particle swarm optimization matlab code

Wed Oct 26, 2011 12:04 am

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

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particle swarm optimization matlab code

particle swarm optimization matlab code

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