DE Class Reference

DE class contains the functions that are specific to this particular optimization algorithm. More...

#include <mpi_optalg.h>

Inheritance diagram for DE:

Public Member Functions
	DE (Problem *problem_ptr, Rng *gaussian_rng, int pop_size)
void	put_to_best ()
void	combination ()
void	selection ()
void	fit_to_global ()
void	find_global ()
void	write_param (double *param_array)
void	read_param (double *param_array)
Public Member Functions inherited from OptAlg
	OptAlg (Problem *problem_ptr, Rng *gaussian_rng, int pop_size)
void	Init_population (int psize)
void	Init_previous (double prev_dev, double new_dev, int psize, double *prev_soln)
void	Cont_fitness (int p)
void	Best_fitness (int p)
void	update_popfit ()
void	set_success (int iter, bool goal)
bool	check_success (int t, double *current_fitarray, double *memory_fitarray, int data_size, double t_goal, bool *mem_ptype, int *numvar, int N_cut, double *memory_forT)
double	Final_select (double *fit, double *solution, double *fitarray)
double	avg_Final_select (double *solution, int repeat, double *soln_fit, double *fitarray)
void	dev_gen (double *dev_array, double prev_dev, double new_dev, int cut_off)
int	find_max (double *fit)

Additional Inherited Members
Public Attributes inherited from OptAlg
Problem *	prob
bool	success
bool	policy_type
Protected Attributes inherited from OptAlg
int	num
int	num_fit
Rng *	gaussian_rng
int	pop_size
int	T
int	t
Candidate *	pop
bool	goal
int	total_pop
int	my_rank
int	nb_proc

Detailed Description

DE class contains the functions that are specific to this particular optimization algorithm.

Member Function Documentation

combination()

void DE::combination ( )

virtual

This function main purpose is to prepare the population for optimization after the population is initialized and the first calculation of the fitness function.

Combination() generates the next generation of candidates. First, all the candidate give their best array to the zeroth processor who then generates the new candidate and send it back to the parent candidate. The parent store the new array into the contender and compute the mean fitness value.

Reimplemented from OptAlg.

find_global()

void DE::find_global ( )

inlinevirtual

This function copies memories on best array to global array.

Reimplemented from OptAlg.

fit_to_global()

void DE::fit_to_global ( )

virtual

This function reads the search parameters that are currently in use.

Reimplemented from OptAlg.

read_param()

void DE::read_param ( double *  param_array )

virtual

This function let the user specify new values to the search parameters.

Reimplemented from OptAlg.

selection()

void DE::selection ( )

virtual

This function generates new candidates that competes with the existing population. This is one potential bottleneck in the communication between processors.

Selection() reads the mean fitness value of best array and contender and decides which one is to be the member of the population. In this version, we simple select the one with the higher fitness value.

Reimplemented from OptAlg.

write_param()

void DE::write_param ( double *  param_array )

virtual

This function performs selects the candidates for the next round of iteration.

Reimplemented from OptAlg.

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

• C:/DATA/PROGRAM/GitHub/phase_estimation/src/mpi_optalg.h
• C:/DATA/PROGRAM/GitHub/phase_estimation/src/mpi_de.cpp