sgalab_demo_mo_showall.m

一个matlab环境下编制的使用很方便的多目标程序

% /*M-FILE SCRIPT SGALAB_DEMO_MO_showall MMM SGALAB */ %
% /*==================================================================================================
%  Simple Genetic Algorithm Laboratory Toolbox for Matlab 7.x
%
%  Copyright 2007 The SxLAB Family - Yi Chen - [e-mail]
% ====================================================================================================
%File description:
%        To show all MO methods in SGALAB on same fitness value, and the
%        results can be shown in one figure
%Input(1):
%            options[8]:
%                      options(1)-- en-/de-coding method
%                                   'Binary' ,'b' :  binary encoding method
%                                   'Real'   ,'r' :  real number encoding method
%                                   'Literal','l' :  literal permutation encoding method
%                                   'Gray'   ,'g' :  Gray encoding method
%                                   'DNA'    ,'d' :  DNA encoding method
%                                   'Messy'  ,'m'  :  Messy encoding method
%
%                      options(2)-- selection method
%                                    'Roulettewheel', 'Roulette','Wheel','r' : Roulette wheel selection method
%                                    'Stochastic','s'                        : Stochastic selection method
%                                    'TSP_Roulettewheel','tsp_rw','tsprw'    : TSP Roulette wheel selection
%
%                      options(3)-- crossover method
%                                   'singlepoint','single'
%                                   'twopoint','two'
%                                   'N = n','n'
%                                   'random','r'
%                                   'EAX':   Travel Salesman Problem--TSP Operator
%                                            Edge Assembly Crossover( EAX ) is to do
%                                            Edge Recombination Crossover(ERX) With double edge marker,
%                                            Briefly:
%                                                    EAX = ERX + Edge_Marker
%                                    'CX' :         TSP - Cycle Crossover, CX
%                                    'OX' :         TSP - Ordered Crossover operation, OX
%                                    'PMX':         TSP - Partially Matched Crossover, PMX
%                                    'BOOLMATRIX':  TSP - Matrix Representations and Operators
%                      options(4)-- mutation  method
%                                   'singlepoint','single'
%                                   'twopoint','two'
%                                   'N = n','n'
%                                   'random','r'
%                                   'ReciprocalExchange', (Reciprocal
%                                                           Exchange. Swap two cities.)
%                                   'Displacement' , Displacement. Select a
%                                                    subtour and insert it in a random place.
%                                   'Insertion',     Select a city and
%                                                    insert it in a random place
%                                   'Inversion',      Select two points along the permutation, cut it at these points and re-insert the reversed string.
%                                                       (1 2 | 3 4 5 6 | 7 8 9) ? (1 2 | 6 5 4 3 | 7 8 9)
%                      options(5)-- constraint_status
%                                   'with'    ,'1'--have constraint conditions
%                                   'without' ,'0'--have no constraint
%                                   conditions
%                      options(6)-- Multi-Objects option
%                                   'NON_MO'    -- Non-Multi-Objective problem
%                                   'VEGA'      -- Vector Evaluated Genetic Algorithms,J. D. Schaffer
%                                   'MOGA'      -- Multiobjective Genetic Algorithm (moGA: Fonseca and Fleming, 1993)
%                                   'NSGA'      -- Non-dominated Sorting Genetic Algorithm(NSGA Srinivas, N. and K. Deb -1994 )
%                                   'NSGAII'    --  Non-dominated Sorting Genetic Algorithm - II
%                      options(7)-- Pareto ranking method
%                                   'Goldberg','G'               -- Goldberg's method: rank = rank + 1;
%                                   'Fonceca','Fleming','MO','F' -- Fonceca and Fleming's ranking method
%                                                                   current rank
%                                                                   =  the number of its dominating individulas + 1
%                      options(8)-- Save each generation fitness data
%                                   '1', -- Save; Pareto front neet this
%                                   '0', -- Not Save,only get last results
%
%Usage:
%  We can run SGALAB by key-in "SGALAB_DEMO_MO_showall" in Matlab command
%  window
%===================================================================================================
%  See Also:         SGALAB_demo_math
%                    SGALAB_demo_MO_VEGA
%                    SGALAB_demo_MO_NPGA
%                    SGALAB_demo_MO_NSGA
%                    SGALAB_demo_MO_NSGAII
%                    SGALAB_demo_MO_VEGA
%                    SGALAB_demo_TSP_13cities
%
%===================================================================================================
%
%===================================================================================================
%Revision -
%Date        Name    Description of Change email                 Location
%14-Dec-2006 Chen Yi update 1003           [e-mail]  Glasgow
%20-Mar-2007 Chen Yi add options(8)        [e-mail]  Glasgow
%HISTORY$
%==================================================================================================*/

% SGALAB_DEMO_MO_showall Begin

%% set screen
% fresh
clear ;
close ('all');
warning off
% to delete old output_*.txt
!del OUTPUT_*.txt
!del SGA_entry_MO_*.mat
% set working path
%cd SGALAB_Funcs
%      SGA_set_working_paths

%% begin to count time during calculating
home ;
tic % timer start >>

% data preparation

%% open data files

%%%input data files
fid1  = fopen('INPUT_min_confines.txt' , 'r' );
fid2  = fopen('INPUT_max_confines.txt' , 'r' );
fid3  = fopen('INPUT_probability_crossover.txt' , 'r' );
fid4  = fopen('INPUT_probability_mutation.txt' , 'r' );
fid5  = fopen('INPUT_population.txt' , 'r' );
fid6  = fopen('INPUT_steps.txt' , 'r' );
fid7  = fopen('INPUT_max_generation.txt' , 'r' );
fid8  = fopen('INPUT_convergence_method.txt' , 'r' );
fid9  = fopen('INPUT_max_no_change_probability_crossover_generation.txt','r');
fid10 = fopen('INPUT_deta_fitness_max.txt','r');
fid11 = fopen('INPUT_max_probability_crossover.txt','r');
fid12 = fopen('INPUT_probability_crossover_step.txt','r');
fid13 = fopen('INPUT_max_no_change_fitness_generation.txt','r');

%output data files
fid14 = fopen('OUTPUT_maxfitness.txt','w+');
fid15 = fopen('OUTPUT_minfitness.txt','w+');
fid16 = fopen('OUTPUT_meanfitness.txt','w+');
fid17 = fopen('OUTPUT_best_result_space.txt','w+');
fid18 = fopen('OUTPUT_best_coding_space.txt','w+');
fid19 = fopen('OUTPUT_now_generation.txt','w+');
fid20 = fopen('OUTPUT_now_probability_crossover.txt','w+');

% begin to load data from file

%% read data from these files
disp('/*==================================================================================================*/')
disp('/*  Simple Genetic Algorithm Laboratory Toolbox (SGALAB) 1.0.0.3 for Matlab 7.x */ ')
disp('');
disp('    20-Mar-2007 Chen Yi update 1003     [e-mail]  Glasgow ')
disp('/*==================================================================================================*/')
disp('>>>>')
disp(' Begin to evaluate...Waiting please ...')


min_confines = fscanf( fid1 , '%g' ); min_confines = min_confines' ;

max_confines = fscanf( fid2 , '%g' ); max_confines = max_confines';

probability_crossover = fscanf( fid3 , '%g' ); probability_mutation = fscanf(fid4,'%g');

population = fscanf( fid5 , '%g' );

decimal_step = fscanf( fid6 , '%g' );

max_generation = fscanf( fid7 , '%g' );

convergence_method = fscanf( fid8 , '%g' );

max_no_change_probability_crossover_generation = fscanf( fid9 , '%g' );

deta_fitness_max = fscanf( fid10 , '%g' );

max_probability_crossover = fscanf( fid11,'%g' );

probability_crossover_step = fscanf(fid12,'%g');

max_no_change_fitness_generation = fscanf(fid13,'%g');

decimal_step = decimal_step' ;

now_probability_crossover = probability_crossover;
%

disp(' >>>>')
disp('End Evaluating, List of results :')

% Step into SGALAB()
options = { 
    'Binary',
    'tournament',
    'singlepoint',
    'singlepoint',
    '0',
    ' ',
    'Goldberg',
    '1'};

% Output

[ maxfitness ,...
    minfitness ,...
    meanfitness ,...
    now_generation , ...
    now_probability_crossover,...
    best_decimal_space ,...
    best_coding_space ,...
    error_status ]= SGA_entry_MO_showall...
    ( options,...
    min_confines ,...
    max_confines ,...
    probability_crossover ,...
    probability_mutation ,...
    population ,...
    decimal_step , ...
    max_generation ,...
    convergence_method ,...
    max_no_change_probability_crossover_generation ,...
    deta_fitness_max ,...
    max_probability_crossover ,...
    max_no_change_fitness_generation ,...
    probability_crossover_step  );

if ( error_status ~= 0 )  return ;  end

%  [ maxfitness , minfitness , meanfitness , best_decimal_space , now_generation , now_probability_crossover , best_binary_space ] = SGALAB...
%  ( min_confines , max_confines , probability_crossover , probability_mutation , population , decimal_step , max_generation , convergence_method , max_no_change_probability_crossover_generation , deta_fitness_max , deta_fitness_max_min , max_probability_crossover , max_no_change_fitness_generation , probability_crossover_step );

%  maxfitness = max( fitness_value )
%  best_decimal_space = decimal_space( max_fitness_temp_position( population ) )

%write data to output files
% fprintf( fid8 , '\n the max value of fitness function:\n' );
% 
% fid14 = fopen('OUTPUT_maxfitness.txt','w+');
% fid15 = fopen('OUTPUT_minfitness.txt','w+');
% fid16 = fopen('OUTPUT_meanfitness.txt','w+');
% fid17 = fopen('OUTPUT_best_result_space.txt','w+');
% fid18 = fopen('OUTPUT_best_coding_space.txt','w+');
% fid19 = fopen('OUTPUT_now_generation.txt','w+');
% fid20 = fopen('OUTPUT_now_probability_crossover.txt','w+');

% fprintf( fid14 , '%f\n' , maxfitness);
save('OUTPUT_maxfitness.txt', '-struct', 'maxfitness','-ASCII');

%fprintf( fid9, '\n the min value of fitness function:\n');
% fprintf( fid15 , '%f\n' ,minfitness);
save('OUTPUT_minfitness.txt', '-struct', 'minfitness','-ASCII');

%fprintf(fid10,'\n the mean value of fitness function:\n');
% fprintf(fid16,'%f\n',meanfitness);
save('OUTPUT_meanfitness.txt', '-struct', 'meanfitness','-ASCII');

%fprintf( fid11,'\nthe best decimal space(x1 x2 x3...):\n');
% fprintf( fid17,'%f\n',best_decimal_space );
save('OUTPUT_best_result_space.txt', '-struct', 'best_decimal_space','-ASCII');

% fprintf( fid18 , '%f\n' , best_coding_space );
save('OUTPUT_best_coding_space.txt', '-struct', 'best_coding_space','-ASCII');
%fprintf( fid12, '\nthe generation number when end GAs:\n' );

% fprintf( fid19, '%f\n' , now_generation );
save('OUTPUT_now_generation.txt', '-struct', 'now_generation','-ASCII');

% fprintf( fid20, '%f\n' , now_probability_crossover );
save('OUTPUT_now_probability_crossover.txt', '-struct', 'now_probability_crossover','-ASCII');

%close files
status = fclose( 'all' );


disp(' More detail result in text files with " Output_*.txt " ' )
disp('----------------------------------------------------------------------------------------')
result_files = list_current_dir_files ('OUTPUT_*.txt')

disp('----------------------------------------------------------------------------------------')

% timer end
toc

% SGALAB_DEMO_MO_showall End