nojhan/paradiseo
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity

Cleaning, doc & fixes for trikiSA

Browse source
This commit is contained in:
LPTK 2013-07-19 13:38:20 +02:00
commit 7ae7468428
1 changed files with 419 additions and 332 deletions
Download patch file Download diff file Expand all files Collapse all files

705
mo/src/coolingSchedule/moTrikiCoolingSchedule.h
View file

@ -1,363 +1,450 @@
/* /*
<moTrikiCoolingSchedule.h>
Copyright (C) DOLPHIN Project-Team, INRIA Futurs, 2006-2008
(C) OPAC Team, LIFL, 2002-2008
Sébastien Cahon, Jean-Charles Boisson (Jean-Charles.Boisson@lifl.fr) (c) Thales group, 2010
This software is governed by the CeCILL license under French law and This library is free software; you can redistribute it and/or
abiding by the rules of distribution of free software. You can use, modify it under the terms of the GNU Lesser General Public
modify and/ or redistribute the software under the terms of the CeCILL License as published by the Free Software Foundation;
license as circulated by CEA, CNRS and INRIA at the following URL version 2 of the License.
"http://www.cecill.info".
As a counterpart to the access to the source code and rights to copy, This library is distributed in the hope that it will be useful,
modify and redistribute granted by the license, users are provided only but WITHOUT ANY WARRANTY; without even the implied warranty of
with a limited warranty and the software's author, the holder of the MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
economic rights, and the successive licensors have only limited liability. Lesser General Public License for more details.
In this respect, the user's attention is drawn to the risks associated You should have received a copy of the GNU Lesser General Public
with loading, using, modifying and/or developing or reproducing the License along with this library; if not, write to the Free Software
software by the user in light of its specific status of free software, Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
that may mean that it is complicated to manipulate, and that also
therefore means that it is reserved for developers and experienced
professionals having in-depth computer knowledge. Users are therefore
encouraged to load and test the software's suitability as regards their
requirements in conditions enabling the security of their systems and/or
data to be ensured and, more generally, to use and operate it in the
same conditions as regards security.
The fact that you are presently reading this means that you have had
knowledge of the CeCILL license and that you accept its terms.
ParadisEO WebSite : http://paradiseo.gforge.inria.fr Contact: http://eodev.sourceforge.net
Contact: paradiseo-help@lists.gforge.inria.fr
*/ Authors:
Lionel Parreaux <lionel.parreaux@gmail.com>
*/
#ifndef _moTrikiCoolingSchedule_h #ifndef _moTrikiCoolingSchedule_h
#define _moTrikiCoolingSchedule_h #define _moTrikiCoolingSchedule_h
#include <coolingSchedule/moCoolingSchedule.h> #include <coolingSchedule/moCoolingSchedule.h>
#include <neighborhood/moNeighborhood.h>
#include <continuator/moNeighborhoodStat.h> #include <continuator/moNeighborhoodStat.h>
#include <continuator/moStdFitnessNeighborStat.h> #include <continuator/moStdFitnessNeighborStat.h>
#include <neighborhood/moNeighborhood.h>
#include <continuator/moStat.h> #include <continuator/moStat.h>
#include <moFitnessMomentsStat.h> #include <continuator/moFitnessMomentsStat.h>
/**
/* * Cooling Schedule, adapted from E.Triki, Y.Collette, P.Siarry (2004)
#include <continuator/moStat.h> * This CS is based on an initial estimation of the standard deviation
* and an expected decrease in cost between each Markov chain,
#include <explorer/moNeighborhoodExplorer.h> * possibly re-estimating the prameters
#include <comparator/moNeighborComparator.h> *
#include <comparator/moSolNeighborComparator.h> *
#include <neighborhood/moNeighborhood.h> * A detailed explanation follows:
*
*
* Initialization
*
* A random walk of n steps should be performed to estimate the std dev
* of the fitness. The init temp is set to this value.
*
*
* Algorithm
*
* The CS is based on Markov chains, during which the temp is constant.
* A Markov chain ends when a given number of solutions 'max_accepted'
* has been reached, or when a given number 'max_generated' of solutions
* have been generated.
*
* After each chain, the average cost of the solutions of the chain is
* expected to have decreased of delta (compared to the previous chain)
* with delta initialized to = stddev/mu2.
* If it's the case (ie: avgCost/(prevAvgCost-delta) < xi, where xi == 1+epsilon)
* then we say we're at equilibrium and we apply a normal temperature
* decrease, of ratio
* alpha = 1-_temp*delta/variance
*
* Negative temperatures (when alpha < 0) happen when the initial std
* dev was misestimated or, according to the article, when "the minimum
* cost is greater than the current average cost minus delta"(sic)(??).
* In case of a neg temp, we increment a counter 'negative_temp' and we
* reinitialize the algorithm until the temperature is no more negative,
* unless the counter of neg temp reaches a constant K2, in which case
* the behavior has been chosen to be this of a greedy algorithm (SA
* with nil temperature).
*
* Reinitializing the algo consists in:
* - setting the temperature "decrease" factor alpha to a
* constant lambda1 > 1 in order to in fact increase it
* - setting delta to sigma/mu1 (sigma being the std dev of the
* current Markov chain)
*
* Note that when not reinitializing, the expected decrease in cost
* 'delta' is never supposed to change.
*
* If the eq is not reached after the current chain, we increment a
* counter 'equilibrium_not_reached', and when it reaches K1 we
* reinitialize the algorithm and reset the counter to 0 (resetting
* to 0 was an added behavior and not part of the article; but without
* it the algo got trapped).
*
*
* Termination
*
* Currently, the algo terminates when the current average cost stops
* changing for 'theta' chains, or when the current std dev becomes
* null (added behavior; indeed, when the std dev is null it is no more
* possible to compute alpha), or when there is no accepted solution
* for the current "chain" (added, cf in this case we can't compute a
* std dev or an average).
* In practice, the algorithm never seems to terminate by "freezing"
* (first case), obviously because we need an implementation of
* approximate double comparison instead of exact comparison.
*
*
*/ */
template< class EOT >
#include <iostream>
using namespace std;
//!
/*!
*/
template< class EOT, class Neighbor > //, class Neighborhood >
class moTrikiCoolingSchedule: public moCoolingSchedule< EOT > class moTrikiCoolingSchedule: public moCoolingSchedule< EOT >
{ {
public: public:
//typedef typename Neighbor::EOT EOT ;
typedef moNeighborhood<Neighbor> Neighborhood ;
//! Constructor /**
/*! * Constructor for the cooling schedule
*/ * @param _initTemp the temperature at which the CS begins; a recommended value is _stdDevEstimation
* @param _stdDevEstimation an estimation of the standard deviation of the fitness. Typically, a random walk of n steps is performed to estimate the std dev of the fitness
* @param _max_accepted maximum number of solutions to accept before ending the Markov chain and reducing the temperature; depends on the pb/neighborhood
* @param _max_generated maximum number of solutions to generate before ending the Markov chain and reducing the temperature; depends on the pb/neighborhood
* @param _mu2 target decrease in cost factor, mu2 typically belongs to [1; 20]
* @param _mu1 target decrease in cost factor when reinitializing, in [2; 20]
* @param _lambda1 the increase in temperature (reheating factor), typically in [1.5; 4]
* @param _lambda2 lambda2 in [0.5; 0.99]
* @param _xi typically belongs to [1; 1.1]
* @param _theta typically set to 10
* @param _K1 in [1; 4], the number of chains without reaching equilibrium before we raise the temperature
* @param _K2 maximul number of consecutive negative temperatures before switching to a greedy algorithm
*/
moTrikiCoolingSchedule (
double _initTemp,
double _stdDevEstimation,
int _max_accepted = 50,
int _max_generated = 100,
double _mu2 = 2.5,
double _mu1 = 10,
double _lambda1 = 2,
double _lambda2 = .7,
double _xi = 1.05,
int _theta = 10,
int _K1 = 10,
int _K2 = 5
)
: initTemp(_initTemp),
initStdDev(_stdDevEstimation),
mu2(_mu2),
K1(_K1),
K2(_K2),
lambda1(_lambda1),
lambda2(_lambda2),
mu1(_mu1),
xi(_xi),
max_accepted(_max_accepted),
max_generated(_max_generated),
theta(_theta),
statIsInitialized(false)
{
chainStat.temperature = initTemp;
}
moTrikiCoolingSchedule (Neighborhood& _neighborhood, moEval<Neighbor>& _eval, double _initTemp) /**
: initTemp(_initTemp), * Initialization
mu2(10), // mu2 typically belongs to [1; 20] * @param _solution initial solution
K1(2), // K1 in [1; 4], the number of chains without reaching equilibrium before we raise the temperature */
K2(5), // ??? double init(EOT & _solution) {
lambda1(2), // the increase in temperature, typically in [1.5; 4]
lambda2(.7), // lambda2 in [0.5; 0.99]
mu1(10), // target decrease in cost factor, in [2; 20]
xi(1.05), // xi typically belongs to [1; 1.1]
max_accepted(50), // depends on pb/neighborhood
max_generated(100), // depends on pb/neighborhood
theta(10), // theta is typically set to 10
statIsInitialized(false),
outf("out.data")
{ }
moTrikiCoolingSchedule ( chainStat.temperature = initTemp;
Neighborhood& _neighborhood, moEval<Neighbor>& _eval, double _initTemp,
double _max_accepted,
double _max_generated
)
: initTemp(_initTemp),
mu2(10), // mu2 typically belongs to [1; 20]
K1(2), // K1 in [1; 4], the number of chains without reaching equilibrium before we raise the temperature
K2(5), // ???
lambda1(2), // the increase in temperature, typically in [1.5; 4]
lambda2(.7), // lambda2 in [0.5; 0.99]
mu1(10), // target decrease in cost factor, in [2; 20]
xi(1.05), // xi typically belongs to [1; 1.1]
max_accepted(_max_accepted), // depends on pb/neighborhood
max_generated(_max_generated), // depends on pb/neighborhood
theta(10), // theta is typically set to 10
statIsInitialized(false),
outf("out.data")
{ }
/** accepted = generated = 0;
* Initial temperature
* @param _solution initial solution
*/
double init(EOT & _solution) {
accepted = generated = costs_sum = 0; negative_temp = equilibrium_not_reached = frozen = 0;
negative_temp = equilibrium_not_reached = frozen = 0; chainStat.delta = initStdDev/mu2;
reinitializing = false; reinitializing = false;
terminated = false;
statIsInitialized = false;
/// return initTemp;
cout << "INIT T=" << initTemp << endl; }
///
//outf.open("out"); /**
//outf << "ok"; * update the temperature by a factor
//outf.close(); * @param _temp current temperature to update
* @param _acceptedMove true when the move is accepted, false otherwise
*/
void update(double& _temp, bool _acceptedMove, EOT & _solution) {
/*
* In the following code, things were added or modified from
* the original (incomplete) version of the algorithm
* described in [2004, Triki et al.]
* Each added/modified behavior is labelled
* with a "// ADDED!" comment.
*/
chainStat.temperature = _temp;
chainStat.stoppingReason = NULL;
chainStat.chainEndingReason = NULL;
chainStat.equilibriumNotReached = false;
chainStat.negativeTemp = false;
chainStat.generatedSolutions = generated;
chainStat.acceptedSolutions = accepted;
generated++;
if (_acceptedMove)
{
accepted++;
if (statIsInitialized)
momentStat(_solution);
else momentStat.init(_solution), statIsInitialized = true;
}
if (accepted > max_accepted || generated > max_generated) {
chainStat.chainEndingReason = accepted > max_accepted ? chainEndingReasons[0]: chainEndingReasons[1];
double avgFitness = momentStat.value().first;
double prevAvgFitness = chainStat.avgFitness;
double alpha = 0;
if (accepted == 0) // ADDED! Otherwise the computed std dev is null; we're probably at equilibrium
{
chainStat.stoppingReason = stoppingReasons[0];
// Note: we could also not stop and just become greedy (temperature set to 0)
}
else
{
double avgFitness = momentStat.value().first;
double variance = momentStat.value().second;
chainStat.stdDev = sqrt(variance);
double sigma = chainStat.stdDev;
accepted = generated = 0;
statIsInitialized = false;
if (negative_temp < K2)
{
if (!reinitializing)
{
if (avgFitness/(prevAvgFitness-chainStat.delta) > xi)
equilibrium_not_reached++, chainStat.equilibriumNotReached = true;
else equilibrium_not_reached = 0;
}
if (equilibrium_not_reached > K1)
{
reinitializing = true;
alpha = lambda1;
chainStat.delta = sigma/mu1;
equilibrium_not_reached = 0; // ADDED! Otherwise the algo gets trapped here!
}
else if (_temp*chainStat.delta/(sigma*sigma) >= 1)
{
negative_temp++;
reinitializing = true;
chainStat.negativeTemp = true;
if (negative_temp < K2)
{
alpha = lambda1;
chainStat.delta = sigma/mu1;
} else
alpha = lambda2;
}
else
{
reinitializing = false;
alpha = 1-_temp*chainStat.delta/variance;
if (sigma == 0) // ADDED! When std dev is null, the solution is probably at eq, and the algo can't go on anyways
chainStat.stoppingReason = stoppingReasons[1];
}
}
else
{ /* Note: the paper doesn't specify a value for alpha in this case.
We've chosen to let it set to 0, which means the algorithm becomes greedy. */
alpha = 0;
}
}
_temp *= alpha;
chainStat.currentFitness = _solution.fitness();
chainStat.alpha = alpha;
chainStat.avgFitness = avgFitness;
return initTemp; // TODO use a relative-epsilon comparison to approximate equality
} if (avgFitness == prevAvgFitness)
frozen++;
else frozen = 0;
/** if (frozen >= theta)
* update the temperature by a factor chainStat.stoppingReason = stoppingReasons[2];
* @param _temp current temperature to update
* @param _acceptedMove true when the move is accepted, false otherwise
*/
void update(double& _temp, bool _acceptedMove, EOT & _solution) {
//cout << _temp << " g " << generated << endl; }
generated++; }
if (_acceptedMove) /*
{ * operator() Determines if the cooling schedule shall end or continue
accepted++; * @param temperature the current temperature
//costs_sum += _solution.fitness(); */
//varStat(_solution); bool operator() (double temperature)
if (statIsInitialized) {
momStat(_solution);
else momStat.init(_solution), statIsInitialized = true;
//cout << _solution.fitness() << " avgCost=" << momStat.value().first << endl; return frozen < theta
} && !chainStat.stoppingReason ; // ADDED! because 'frozen' isn't a sufficient terminating criterion (yet?)
}
/*
* Definition of getter functions useful for monitoring the algorithm
* using an eoGetterUpdater.
*/
#define __triki_makeGetter(name, type) type name() { return chainStat.name; }
__triki_makeGetter(stdDev, double)
__triki_makeGetter(avgFitness, double)
__triki_makeGetter(temperature, double)
__triki_makeGetter(currentFitness, double)
__triki_makeGetter(alpha, double)
__triki_makeGetter(delta, double)
__triki_makeGetter(generatedSolutions, int)
__triki_makeGetter(acceptedSolutions, int)
__triki_makeGetter(equilibriumNotReached, bool)
__triki_makeGetter(negativeTemp, bool)
__triki_makeGetter(terminated, bool)
__triki_makeGetter(stoppingReason, const char *)
__triki_makeGetter(chainEndingReason, const char *)
#undef __triki_makeGetter
if (accepted > max_accepted || generated > max_generated) { const bool markovChainEnded() const
{
return chainStat.chainEndingReason != NULL;
}
if (accepted == 0) // ADDED! Otherwise the computed std dev is null; we're probably at equilibrium struct MarkovChainStats;
{ const MarkovChainStats& markovChainStats() const
/// {
cout << "Stopping: no accepted solution" << endl; return chainStat;
/// }
terminated = true; struct MarkovChainStats {
return;
}
/// MarkovChainStats()
cout << (accepted > max_accepted? "MAXACC ": "MAXGEN "); : stdDev(0), avgFitness(0), temperature(-1), currentFitness(-1), alpha(0), delta(0),
/// equilibriumNotReached(false), negativeTemp(false)
{ }
//double avgCost = costs_sum/(double)accepted; double
//double stdDev = sqrt(varStat.value()); // WARNING: IT'S NO MORE THE AVG COST, NOW IT'S THE STD DEV! stdDev,
//double variance = varStat.value(); avgFitness,
double avgCost = momStat.value().first; temperature,
double variance = momStat.value().second; currentFitness,
double stdDev = sqrt(variance); alpha,
double sigma = stdDev; delta
double delta = sigma/mu2; ;
int generatedSolutions, acceptedSolutions;
bool equilibriumNotReached, negativeTemp;
const char * stoppingReason; // if NULL, the algo has not stopped
const char * chainEndingReason; // if NULL, the chain has not ended
void print(std::ostream& os = std::cout, bool onlyWhenChainEnds = true) const
{
if (chainEndingReason != NULL || !onlyWhenChainEnds)
{
os << "T=" << temperature << " avgFitness=" << avgFitness << " stdDev=" << stdDev
<< " currentFitness=" << currentFitness << " expected decrease in cost=" << delta
<< std::endl;
if (chainEndingReason)
os << "T*=" << alpha << " chain ended, because " << chainEndingReason;
if (equilibriumNotReached)
os << " /!\\ equilibrium not reached";
if (negativeTemp)
os << " /!\\ negative temperature";
os << std::endl;
if (stoppingReason)
os << "Terminated, because " << stoppingReason << std::endl;
}
}
//outf << avgCost << endl; };
//outf << _temp << endl;
outf << prevAvgCost-delta << endl;
accepted = generated = costs_sum = 0;
//varStat.init(_solution);//TODON use next chain's first sol
//momStat.init(_solution);//TODONE use next chain's first sol
statIsInitialized = false;
///
cout << "T=" << _temp << " avgCost=" << avgCost << " stdDev=" << stdDev << " currCost=" << _solution.fitness() << endl;
///
double alpha;
double oldprevAvgCost = prevAvgCost;
///
cout << "negTemp: " << negative_temp << " / " << K2 << endl;
///
if (negative_temp < K2)
{
if (!reinitializing)
{
///
if (avgCost/(prevAvgCost-delta) > xi) cout << "/!\\ eq not reached!" << endl;
///
if (avgCost/(prevAvgCost-delta) > xi)
equilibrium_not_reached++;
else equilibrium_not_reached = 0;
}
if (equilibrium_not_reached > K1)
{
///
cout << "/!\\ Reinitializing (eq not reached)" << endl;
///
reinitializing = true;
alpha = lambda1;
delta = sigma/mu1;
equilibrium_not_reached = 0; // ADDED! Otherwise the algo gets trapped here!
}
else if (_temp*delta/(sigma*sigma) >= 1)
{
///
cout << "/!\\ neg temp!" << endl;
///
negative_temp++;
reinitializing = true;
if (negative_temp < K2)
{
alpha = lambda1;
delta = sigma/mu1;
} else
alpha = lambda2;
}
// First interpretation of the pseudocode indentation: (seems obviously false because it makes the above code unreachable)
/*
}
else
{
cout << "ccc" << endl;
reinitializing = false;
prevAvgCost = avgCost;
alpha = 1-_temp*delta/(sigma*sigma);
}
*/
// Second interpretation of the pseudocode indentation:
else
{
///
cout << "[normal decrease]" << endl;
///
reinitializing = false;
prevAvgCost = avgCost;
//alpha = 1-_temp*delta/(sigma*sigma);
alpha = 1-_temp*delta/variance;
//alpha = (sigma==0? 1: 1-_temp*delta/(sigma*sigma)); // ADDED! but removed
if (sigma == 0) // ADDED! When std dev is null, the solution is probably at eq, and the algo can't go on anyways
terminated = true, cout << "Stopping: null std dev" << endl;
}
}
// FIXME: else what? alpha=?
///
cout << "*=" << alpha << endl;
///
_temp *= alpha;
// Never seems to be used
if (avgCost == oldprevAvgCost) // use a neighborhood to approximate double equality?
frozen++;
else frozen = 0;
//exit(0);
//cin.get();
}
}
//! Function which proceeds to the cooling
/*!
*/
bool operator() (double temperature)
{
///
if (terminated) cout << "TERMINATED" << endl;
///
return frozen < theta
&& !terminated ; // ADDED! because 'frozen' doesn't terminate anything
}
private: private:
//moNeighborhoodStat<Neighbor> nhStat;
//moStdFitnessNeighborStat<Neighbor> stdDevStat;
const double
// parameters of the algorithm
//currentTemp,
initTemp,
//ratio,
//threshold,
mu2, // mu2 typically belongs to [1; 20]
K1, // K1 in [1; 4], the number of chains without reaching equilibrium before we raise the temperature
K2,
lambda1, // the increase in temperature, typically in [1.5; 4]
lambda2, // lambda2 in [0.5; 0.99]
mu1, // target decrease in cost factor, in [2; 20]
xi // xi typically belongs to [1; 1.1]
// private variables
;
double
stdDev,
prevAvgCost,
expectedDecreaseInCost, // delta
costs_sum
;
const int
max_accepted,
max_generated,
theta // theta is typically set to 10
;
int
accepted,
generated,
equilibrium_not_reached,
negative_temp,
frozen
;
bool reinitializing, terminated;
//moFitnessVarianceStat<EOT> varStat; // parameters of the algorithm
moFitnessMomentsStat<EOT> momStat;
bool statIsInitialized;
ofstream outf; const double
initTemp,
initStdDev,
mu2,
K1,
K2,
lambda1,
lambda2,
mu1,
xi
;
const int
max_accepted,
max_generated,
theta
;
// Variables of the algorithm
MarkovChainStats chainStat;
int
accepted,
generated,
equilibrium_not_reached,
negative_temp,
frozen
;
bool statIsInitialized, reinitializing;
moFitnessMomentsStat<EOT> momentStat;
// Possible reasons why the algorithm has stopped
static const char * stoppingReasons[];
// Possible reasons why the previous Markov chain has ended
static const char * chainEndingReasons[];
}; };
/*
* Definition of the static members of the class
*/
template< class Neighbor >
const char * moTrikiCoolingSchedule<Neighbor>::stoppingReasons[] = {"no accepted solution", "null std dev" , "frozen >= theta"};
template< class Neighbor >
const char * moTrikiCoolingSchedule<Neighbor>::chainEndingReasons[] = {"MAX ACCepted solutions", "MAX GENerated solutions"};
#endif #endif