PaGMO  1.1.5
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pagmo::problem::base_dtlz Class Referenceabstract

Base DTLZ Multi-objective optimization problem. More...

#include <base_dtlz.h>

Inheritance diagram for pagmo::problem::base_dtlz:
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Public Member Functions

 base_dtlz (int, int)
 Constructor.
 
virtual base_ptr clone () const =0
 Clone method.
 
- Public Member Functions inherited from pagmo::problem::base_unc_mo
 base_unc_mo (base::size_type, base::size_type, base::f_size_type)
 Constructor from dimension and fitness dimension. More...
 
double p_distance (const decision_vector &) const
 Distance from the Pareto front (of a decision_vector) More...
 
double p_distance (const pagmo::population &) const
 Distance from the Pareto front (of a population) More...
 
- Public Member Functions inherited from pagmo::problem::base
 base (int, int=0, int=1, int=0, int=0, const double &=0)
 Constructor from global dimension, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...
 
 base (int, int, int, int, int, const std::vector< double > &)
 Constructor from global dimension, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...
 
 base (const double &, const double &, int, int=0, int=1, int=0, int=0, const double &=0)
 Constructor from values for lower and upper bounds, global dimension, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...
 
 base (const decision_vector &, const decision_vector &, int=0, int=1, int=0, int=0, const double &=0)
 Constructor from upper/lower bounds, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...
 
template<std::size_t N>
 base (const double(&v1)[N], const double(&v2)[N], int ni=0, int nf=1, int nc=0, int nic=0, const double &c_tol=0)
 Constructor from raw arrays, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...
 
template<class Iterator1 , class Iterator2 >
 base (Iterator1 start1, Iterator1 end1, Iterator2 start2, Iterator2 end2, int ni=0, int nf=1, int nc=0, int nic=0, const double &c_tol=0)
 Constructor from iterators, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...
 
virtual ~base ()
 Trivial destructor. More...
 
std::string human_readable () const
 Return human readable representation of the problem. More...
 
virtual std::string human_readable_extra () const
 Extra information in human readable format. More...
 
bool operator== (const base &) const
 Equality operator. More...
 
bool operator!= (const base &) const
 Inequality operator. More...
 
bool is_compatible (const base &) const
 Compatibility operator. More...
 
bool compare_x (const decision_vector &, const decision_vector &) const
 Compare decision vectors. More...
 
bool verify_x (const decision_vector &) const
 Verify compatibility of decision vector x with problem. More...
 
bool compare_fc (const fitness_vector &, const constraint_vector &, const fitness_vector &, const constraint_vector &) const
 Simultaneous fitness-constraint comparison. More...
 
virtual void pre_evolution (population &) const
 Pre-evolution hook. More...
 
virtual void post_evolution (population &) const
 Post-evolution hook. More...
 
virtual void set_sparsity (int &lenG, std::vector< int > &iGfun, std::vector< int > &jGvar) const
 Sets the sparsity pattern of the gradient. More...
 
const decision_vectorget_lb () const
 Lower bounds getter. More...
 
const decision_vectorget_ub () const
 Upper bounds getter. More...
 
void set_bounds (const decision_vector &, const decision_vector &)
 Bounds setter from pagmo::decision_vector. More...
 
template<class Iterator1 , class Iterator2 >
void set_bounds (Iterator1 start1, Iterator1 end1, Iterator2 start2, Iterator2 end2)
 Bounds setter from iterators. More...
 
template<std::size_t N>
void set_bounds (const double(&v1)[N], const double(&v2)[N])
 Bounds setter from raw arrays. More...
 
void set_bounds (const double &, const double &)
 Set bounds to specified values. More...
 
void set_bounds (int, const double &, const double &)
 Set bounds to specified values. More...
 
void set_lb (const decision_vector &)
 Set lower bounds from pagmo::decision_vector. More...
 
void set_lb (int, const double &)
 Set specific lower bound to value. More...
 
void set_lb (const double &)
 Set all lower bounds to value. More...
 
template<class Iterator >
void set_lb (Iterator start, Iterator end)
 Lower bounds setter from iterators. More...
 
template<std::size_t N>
void set_lb (const double(&v)[N])
 Lower bounds setter from raw array. More...
 
void set_ub (const decision_vector &)
 Set upper bounds from pagmo::decision_vector. More...
 
void set_ub (int, const double &)
 Set specific upper bound to value. More...
 
void set_ub (const double &)
 Set all upper bounds to value. More...
 
template<class Iterator >
void set_ub (Iterator start, Iterator end)
 Upper bounds setter from iterators. More...
 
template<std::size_t N>
void set_ub (const double(&v)[N])
 Upper bounds setter from raw array. More...
 
unsigned int get_fevals () const
 Return number of function evaluations. More...
 
unsigned int get_cevals () const
 Return number of constraints function evaluations. More...
 
size_type get_dimension () const
 Return global dimension. More...
 
size_type get_i_dimension () const
 Return integer dimension. More...
 
f_size_type get_f_dimension () const
 Return fitness dimension. More...
 
c_size_type get_c_dimension () const
 Return global constraints dimension. More...
 
c_size_type get_ic_dimension () const
 Return inequality constraints dimension. More...
 
const std::vector< double > & get_c_tol () const
 Return constraints tolerance. More...
 
double get_diameter () const
 Get the diameter of the problem. More...
 
virtual std::string get_name () const
 Get problem's name. More...
 
constraint_vector compute_constraints (const decision_vector &) const
 Compute constraints and return constraint vector. More...
 
void compute_constraints (constraint_vector &, const decision_vector &) const
 Compute constraints and write them into contraint vector. More...
 
bool compare_constraints (const constraint_vector &, const constraint_vector &) const
 Compare constraint vectors. More...
 
bool test_constraint (const constraint_vector &, const c_size_type &) const
 Test i-th constraint of c (using tolerance information). More...
 
bool feasibility_x (const decision_vector &) const
 Test feasibility of decision vector. More...
 
bool feasibility_c (const constraint_vector &) const
 Test feasibility of constraint vector. More...
 
fitness_vector objfun (const decision_vector &) const
 Return fitness of pagmo::decision_vector. More...
 
void objfun (fitness_vector &, const decision_vector &) const
 Write fitness of pagmo::decision_vector into pagmo::fitness_vector. More...
 
bool compare_fitness (const fitness_vector &, const fitness_vector &) const
 Compare fitness vectors. More...
 
void reset_caches () const
 Reset internal caches. More...
 
const std::vector< constraint_vector > & get_best_c (void) const
 Get the best known constraint vector. More...
 
const std::vector< decision_vector > & get_best_x (void) const
 Get the best known decision vector. More...
 
const std::vector< fitness_vector > & get_best_f (void) const
 Get the best known fitness vector. More...
 
void set_best_x (const std::vector< decision_vector > &)
 Sets the best known decision vectors. More...
 

Protected Member Functions

virtual double g_func (const decision_vector &) const =0
 Distance function. More...
 
- Protected Member Functions inherited from pagmo::problem::base
virtual bool equality_operator_extra (const base &) const
 Extra requirements for equality. More...
 
virtual bool compare_fc_impl (const fitness_vector &, const constraint_vector &, const fitness_vector &, const constraint_vector &) const
 Implementation of simultaneous fitness-constraint comparison. More...
 
void estimate_sparsity (const decision_vector &, int &lenG, std::vector< int > &iGfun, std::vector< int > &jGvar) const
 Heuristics to estimate the sparsity pattern of the problem. More...
 
void estimate_sparsity (int &lenG, std::vector< int > &iGfun, std::vector< int > &jGvar) const
 Heuristics to estimate the sparsity pattern of the problem. More...
 
virtual void compute_constraints_impl (constraint_vector &, const decision_vector &) const
 Implementation of constraint computation. More...
 
virtual bool compare_constraints_impl (const constraint_vector &, const constraint_vector &) const
 Implementation of constraint vector comparison. More...
 
virtual bool compare_fitness_impl (const fitness_vector &, const fitness_vector &) const
 Implementation of fitness vectors comparison. More...
 
virtual void objfun_impl (fitness_vector &f, const decision_vector &x) const =0
 Objective function implementation. More...
 

Friends

class boost::serialization::access
 

Additional Inherited Members

- Public Types inherited from pagmo::problem::base
typedef decision_vector::size_type size_type
 Problem's size type: the same as pagmo::decision_vector's size type.
 
typedef fitness_vector::size_type f_size_type
 Fitness' size type: the same as pagmo::fitness_vector's size type.
 
typedef constraint_vector::size_type c_size_type
 Constraints' size type: the same as pagmo::constraint_vector's size type.
 
- Static Public Attributes inherited from pagmo::problem::base
static const std::size_t cache_capacity = 5
 Capacity of the internal caches.
 

Detailed Description

Base DTLZ Multi-objective optimization problem.

The DTLZ test suite was introduced by Deb et. al in order to create benchmark problems with various features that are scalable to any number of objectives and dimensions. The first seven DTLZ problems are constructed with a bottom up approach: First, the pareto-optimal front is described by a surface. Then, parallel layers of this surface are used to build the remainder of a decision space. For this, a distance function g is introduced which is minimized for pareto-optimal solutions. By evaluation of the g-function we thus get information how close the a solution is to the Pareto-optimal front. This is used to define a simple convergence metric (p-distance) that is average value of the g-function over all individuals.

The first seven DTLZ problems are already implemented in PaGMO but you can create your own DTLZ-style problems by subclassing this base class. In order to do this, you have to implement a g-function. The shape of the front itself has to be defined in the implementation of the objective function. The p-distance and a generic 3d-plot will then automatically be available for your new problem.

See also
K. Deb, L. Thiele, M. Laumanns, E. Zitzler, Scalable test problems for evoulationary multiobjective optimization
Author
Marcus Maertens (mmarc.nosp@m.usx@.nosp@m.gmail.nosp@m..com)

Definition at line 53 of file base_dtlz.h.

Member Function Documentation

virtual double pagmo::problem::base_dtlz::g_func ( const decision_vector ) const
protectedpure virtual

Distance function.

This pure virtual function is re-implemented in the derived classes and is used to compute the distance of a point from the Pareto front


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