/move/moeoMoveIncrEval.h
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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
#ifndef _MOEOMOVEINCREVAL_H
#define _MOEOMOVEINCREVAL_H
#include <eoFunctor.h>
template < class Move >
class moeoMoveIncrEval : public eoBF < const Move &, const typename Move::EOType &, typename Move::EOType::ObjectiveVector > {};
#endif