particle swarm optimization matlab code

% goplotpso.m
% default plotting script used in PSO functions
%
% this script is not a function,
% it is a plugin for the main PSO routine (pso_Trelea_vectorized)
% so it shares all the same variables, be careful with variable names
% when making your own plugin

% Brian Birge
% Rev 2.0
% 3/1/06

% setup figure, change this for your own machine
clf
set(gcf,'Position',[651    31   626   474]); % this is the computer dependent part
%set(gcf,'Position',[743    33   853   492]);
set(gcf,'Doublebuffer','on');
               
% particle plot, upper right
subplot('position',[.7,.6,.27,.32]);
set(gcf,'color','k')

plot3(pos(:,1),pos(:,D),out,'b.','Markersize',7)

hold on
plot3(pbest(:,1),pbest(:,D),pbestval,'g.','Markersize',7);
plot3(gbest(1),gbest(D),gbestval,'r.','Markersize',25);

% crosshairs
offx = max(abs(min(min(pbest(:,1)),min(pos(:,1)))),...
            abs(max(max(pbest(:,1)),max(pos(:,1)))));

offy = max(abs(min(min(pbest(:,D)),min(pos(:,D)))),...
            abs(min(max(pbest(:,D)),max(pos(:,D)))));
plot3([gbest(1)-offx;gbest(1)+offx],...
       [gbest(D);gbest(D)],...
       [gbestval;gbestval],...
       'r-.');
plot3([gbest(1);gbest(1)],...
       [gbest(D)-offy;gbest(D)+offy],...
       [gbestval;gbestval],...
       'r-.');
   
hold off

xlabel('Dimension 1','color','y')
ylabel(['Dimension ',num2str(D)],'color','y')
zlabel('Cost','color','y')

title('Particle Dynamics','color','w','fontweight','bold')

set(gca,'Xcolor','y')
set(gca,'Ycolor','y')
set(gca,'Zcolor','y')
set(gca,'color','k')
           
% camera control
view(2)
try
   axis([gbest(1)-offx,gbest(1)+offx,gbest(D)-offy,gbest(D)+offy]);
catch
   axis([VR(1,1),VR(1,2),VR(D,1),VR(D,2)]);
end

% error plot, left side
subplot('position',[0.1,0.1,.475,.825]);
  semilogy(tr(find(~isnan(tr))),'color','m','linewidth',2)
  %plot(tr(find(~isnan(tr))),'color','m','linewidth',2)
  xlabel('epoch','color','y')
  ylabel('gbest val.','color','y')
 
  if D==1
     titstr1=sprintf(['%11.6g = %s( [ %9.6g ] )'],...
                gbestval,strrep(functname,'_','\_'),gbest(1));
  elseif D==2
     titstr1=sprintf(['%11.6g = %s( [ %9.6g, %9.6g ] )'],...
                gbestval,strrep(functname,'_','\_'),gbest(1),gbest(2));
  elseif D==3
     titstr1=sprintf(['%11.6g = %s( [ %9.6g, %9.6g, %9.6g ] )'],...
                gbestval,strrep(functname,'_','\_'),gbest(1),gbest(2),gbest(3));
  else
     titstr1=sprintf(['%11.6g = %s( [ %g inputs ] )'],...
                gbestval,strrep(functname,'_','\_'),D);
  end
  title(titstr1,'color','m','fontweight','bold');
 
  grid on
%  axis tight

  set(gca,'Xcolor','y')
  set(gca,'Ycolor','y')
  set(gca,'Zcolor','y')
  set(gca,'color','k')

  set(gca,'YMinorGrid','off')
 
% text box in lower right
% doing it this way so I can format each line any way I want
subplot('position',[.62,.1,.29,.4]);
  clear titstr
  if trelea==0
       PSOtype  = 'Common PSO';
       xtraname = 'Inertia Weight : ';
       xtraval  = num2str(iwt(length(iwt)));
       
     elseif trelea==2 | trelea==1
       
       PSOtype  = (['Trelea Type ',num2str(trelea)]);
       xtraname = ' ';
       xtraval  = ' ';
       
     elseif trelea==3
       PSOtype  = (['Clerc Type 1"']);
       xtraname = '\chi value : ';
       xtraval  = num2str(chi);

  end
  if isnan(errgoal)
    errgoalstr='Unconstrained';
  else
    errgoalstr=num2str(errgoal);
  end
  if minmax==1
     minmaxstr = ['Maximize to : '];
  elseif minmax==0
     minmaxstr = ['Minimize to : '];
  else
     minmaxstr = ['Target   to : '];
  end
 
  if rstflg==1
     rststat1 = 'Environment Change';
     rststat2 = ' ';
  else
     rststat1 = ' ';
     rststat2 = ' ';
  end
 
  titstr={'PSO Model: '      ,PSOtype;...
          'Dimensions : '    ,num2str(D);...
          '# of particles : ',num2str(ps);...
          minmaxstr          ,errgoalstr;...
          'Function : '      ,strrep(functname,'_','\_');...
          xtraname           ,xtraval;...
          rststat1           ,rststat2};
 
  text(.1,1,[titstr{1,1},titstr{1,2}],'color','g','fontweight','bold');
  hold on
  text(.1,.9,[titstr{2,1},titstr{2,2}],'color','m');
  text(.1,.8,[titstr{3,1},titstr{3,2}],'color','m');
  text(.1,.7,[titstr{4,1}],'color','w');
  text(.55,.7,[titstr{4,2}],'color','m');
  text(.1,.6,[titstr{5,1},titstr{5,2}],'color','m');
  text(.1,.5,[titstr{6,1},titstr{6,2}],'color','w','fontweight','bold');
  text(.1,.4,[titstr{7,1},titstr{7,2}],'color','r','fontweight','bold');
 
  % if we are training a neural net, show a few more parameters
  if strcmp('pso_neteval',functname)
    % net is passed from trainpso to pso_Trelea_vectorized in case you are
    % wondering where that structure comes from
    hiddlyrstr = []; 
    for lyrcnt=1:length(net.layers)
       TF{lyrcnt} = net.layers{lyrcnt}.transferFcn;
       Sn(lyrcnt) = net.layers{lyrcnt}.dimensions;
       hiddlyrstr = [hiddlyrstr,', ',TF{lyrcnt}];
    end
    hiddlyrstr = hiddlyrstr(3:end);
 
    text(0.1,.35,['#neur/lyr = [ ',num2str(net.inputs{1}.size),'  ',...
               num2str(Sn),' ]'],'color','c','fontweight','normal',...
               'fontsize',10);   
    text(0.1,.275,['Lyr Fcn: ',hiddlyrstr],...
       'color','c','fontweight','normal','fontsize',9);
       
  end
 
 
  legstr = {'Green = Personal Bests';...
            'Blue  = Current Positions';...
            'Red   = Global Best'};
  text(.1,0.025,legstr{1},'color','g');
  text(.1,-.05,legstr{2},'color','b');
  text(.1,-.125,legstr{3},'color','r');
 
  hold off

  set(gca,'color','k');
  set(gca,'visible','off');
 
  drawnow