#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 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 ()
Static Public Member Functions
static Pointer	New ()
Protected Member Functions
	InitializationBiasedParticleSwarmOptimizer ()
void	PrintSelf (std::ostream &os, Indent indent) const
virtual void	UpdateSwarm ()
virtual	~InitializationBiasedParticleSwarmOptimizer ()
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

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 ( ) [inline, protected, virtual]

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 `[protected, virtual]`

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 ( ) [protected, virtual]

Implement your update rule in this function.

Implements itk::ParticleSwarmOptimizerBase.