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Copy pathoptimizer_random.h
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131 lines (110 loc) · 3.43 KB
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typedef double (*FitnessFunction)( double * xs );
class OptimizerRandom{
public:
int n = 0;
int nfail = 0;
double * xBest = NULL;
double * xNew = NULL;
double * dxMax = NULL;
double bestFitness = 0.0f;
FitnessFunction fitnessFunction;
virtual void restart(){
bestFitness = fitnessFunction( xBest );
nfail = 0;
}
OptimizerRandom(int n_, double* xBest_, double* dxMax_, FitnessFunction fitnessFunction_ ){
n = n_;
dxMax = dxMax_;
xBest = new double [n]; for(int i=0; i<n; i++){ xBest[i] = xBest_[i]; }
xNew = new double [n];
fitnessFunction = fitnessFunction_;
restart();
};
virtual double step(){
for(int i=0; i<n; i++){ xNew[i] = xBest[i] + dxMax[i] * ( 2.0*randf() - 1.0 ); }
double dfitness = fitnessFunction( xNew ) - bestFitness;
if( dfitness > 0 ){
for(int i=0; i<n; i++){ xBest[i] = xNew[i]; }
bestFitness += dfitness;
nfail=0;
}else{
nfail++;
}
return dfitness;
};
};
class OptimizerRandom_2 : public OptimizerRandom {
public:
double decay = 0.0d;
double c_moment = 0.0d;
double * momentum = NULL;
OptimizerRandom_2 (int n_, double* xBest_, double* dxMax_, double decay_, double c_moment_, FitnessFunction fitnessFunction_ )
: OptimizerRandom ( n_, xBest_, dxMax_, fitnessFunction_ ) {
decay = decay_;
c_moment = c_moment_;
momentum = new double [n]; for(int i=0; i<n; i++){ momentum[i] = 0; }
};
void cleanMomentum(){ for(int i=0; i<n; i++){ momentum[i]=0; } }
virtual void restart(){
OptimizerRandom::restart();
cleanMomentum();
}
virtual double step(){
//for(int i=0; i<n; i++){ xNew[i] = xBest[i] + dxMax[i] * ( 2.0*randf() - 1.0 ) + momentum[i]; }
for(int i=0; i<n; i++){ xNew[i] = xBest[i] + dxMax[i] * ( 2.0*randf() - 1.0 ) + momentum[i]*randf(); }
double dfitness = fitnessFunction( xNew ) - bestFitness;
if( dfitness > 0 ){
for(int i=0; i<n; i++){
momentum[i] += c_moment * ( xNew[i] - xBest[i] );
xBest[i] = xNew[i];
}
bestFitness += dfitness;
} else {
nfail++;
for(int i=0; i<n; i++){ momentum[i] *= decay; }
}
return dfitness;
};
};
class OptimizerRandom_3 : public OptimizerRandom {
public:
double decay = 0.0d;
double c_moment = 0.0d;
double * xBestNew = NULL;
double bestNewFitness;
OptimizerRandom_3 (int n_, double* xBest_, double* dxMax_, double decay_, double c_moment_, FitnessFunction fitnessFunction_ )
: OptimizerRandom ( n_, xBest_, dxMax_, fitnessFunction_ ) {
decay = decay_;
c_moment = c_moment_;
xBestNew = new double [n]; for(int i=0; i<n; i++){ xBestNew[i] = xBest[i]; }
};
void success_step(){
for(int i=0; i<n; i++){
xBestNew[i] = xNew[i] + c_moment * ( xNew[i] - xBest[i] );
xBest[i] = xNew[i];
}
bestNewFitness = fitnessFunction( xNew );
if( bestNewFitness > bestFitness ){
bestFitness = bestNewFitness;
success_step();
}
}
virtual double step(){
for(int i=0; i<n; i++){ xNew[i] = xBestNew[i] + dxMax[i] * ( 2.0*randf() - 1.0 ); }
double fitness = fitnessFunction( xNew );
double dfitness = fitness - bestFitness;
if( dfitness > 0 ){
bestFitness = fitness;
success_step();
} else {
nfail++;
if( fitness > bestNewFitness ){
bestNewFitness = fitness;
for(int i=0; i<n; i++){ xBestNew[i] = xNew[i]; }
}else{
for(int i=0; i<n; i++){ xBestNew[i] = decay*xBestNew[i] + (1.0d-decay)*xBest[i]; }
}
}
return dfitness;
};
};