pagmo::algorithm::moead Class Reference

MOEA/D - DE. More...

#include <moea_d.h>

Inheritance diagram for pagmo::algorithm::moead:

Public Types
enum	weight_generation_type { RANDOM =0, GRID =1, LOW_DISCREPANCY =2 }
	Mechanism used to generate the weight vectors. More...

Public Member Functions
	moead (int gen=100, weight_generation_type=GRID, population::size_type=20, double realb=0.9, unsigned int limit=2, double CR=1.0, double F=0.5, double eta_m=20, bool preserve_diversity=true)
	Constructor. More...
base_ptr	clone () const
	Clone method.
void	evolve (population &) const
	Evolve implementation.
std::string	get_name () const
	Algorithm name.
std::vector< fitness_vector >	generate_weights (const unsigned int, const unsigned int) const
	Weight generation. More...
Public Member Functions inherited from pagmo::algorithm::base
	base ()
	Default constructor. More...
virtual	~base ()
	Trivial destructor. More...
std::string	human_readable () const
	Return human readable representation of the algorithm. More...
void	set_screen_output (const bool p)
	Setter-Getter for protected m_screen_output data. More...
bool	get_screen_output () const
	Gets screen output. More...
void	reset_rngs (const unsigned int) const
	Resets the seed of the internal rngs using a user-provided seed. More...

Protected Member Functions
std::string	human_readable_extra () const
	Extra human readable algorithm info. More...

Friends
class	boost::serialization::access

Additional Inherited Members
Protected Attributes inherited from pagmo::algorithm::base
bool	m_screen_output
	Indicates to the derived class whether to print stuff on screen.
rng_double	m_drng
	Random number generator for double-precision floating point values.
rng_uint32	m_urng
	Random number generator for unsigned integer values.
unsigned int	m_fevals
	A counter for the number of function evaluations.

Detailed Description

MOEA/D - DE.

This class implements the Multi Objective Evolutionary Algorithm based on Decomposition and Differential Evolution crossover. The reference papers can be found below. By activating or deactivating the bool preserve_diversity one can select to use the ideas introduced in the second paper or not. In all cases Tchebycheff decomposition and a differential evolution operator are used

See also

Zhang, Qingfu, and Hui Li. "MOEA/D: A multiobjective evolutionary algorithm based on decomposition." Evolutionary Computation, IEEE Transactions on 11.6 (2007): 712-731.

Li, Hui, and Qingfu Zhang. "Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II." Evolutionary Computation, IEEE Transactions on 13.2 (2009): 284-302.

Definition at line 50 of file moea_d.h.

Member Enumeration Documentation

enum pagmo::algorithm::moead::weight_generation_type

Mechanism used to generate the weight vectors.

Enumerator
RANDOM	Weights are generated uniformly at random on the simplex.
GRID	Weights are generated on a uniform grid layed down on the simplex.
LOW_DISCREPANCY	Weights are generated on the simplex with low-discrepancy.

Definition at line 54 of file moea_d.h.

Constructor & Destructor Documentation

pagmo::algorithm::moead::moead	(	int	gen = 100,
		weight_generation_type	weight_generation = GRID,
		population::size_type	T = 20,
		double	realb = 0.9,
		unsigned int	limit = 2,
		double	cr = 1.0,
		double	f = 0.5,
		double	eta_m = 20,
		bool	preserve_diversity = true
	)

Constructor.

Constructs a MOEA/D-DE algorithm

Parameters

[in]	gen	Number of generations to evolve.
[in]	weight_generation	Method to generate the weights (GRID, LOW-DISCREPANCY or RANDOM)
[in]	T	Size of the neighborhood
[in]	realb	Chance that the neighbourhood is T rather than the whole population (only if m_preserve_diversity is true)
[in]	limit	Maximum number of copies reinserted in the population (only if m_preserve_diversity is true)
[in]	cr	Crossover parameter in the Differential Evolution operator
[in]	f	f parameter in the Differential Evolution operator
[in]	eta_m	Distribution index for the polynomial mutation
[in]	preserve_diversity	when true activates the two diversity preservation mechanism described in Li, Hui, and Qingfu Zhang paper

Exceptions

value_error

if gen is negative, weight_generation is not one of the allowed types, realb,cr or f are not in [1.0] or m_eta is < 0

Definition at line 66 of file moea_d.cpp.

Member Function Documentation

std::vector< fitness_vector > pagmo::algorithm::moead::generate_weights	(	const unsigned int	n_f,
		const unsigned int	n_w
	)		const

Weight generation.

Generates the weights used to decompose the problem.

Parameters

[in]	n_f	dimesnion of each weight (i.e. fitness dimension)
[in]	n_w	number of weights

Returns

an std:vector containing the weights (fitness_vector)

Exceptions

if	the population size is not possible for the selected generation method

Definition at line 152 of file moea_d.cpp.

std::string pagmo::algorithm::moead::human_readable_extra ( ) const

protectedvirtual

Extra human readable algorithm info.

Will return a formatted string displaying the parameters of the algorithm.

Reimplemented from pagmo::algorithm::base.

Definition at line 439 of file moea_d.cpp.

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

• moea_d.h
• moea_d.cpp