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deopt.h
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deopt.h
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//$ nocpp
/**
* @file deopt.h
*
* @version 2024.5
*
* @brief The inclusion file for the CDEOpt class.
*
* @section license License
*
* Copyright (c) 2021-2024 Aleksey Vaneev
*
* Permission is hereby granted, free of charge, to any person obtaining a
* copy of this software and associated documentation files (the "Software"),
* to deal in the Software without restriction, including without limitation
* the rights to use, copy, modify, merge, publish, distribute, sublicense,
* and/or sell copies of the Software, and to permit persons to whom the
* Software is furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
* DEALINGS IN THE SOFTWARE.
*/
#ifndef DEOPT_INCLUDED
#define DEOPT_INCLUDED
#include "biteaux.h"
/**
* Differential Evolution-alike DFO solver.
*
* Description is available at https://github.com/avaneev/biteopt
*/
class CDEOpt : public CBiteOptBase< int64_t >
{
public:
typedef int64_t ptype; ///< Parameter value storage type (should be a
///< signed integer type, same as CBiteOptBase template parameter).
/**
* Function updates dimensionality of *this object.
*
* @param aParamCount The number of parameters being optimized.
* @param PopSize0 The number of elements in population to use. If set to
* 0 or negative, the default formula will be used.
*/
void updateDims( const int aParamCount, const int PopSize0 = 0 )
{
const int aPopSize = ( PopSize0 > 0 ? PopSize0 : 30 * aParamCount );
if( aParamCount == ParamCount && aPopSize == PopSize )
{
return;
}
initBuffers( aParamCount, aPopSize );
}
/**
* Function initializes *this optimizer.
*
* @param rnd Random number generator.
* @param InitParams If not NULL, initial parameter vector, also used as
* centroid.
* @param InitRadius Initial radius, multiplier relative to the default
* sigma value.
*/
void init( CBiteRnd& rnd, const double* const InitParams = NULL,
const double InitRadius = 1.0 )
{
initCommonVars( rnd );
StartSD = 0x1p-4 * InitRadius;
if( InitParams != NULL )
{
int i;
for( i = 0; i < ParamCount; i++ )
{
StartParams[ i ] = (ptype) (( InitParams[ i ] -
MinValues[ i ]) / DiffValues[ i ]);
}
UseStartParams = true;
}
}
/**
* Function performs the parameter optimization iteration that involves 1
* objective function evaluation.
*
* @param rnd Random number generator.
* @return The number of non-improving iterations so far.
*/
int optimize( CBiteRnd& rnd )
{
int i;
if( DoInitEvals )
{
ptype* const Params = getCurParams();
genInitParams( rnd, Params );
const double NewCost = fixCostNaN( optcost( NewValues ));
NewCosts[ 0 ] = NewCost;
updateBestCost( NewCost, NewValues, updatePop( NewCost, Params ));
if( CurPopPos == PopSize )
{
DoInitEvals = false;
}
return( 0 );
}
const int si1 = rnd.getPowInt( 4.0, CurPopSize / 2 );
const ptype* const rp1 = getParamsOrdered( si1 );
const int PairCount = 3;
const int pc = 1 + 2 * PairCount;
int PopIdx[ pc ];
PopIdx[ 0 ] = si1;
int pp = 1;
int j;
if( CurPopSize1 <= pc )
{
while( pp < pc )
{
PopIdx[ pp ] = rnd.getInt( CurPopSize );
pp++;
}
}
else
{
while( pp < pc )
{
const int sii = rnd.getInt( CurPopSize );
for( j = 0; j < pp; j++ )
{
if( PopIdx[ j ] == sii )
{
break;
}
}
if( j >= pp )
{
PopIdx[ pp ] = sii;
pp++;
}
}
}
const ptype* const rp2 = getParamsOrdered( PopIdx[ 1 ]);
const ptype* const rp3 = getParamsOrdered( PopIdx[ 2 ]);
const ptype* const rp4 = getParamsOrdered( PopIdx[ 3 ]);
const ptype* const rp5 = getParamsOrdered( PopIdx[ 4 ]);
const ptype* const rp6 = getParamsOrdered( PopIdx[ 5 ]);
const ptype* const rp7 = getParamsOrdered( PopIdx[ 6 ]);
for( i = 0; i < ParamCount; i++ )
{
TmpParams[ i ] = ( rp2[ i ] - rp3[ i ]) + ( rp4[ i ] - rp5[ i ]) +
( rp6[ i ] - rp7[ i ]);
}
// TPDF bit randomization.
if( rnd.getBit() && rnd.getBit() )
{
const int k = rnd.getInt( ParamCount );
// Produce sparsely-random bit-strings.
const ptype v1 = rnd.getRaw() & rnd.getRaw() & rnd.getRaw() &
rnd.getRaw() & rnd.getRaw() & IntMantMask;
const ptype v2 = rnd.getRaw() & rnd.getRaw() & rnd.getRaw() &
rnd.getRaw() & rnd.getRaw() & IntMantMask;
TmpParams[ k ] += v1 - v2; // Apply in TPDF manner.
}
if( rnd.getBit() )
{
int si2 = si1 + rnd.getBit() * 2 - 1;
if( si2 < 0 )
{
si2 = 1;
}
const ptype* const rp1b = getParamsOrdered( si2 );
for( i = 0; i < ParamCount; i++ )
{
TmpParams[ i ] = (( rp1[ i ] + rp1b[ i ]) >> 1 ) +
( TmpParams[ i ] >> 2 );
}
}
else
{
for( i = 0; i < ParamCount; i++ )
{
TmpParams[ i ] = rp1[ i ] + ( TmpParams[ i ] >> 2 );
}
}
for( i = 0; i < ParamCount; i++ )
{
TmpParams[ i ] = wrapParam( rnd, TmpParams[ i ]);
NewValues[ i ] = getRealValue( TmpParams, i );
}
const double NewCost = fixCostNaN( optcost( NewValues ));
NewCosts[ 0 ] = NewCost;
const int p = updatePop( NewCost, TmpParams );
if( p < CurPopSize )
{
updateBestCost( NewCost, NewValues, p );
StallCount = 0;
}
else
{
StallCount++;
}
return( StallCount );
}
};
#endif // DEOPT_INCLUDED