ga_ex651.m

书籍代码:遗传演算法原理与应用_活用MATLAB(Source Code)

% GA_ex651.m file
% Find scaling factors of a Fuzzy control system by using GA

% PenChen Chou, 8-6-2001

%**********************************************************/
%       User can modify the following in blocks
%**********************************************************/
global MIN_offset MUL_factor LOCUS x_data y_data 
global Emin MDL Se Sde Su FT x0 y0 phi0

MDL='back_track'; eval(MDL); FT=2; 
MIN_offset=9e5; LOCUS=0; x_data=[]; y_data=[];
MUL_factor=5;
Emin=MIN_offset;
obj_fcn = 'GA_f651';	% Objective function
range = [  0.8     0.9     0.1
           1.2     1.1     1.0];
IC=[];elite=1;
gen_no=40;  popuSize=40;
bit_n=40; xover_rate=1; mutate_rate=0.05;
figure(3);hold on;grid
%************************************************************
% calling GA 
tic
% function [popu, popu_real, fcn_value, upper, average,...
%   lower, BEST_popu, popuSize, gen_no, para]=GA_genetic(obj_fcn,...
%   range, IC, elite, gen_no, popuSize, bit_n, xover_rate,...
%   mutate_rate);
[popu, popu_real, fcn_value, upper, average, lower, ...
    BEST_popu, popuSize, gen_no, para] = GA_genetic(obj_fcn,...
    range, IC, elite, gen_no, popuSize, bit_n, xover_rate,...
    mutate_rate);
t=toc/60;

fprintf('==>  Computation time is (%.2f) minutes.\n',t);            
            
% Show the result using parameters found by GA
Se=para(1);
Sde=para(2);
Su=para(3);
x0=0;y0=0;phi0=30;
[t, X, Y]=sim(MDL,[0:0.01:2]);
x=Y(:,1);y=Y(:,2);phi=Y(:,3);N=length(t);
figure(2);subplot(111);plot(x,y,'.');axis([0 20 0 200])
title('GA based vehicle back track trajectory');
xlabel(['Time(s), Se=' num2str(Se) ',Sde=' num2str(Sde) ',Su=' num2str(Su)]);
figure(3);hold off;clf