ITK  4.2.0
Insight Segmentation and Registration Toolkit
Public Types | Public Member Functions | Static Public Member Functions | Protected Member Functions | Private Member Functions | Private Attributes
itk::InitializationBiasedParticleSwarmOptimizer Class Reference

#include <itkInitializationBiasedParticleSwarmOptimizer.h>

+ Inheritance diagram for itk::InitializationBiasedParticleSwarmOptimizer:
+ Collaboration diagram for itk::InitializationBiasedParticleSwarmOptimizer:

List of all members.

Public Types

typedef double CoefficientType
typedef SmartPointer< const SelfConstPointer
typedef SmartPointer< SelfPointer
typedef
InitializationBiasedParticleSwarmOptimizer 
Self
typedef ParticleSwarmOptimizerBase Superclass
- Public Types inherited from itk::ParticleSwarmOptimizerBase
typedef
CostFunctionType::MeasureType 
MeasureType
typedef unsigned int NumberOfGenerationsType
typedef unsigned int NumberOfIterationsType
typedef unsigned int NumberOfParticlesType
typedef std::vector< std::pair
< ParametersType::ValueType,
ParametersType::ValueType > > 
ParameterBoundsType
typedef
Statistics::MersenneTwisterRandomVariateGenerator 
RandomVariateGeneratorType
typedef std::vector< ParticleDataSwarmType
typedef ParametersType::ValueType ValueType
- Public Types inherited from itk::SingleValuedNonLinearOptimizer
typedef CostFunctionType::Pointer CostFunctionPointer
typedef SingleValuedCostFunction CostFunctionType
typedef
CostFunctionType::DerivativeType 
DerivativeType
typedef Superclass::ParametersType ParametersType
- Public Types inherited from itk::NonLinearOptimizer
typedef Superclass::ScalesType ScalesType
- Public Types inherited from itk::Optimizer
- Public Types inherited from itk::Object
- Public Types inherited from itk::LightObject

Public Member Functions

virtual ::itk::LightObject::Pointer CreateAnother (void) const
virtual const char * GetNameOfClass () const
virtual void SetInertiaCoefficient (CoefficientType _arg)
virtual CoefficientType GetInertiaCoefficient ()
virtual void SetPersonalCoefficient (CoefficientType _arg)
virtual CoefficientType GetPersonalCoefficient ()
virtual void SetGlobalCoefficient (CoefficientType _arg)
virtual CoefficientType GetGlobalCoefficient ()
virtual void SetInitializationCoefficient (CoefficientType _arg)
virtual CoefficientType GetInitializationCoefficient ()
- Public Member Functions inherited from itk::ParticleSwarmOptimizerBase
virtual MeasureType GetFunctionConvergenceTolerance ()
virtual bool GetInitializeNormalDistribution ()
virtual NumberOfIterationsType GetMaximalNumberOfIterations ()
virtual NumberOfGenerationsType GetNumberOfGenerationsWithMinimalImprovement ()
virtual NumberOfParticlesType GetNumberOfParticles ()
ParameterBoundsType GetParameterBounds () const
virtual ParametersType GetParametersConvergenceTolerance ()
virtual double GetPercentageParticlesConverged ()
virtual bool GetPrintSwarm ()
virtual
RandomVariateGeneratorType::IntegerType 
GetSeed ()
virtual const std::string GetStopConditionDescription () const
virtual bool GetUseSeed ()
MeasureType GetValue () const
virtual void InitializeNormalDistributionOff ()
virtual void InitializeNormalDistributionOn ()
void PrintSwarm (std::ostream &os, Indent indent) const
virtual void PrintSwarmOff ()
virtual void PrintSwarmOn ()
virtual void SetFunctionConvergenceTolerance (MeasureType _arg)
virtual void SetInitializeNormalDistribution (bool _arg)
virtual void SetMaximalNumberOfIterations (NumberOfIterationsType _arg)
virtual void SetNumberOfGenerationsWithMinimalImprovement (NumberOfGenerationsType _arg)
void SetNumberOfParticles (NumberOfParticlesType n)
void SetParametersConvergenceTolerance (ValueType convergenceTolerance, unsigned int sz)
virtual void SetParametersConvergenceTolerance (ParametersType _arg)
virtual void SetPercentageParticlesConverged (double _arg)
virtual void SetPrintSwarm (bool _arg)
virtual void SetSeed (RandomVariateGeneratorType::IntegerType _arg)
virtual void SetUseSeed (bool _arg)
void StartOptimization (void)
virtual void UseSeedOff ()
virtual void UseSeedOn ()
void SetInitialSwarm (const SwarmType &initialSwarm)
void ClearSwarm ()
virtual void SetParameterBounds (ParameterBoundsType &bounds)
void SetParameterBounds (std::pair< ParametersType::ValueType, ParametersType::ValueType > &bounds, unsigned int n)
- Public Member Functions inherited from itk::SingleValuedNonLinearOptimizer
virtual const CostFunctionTypeGetCostFunction ()
MeasureType GetValue (const ParametersType &parameters) const
virtual void SetCostFunction (CostFunctionType *costFunction)

Static Public Member Functions

static Pointer New ()

Protected Member Functions

 InitializationBiasedParticleSwarmOptimizer ()
void PrintSelf (std::ostream &os, Indent indent) const
virtual void UpdateSwarm ()
virtual ~InitializationBiasedParticleSwarmOptimizer ()
- Protected Member Functions inherited from itk::ParticleSwarmOptimizerBase
void FileInitialization ()
virtual void Initialize ()
 ParticleSwarmOptimizerBase ()
 ParticleSwarmOptimizerBase (const Self &)
void PrintParamtersType (const ParametersType &x, std::ostream &os) const
void RandomInitialization ()
virtual void ValidateSettings ()
virtual ~ParticleSwarmOptimizerBase ()
- Protected Member Functions inherited from itk::SingleValuedNonLinearOptimizer
 SingleValuedNonLinearOptimizer ()
virtual ~SingleValuedNonLinearOptimizer ()
 NonLinearOptimizer ()
virtual ~NonLinearOptimizer ()
- Protected Member Functions inherited from itk::Optimizer
 Optimizer ()
virtual void SetCurrentPosition (const ParametersType &param)
virtual ~Optimizer ()
- Protected Member Functions inherited from itk::Object
 Object ()
bool PrintObservers (std::ostream &os, Indent indent) const
virtual void SetTimeStamp (const TimeStamp &time)
virtual ~Object ()
- Protected Member Functions inherited from itk::LightObject
virtual LightObject::Pointer InternalClone () const
 LightObject ()
virtual void PrintHeader (std::ostream &os, Indent indent) const
virtual void PrintTrailer (std::ostream &os, Indent indent) const
virtual ~LightObject ()

Private Member Functions

 InitializationBiasedParticleSwarmOptimizer (const Self &)
void operator= (const Self &)

Private Attributes

ParametersType::ValueType m_GlobalCoefficient
ParametersType::ValueType m_InertiaCoefficient
ParametersType::ValueType m_InitializationCoefficient
ParametersType::ValueType m_PersonalCoefficient

Additional Inherited Members

- Protected Attributes inherited from itk::ParticleSwarmOptimizerBase
CostFunctionType::MeasureType m_FunctionBestValue
std::vector< MeasureTypem_FunctionBestValueMemory
CostFunctionType::MeasureType m_FunctionConvergenceTolerance
bool m_InitializeNormalDistribution
NumberOfIterationsType m_IterationIndex
NumberOfIterationsType m_MaximalNumberOfIterations
NumberOfGenerationsType m_NumberOfGenerationsWithMinimalImprovement
NumberOfParticlesType m_NumberOfParticles
ParameterBoundsType m_ParameterBounds
ParametersType m_ParametersBestValue
ParametersType m_ParametersConvergenceTolerance
std::vector< ParticleDatam_Particles
double m_PercentageParticlesConverged
bool m_PrintSwarm
RandomVariateGeneratorType::IntegerType m_Seed
std::ostringstream m_StopConditionDescription
bool m_UseSeed
- Protected Attributes inherited from itk::SingleValuedNonLinearOptimizer
CostFunctionPointer m_CostFunction

Detailed Description

Implementation of a biased/regularized Particle Swarm Optimization (PSO) algorithm.

This PSO algorithm was originally described in: M. P. Wachowiak, R. Smolikova, Y. Zheng, J. M. Zurada, A. S. Elmaghraby, "An approach to multimodal biomedical image registration utilizing particle swarm optimization", IEEE Trans. Evol. Comput., vol. 8(3): 289-301, 2004.

The algorithm uses a stochastic optimization approach. Optimization is performed by maintaining a swarm (flock) of particles that traverse the parameter space, searching for the optimal function value. Associated with each particle are its location and speed, in parameter space. A particle's next location is determined by its current location, its current speed, the location of the best function value it previously encountered, the location of the best function value the particles in its neighborhood previously encountered and the initial position the user specified.

The assumption is that the user's initial parameter settings are close to the minimum, which is often the case for registration. The initial parameter values are incorporated into the PSO's update rules, biasing the search in their direction. The swarms update equations are thus:

$v_i(t+1) = wv_i(t) + c_1u_1(p_i-x_i(t)) + c_2u_2(p_g-x_i(t)) + c_3u_3(x_{init} - x_i(t))$ $x_i(t+1) = clampToBounds(x_i(t) + v_i(t+1))$

where $u_i$ are $~U(0,1)$ and $w,c_1,c_2, c_3$ are user selected weights, and c_3 is linearly decreased per iteration so that it is in $c_3=initial, 0$.

Swarm initialization is performed within the user supplied parameter bounds using a uniform distribution or a normal distribution centered on the initial parameter values supplied by the user, $x_{init}$. The search terminates when the maximal number of iterations has been reached or when the change in the best value in the past $g$ generations is below a threshold and the swarm has collapsed (i.e. particles are close to each other in parameter space).

Note:
This implementation only performs minimization.

Definition at line 71 of file itkInitializationBiasedParticleSwarmOptimizer.h.


Member Typedef Documentation

Standard "Self" typedef.

Reimplemented from itk::ParticleSwarmOptimizerBase.

Definition at line 76 of file itkInitializationBiasedParticleSwarmOptimizer.h.


Constructor & Destructor Documentation

itk::InitializationBiasedParticleSwarmOptimizer::InitializationBiasedParticleSwarmOptimizer ( )
protected
virtual itk::InitializationBiasedParticleSwarmOptimizer::~InitializationBiasedParticleSwarmOptimizer ( )
inlineprotectedvirtual
itk::InitializationBiasedParticleSwarmOptimizer::InitializationBiasedParticleSwarmOptimizer ( const Self )
private

Member Function Documentation

virtual::itk::LightObject::Pointer itk::InitializationBiasedParticleSwarmOptimizer::CreateAnother ( void  ) const
virtual

Create an object from an instance, potentially deferring to a factory. This method allows you to create an instance of an object that is exactly the same type as the referring object. This is useful in cases where an object has been cast back to a base class.

Reimplemented from itk::SingleValuedNonLinearOptimizer.

virtual CoefficientType itk::InitializationBiasedParticleSwarmOptimizer::GetGlobalCoefficient ( )
virtual

The Particle swarm optimizer uses the following update formula:

\[c_3 = c_{3initial}(1.0 - IterationIndex/MaximalNumberOfIterations)\]

\[v_i(t+1) = w*v_i(t) + c_1*uniform(0,1)*(p_i-x_i(t)) + c_2*uniform(0,1)*(p_g-x_i(t)) + c_3*uniform(0,1)*(x_{init}-x_i(t))\]

\[x_i(t+1) = clampToBounds(x_i(t) + v_i(t+1))\]

where $w$ - inertia constant $c_1$ - personal coefficient $c_2$ - global coefficient $c_3$ - initial location coefficient $p_i$ - parameters yielding the best function value obtained by this particle $p_g$ - parameters yielding the best function value obtained by all particles $x_{init}$ - initial parameter values provided by user

virtual CoefficientType itk::InitializationBiasedParticleSwarmOptimizer::GetInertiaCoefficient ( )
virtual

The Particle swarm optimizer uses the following update formula:

\[c_3 = c_{3initial}(1.0 - IterationIndex/MaximalNumberOfIterations)\]

\[v_i(t+1) = w*v_i(t) + c_1*uniform(0,1)*(p_i-x_i(t)) + c_2*uniform(0,1)*(p_g-x_i(t)) + c_3*uniform(0,1)*(x_{init}-x_i(t))\]

\[x_i(t+1) = clampToBounds(x_i(t) + v_i(t+1))\]

where $w$ - inertia constant $c_1$ - personal coefficient $c_2$ - global coefficient $c_3$ - initial location coefficient $p_i$ - parameters yielding the best function value obtained by this particle $p_g$ - parameters yielding the best function value obtained by all particles $x_{init}$ - initial parameter values provided by user

virtual CoefficientType itk::InitializationBiasedParticleSwarmOptimizer::GetInitializationCoefficient ( )
virtual

The Particle swarm optimizer uses the following update formula:

\[c_3 = c_{3initial}(1.0 - IterationIndex/MaximalNumberOfIterations)\]

\[v_i(t+1) = w*v_i(t) + c_1*uniform(0,1)*(p_i-x_i(t)) + c_2*uniform(0,1)*(p_g-x_i(t)) + c_3*uniform(0,1)*(x_{init}-x_i(t))\]

\[x_i(t+1) = clampToBounds(x_i(t) + v_i(t+1))\]

where $w$ - inertia constant $c_1$ - personal coefficient $c_2$ - global coefficient $c_3$ - initial location coefficient $p_i$ - parameters yielding the best function value obtained by this particle $p_g$ - parameters yielding the best function value obtained by all particles $x_{init}$ - initial parameter values provided by user

virtual const char* itk::InitializationBiasedParticleSwarmOptimizer::GetNameOfClass ( ) const
virtual

Run-time type information (and related methods).

Reimplemented from itk::ParticleSwarmOptimizerBase.

virtual CoefficientType itk::InitializationBiasedParticleSwarmOptimizer::GetPersonalCoefficient ( )
virtual

The Particle swarm optimizer uses the following update formula:

\[c_3 = c_{3initial}(1.0 - IterationIndex/MaximalNumberOfIterations)\]

\[v_i(t+1) = w*v_i(t) + c_1*uniform(0,1)*(p_i-x_i(t)) + c_2*uniform(0,1)*(p_g-x_i(t)) + c_3*uniform(0,1)*(x_{init}-x_i(t))\]

\[x_i(t+1) = clampToBounds(x_i(t) + v_i(t+1))\]

where $w$ - inertia constant $c_1$ - personal coefficient $c_2$ - global coefficient $c_3$ - initial location coefficient $p_i$ - parameters yielding the best function value obtained by this particle $p_g$ - parameters yielding the best function value obtained by all particles $x_{init}$ - initial parameter values provided by user

static Pointer itk::InitializationBiasedParticleSwarmOptimizer::New ( )
static

Method for creation through the object factory.

Reimplemented from itk::SingleValuedNonLinearOptimizer.

void itk::InitializationBiasedParticleSwarmOptimizer::operator= ( const Self )
private

Types inherited from the superclass

Reimplemented from itk::ParticleSwarmOptimizerBase.

void itk::InitializationBiasedParticleSwarmOptimizer::PrintSelf ( std::ostream &  os,
Indent  indent 
) const
protectedvirtual

Methods invoked by Print() to print information about the object including superclasses. Typically not called by the user (use Print() instead) but used in the hierarchical print process to combine the output of several classes.

Reimplemented from itk::ParticleSwarmOptimizerBase.

virtual void itk::InitializationBiasedParticleSwarmOptimizer::SetGlobalCoefficient ( CoefficientType  _arg)
virtual

The Particle swarm optimizer uses the following update formula:

\[c_3 = c_{3initial}(1.0 - IterationIndex/MaximalNumberOfIterations)\]

\[v_i(t+1) = w*v_i(t) + c_1*uniform(0,1)*(p_i-x_i(t)) + c_2*uniform(0,1)*(p_g-x_i(t)) + c_3*uniform(0,1)*(x_{init}-x_i(t))\]

\[x_i(t+1) = clampToBounds(x_i(t) + v_i(t+1))\]

where $w$ - inertia constant $c_1$ - personal coefficient $c_2$ - global coefficient $c_3$ - initial location coefficient $p_i$ - parameters yielding the best function value obtained by this particle $p_g$ - parameters yielding the best function value obtained by all particles $x_{init}$ - initial parameter values provided by user

virtual void itk::InitializationBiasedParticleSwarmOptimizer::SetInertiaCoefficient ( CoefficientType  _arg)
virtual

The Particle swarm optimizer uses the following update formula:

\[c_3 = c_{3initial}(1.0 - IterationIndex/MaximalNumberOfIterations)\]

\[v_i(t+1) = w*v_i(t) + c_1*uniform(0,1)*(p_i-x_i(t)) + c_2*uniform(0,1)*(p_g-x_i(t)) + c_3*uniform(0,1)*(x_{init}-x_i(t))\]

\[x_i(t+1) = clampToBounds(x_i(t) + v_i(t+1))\]

where $w$ - inertia constant $c_1$ - personal coefficient $c_2$ - global coefficient $c_3$ - initial location coefficient $p_i$ - parameters yielding the best function value obtained by this particle $p_g$ - parameters yielding the best function value obtained by all particles $x_{init}$ - initial parameter values provided by user

virtual void itk::InitializationBiasedParticleSwarmOptimizer::SetInitializationCoefficient ( CoefficientType  _arg)
virtual

The Particle swarm optimizer uses the following update formula:

\[c_3 = c_{3initial}(1.0 - IterationIndex/MaximalNumberOfIterations)\]

\[v_i(t+1) = w*v_i(t) + c_1*uniform(0,1)*(p_i-x_i(t)) + c_2*uniform(0,1)*(p_g-x_i(t)) + c_3*uniform(0,1)*(x_{init}-x_i(t))\]

\[x_i(t+1) = clampToBounds(x_i(t) + v_i(t+1))\]

where $w$ - inertia constant $c_1$ - personal coefficient $c_2$ - global coefficient $c_3$ - initial location coefficient $p_i$ - parameters yielding the best function value obtained by this particle $p_g$ - parameters yielding the best function value obtained by all particles $x_{init}$ - initial parameter values provided by user

virtual void itk::InitializationBiasedParticleSwarmOptimizer::SetPersonalCoefficient ( CoefficientType  _arg)
virtual

The Particle swarm optimizer uses the following update formula:

\[c_3 = c_{3initial}(1.0 - IterationIndex/MaximalNumberOfIterations)\]

\[v_i(t+1) = w*v_i(t) + c_1*uniform(0,1)*(p_i-x_i(t)) + c_2*uniform(0,1)*(p_g-x_i(t)) + c_3*uniform(0,1)*(x_{init}-x_i(t))\]

\[x_i(t+1) = clampToBounds(x_i(t) + v_i(t+1))\]

where $w$ - inertia constant $c_1$ - personal coefficient $c_2$ - global coefficient $c_3$ - initial location coefficient $p_i$ - parameters yielding the best function value obtained by this particle $p_g$ - parameters yielding the best function value obtained by all particles $x_{init}$ - initial parameter values provided by user

virtual void itk::InitializationBiasedParticleSwarmOptimizer::UpdateSwarm ( )
protectedvirtual

Implement your update rule in this function.

Implements itk::ParticleSwarmOptimizerBase.


Member Data Documentation

ParametersType::ValueType itk::InitializationBiasedParticleSwarmOptimizer::m_GlobalCoefficient
private
ParametersType::ValueType itk::InitializationBiasedParticleSwarmOptimizer::m_InertiaCoefficient
private
ParametersType::ValueType itk::InitializationBiasedParticleSwarmOptimizer::m_InitializationCoefficient
private
ParametersType::ValueType itk::InitializationBiasedParticleSwarmOptimizer::m_PersonalCoefficient
private

The documentation for this class was generated from the following file: