www.pudn.com > PSOtofMatlab.rar > DemoPSOBehavior.m, change:2005-03-22,size:3549b

% demopsobehavior.m 
% demo of the pso.m function 
% the pso tries to find the minimum of the f6 function, a standard 
% benchmark 
% 
% on the plots, blue is current position, green is Pbest, and red is Gbest 
 
% Brian Birge 
% Rev 2.0 
% 10/30/03 
 
clear all 
close all 
clc 
help demopsobehavior 
warning off 
 
%aviobj = avifile(['demopsobehavior.avi']); 
%aviobj.Quality=100; 
%aviobj.Compression='Cinepak'; 
%aviobj.fps=15; 
 
%xrng=input('Input search range for X, e.g. [-10,10] ? '); 
%yrng=input('Input search range for Y ? '); 
xrng=[-20,20]; 
yrng=[-20,20]; 
 
% if =0 then we look for minimum, =1 then max 
 % minmax=input('Search for min (0) or max (1) ?'); 
  minmax=1; 
  mvden = input('Max velocity divisor ? ');  
  ps    = input('How many particles? '); 
  modl  = input('Choose PSO model? '); 
 % note: if errgoal=NaN then unconstrained min or max is performed 
  if minmax==1 
    %  errgoal=0.97643183; % max for f6 function (close enough for termination) 
      errgoal=NaN; 
  else 
     % errgoal=0; % min 
      errgoal=NaN; 
  end 
  minx = xrng(1); 
  maxx = xrng(2); 
  miny = yrng(1); 
  maxy = yrng(2); 
 
%-------------------------------------------------------------------------- 
  dims=2; 
  varrange=[]; 
  mv=[]; 
  for i=1:dims 
      varrange=[varrange;minx maxx]; 
      mv=[mv;(varrange(i,2)-varrange(i,1))/mvden]; 
  end 
   
  ac      = [2.1,2.1];% acceleration constants, only used for modl=0 
  Iwt     = [0.9,0.6];  % intertia weights, only used for modl=0 
  shw     = 100;   % how often to update display 
  epoch   = 20000; % max iterations 
  wt_end  = 1500; % iterations it takes to go from Iwt(1) to Iwt(2), only for modl=0 
  errgrad = 1e-99;   % lowest error gradient tolerance 
  errgraditer=5000; % max # of epochs without error change >= errgrad 
  PSOseed = 0;    % if=1 then can input particle starting positions, if= 0 then all random 
  % starting particle positions (first 20 at zero, just for an example) 
   PSOseedValue = repmat([0],ps-10,1); 
   
  psoparams=... 
   [shw epoch ps ac(1) ac(2) Iwt(1) Iwt(2) ... 
    wt_end errgrad errgraditer errgoal modl PSOseed]; 
 
 % run pso 
 % vectorized version (normally instead of goplotpso4demo use goplotpso or your own) 
  pso_out=pso_Trelea_vectorized('f6', dims,... 
      mv, varrange, minmax, psoparams,'goplotpso4demo',PSOseedValue); 
  %% vectorized version (normally instead of goplotpso4demo use goplotpso or your own) 
  %[pso_out,tr,te]=pso_Trelea_vectorized('f6', dims,... 
  %    mv, varrange, minmax, psoparams,'goplotpso',PSOseedValue); 
   
  %% non-vectorized (much slower but some cost functions can't be vectorized) 
 %  pso_out=pso_Trelea('f6', dims,... 
 %      mv, varrange, minmax, psoparams,'goplotpso4demo');   
%--------------------------------------------------------------------------  
disp('Best fit parameters: cost = f6(x,y)'); 
disp(['    x = ',num2str(pso_out(1))]); 
disp(['    y = ',num2str(pso_out(2))]); 
disp([' cost = ',num2str(pso_out(3))]); 
disp(['mean(te) = ',num2str(mean(te))]); 
%aviobj = close(aviobj); 
 
indx=find(bestpos(:,3)<1e-4); 
figure 
plot(gbest_pred(:,1),gbest_pred(:,2),'g.','Markersize',5) 
hold on 
plot(bestpos(:,1),bestpos(:,2),'b.','markersize',5) 
plot(bestpos(indx,1),bestpos(indx,2),'r.','markersize',8) 
%clear all,for i=1:5000, offset(i)=linear_dyn(0.5);, pause(0.01),end,x=offset;,y=x; 
%clear all,for i=1:2000, [x(i),y(i)]=spiral_dyn(1,.1);, pause(0.01),end 
%plot(x,y,'k-'), grid on, axis equal 
%title('red=good match gbest, blue=gbest, black is known') 
hold off