#include <itkInitializationBiasedParticleSwarmOptimizer.h>

Inheritance diagram for itk::InitializationBiasedParticleSwarmOptimizer:

Collaboration diagram for itk::InitializationBiasedParticleSwarmOptimizer:

Public Types
typedef double	CoefficientType
typedef SmartPointer< const Self >	ConstPointer
typedef SmartPointer< Self >	Pointer
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< ParticleData >	SwarmType
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 CostFunctionType *	GetCostFunction ()
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< MeasureType >	m_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< ParticleData >	m_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

typedef double itk::InitializationBiasedParticleSwarmOptimizer::CoefficientType

Definition at line 81 of file itkInitializationBiasedParticleSwarmOptimizer.h.

typedef SmartPointer<const Self> itk::InitializationBiasedParticleSwarmOptimizer::ConstPointer

Reimplemented from itk::ParticleSwarmOptimizerBase.

Definition at line 79 of file itkInitializationBiasedParticleSwarmOptimizer.h.

typedef SmartPointer<Self> itk::InitializationBiasedParticleSwarmOptimizer::Pointer

Reimplemented from itk::ParticleSwarmOptimizerBase.

Definition at line 78 of file itkInitializationBiasedParticleSwarmOptimizer.h.

typedef InitializationBiasedParticleSwarmOptimizer itk::InitializationBiasedParticleSwarmOptimizer::Self

Standard "Self" typedef.

Reimplemented from itk::ParticleSwarmOptimizerBase.

Definition at line 76 of file itkInitializationBiasedParticleSwarmOptimizer.h.

typedef ParticleSwarmOptimizerBase itk::InitializationBiasedParticleSwarmOptimizer::Superclass

Reimplemented from itk::ParticleSwarmOptimizerBase.

Definition at line 77 of file itkInitializationBiasedParticleSwarmOptimizer.h.

Constructor & Destructor Documentation

itk::InitializationBiasedParticleSwarmOptimizer::InitializationBiasedParticleSwarmOptimizer ( )

protected

virtual itk::InitializationBiasedParticleSwarmOptimizer::~InitializationBiasedParticleSwarmOptimizer ( )

inlineprotectedvirtual

Definition at line 118 of file itkInitializationBiasedParticleSwarmOptimizer.h.

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.