pso_trelea_vectorized.m

本文是关于粒子群算法(pso)的介绍和相关MATLAB源程序,是智能天线权值优化方面的一种很前沿的算法

     psi     = ac1 + ac2;
     chi_den = abs(2-psi-sqrt(psi^2 - 4*psi));
     chi_num = 2*kappa;
     chi     = chi_num/chi_den;
 end
end

% INITIALIZE END INITIALIZE END INITIALIZE END INITIALIZE END
rstflg = 0; % for dynamic environment checking
% start PSO iterative procedures
 cnt    = 0; % counter used for updating display according to df in the options
 cnt2   = 0; % counter used for the stopping subroutine based on error convergence
 iwt(1) = iw1;
for i=1:me  % start epoch loop (iterations)

     out        = feval(functname,[pos;gbest]);
     outbestval = out(end,:);
     out        = out(1:end-1,:);

     tr(i+1)          = gbestval; % keep track of global best val
     te               = i; % returns epoch number to calling program when done
     bestpos(i,1:D+1) = [gbest,gbestval];
     
     %assignin('base','bestpos',bestpos(i,1:D+1));
   %------------------------------------------------------------------------      
   % this section does the plots during iterations   
    if plotflg==1      
      if (rem(i,df) == 0 ) | (i==me) | (i==1) 
         fprintf(message,i,gbestval);
         cnt = cnt+1; % count how many times we display (useful for movies)
          
         eval(plotfcn); % defined at top of script
         
      end  % end update display every df if statement    
    end % end plotflg if statement

    % check for an error space that changes wrt time/iter
    % threshold value that determines dynamic environment 
    % sees if the value of gbest changes more than some threshold value
    % for the same location
    chkdyn = 1;
    rstflg = 0; % for dynamic environment checking

    if chkdyn==1
     threshld = 0.05;  % percent current best is allowed to change, .05 = 5% etc
     letiter  = 5; % # of iterations before checking environment, leave at least 3 so PSO has time to converge
     outorng  = abs( 1- (outbestval/gbestval) ) >= threshld;
     samepos  = (max( sentry == gbest ));

     if (outorng & samepos) & rem(i,letiter)==0
         rstflg=1;
       % disp('New Environment: reset pbest, gbest, and vel');
       %% reset pbest and pbestval if warranted
%        outpbestval = feval( functname,[pbest] );
%        Poutorng    = abs( 1-(outpbestval./pbestval) ) > threshld;
%        pbestval    = pbestval.*~Poutorng + outpbestval.*Poutorng;
%        pbest       = pbest.*repmat(~Poutorng,1,D) + pos.*repmat(Poutorng,1,D);   

        pbest     = pos; % reset personal bests to current positions
        pbestval  = out; 
        vel       = vel*10; % agitate particles a little (or a lot)
        
       % recalculate best vals 
        if minmax == 1
           [gbestval,idx1] = max(pbestval);
        elseif minmax==0
           [gbestval,idx1] = min(pbestval);
        elseif minmax==2 % this section needs work
           [temp,idx1] = min((pbestval-ones(size(pbestval))*errgoal).^2);
           gbestval    = pbestval(idx1);
        end
        
        gbest  = pbest(idx1,:);
        
        % used with trainpso, for neural net training
        % assign gbest to net at each iteration, these interim assignments
        % are for plotting mostly
        if strcmp(functname,'pso_neteval')
           net=setx(net,gbest);
        end
     end  % end if outorng
     
     sentryval = gbestval;
     sentry    = gbest;
     
    end % end if chkdyn
    
    % find particles where we have new pbest, depending on minmax choice 
    % then find gbest and gbestval
     %[size(out),size(pbestval)]
    if rstflg == 0
     if minmax == 0
        [tempi]            = find(pbestval>=out); % new min pbestvals
        pbestval(tempi,1)  = out(tempi);   % update pbestvals
        pbest(tempi,:)     = pos(tempi,:); % update pbest positions
       
        [iterbestval,idx1] = min(pbestval);
        
        if gbestval >= iterbestval
            gbestval = iterbestval;
            gbest    = pbest(idx1,:);
            % used with trainpso, for neural net training
            % assign gbest to net at each iteration, these interim assignments
            % are for plotting mostly
             if strcmp(functname,'pso_neteval')
                net=setx(net,gbest);
             end
        end
     elseif minmax == 1
        [tempi,dum]        = find(pbestval<=out); % new max pbestvals
        pbestval(tempi,1)  = out(tempi,1); % update pbestvals
        pbest(tempi,:)     = pos(tempi,:); % update pbest positions
 
        [iterbestval,idx1] = max(pbestval);
        if gbestval <= iterbestval
            gbestval = iterbestval;
            gbest    = pbest(idx1,:);
            % used with trainpso, for neural net training
            % assign gbest to net at each iteration, these interim assignments
            % are for plotting mostly
             if strcmp(functname,'pso_neteval')
                net=setx(net,gbest);
             end
        end
     elseif minmax == 2  % this won't work as it is, fix it later
        egones            = errgoal*ones(ps,1); % vector of errgoals
        sqrerr2           = ((pbestval-egones).^2);
        sqrerr1           = ((out-egones).^2);
        [tempi,dum]       = find(sqerr1 <= sqrerr2); % find particles closest to targ
        pbestval(tempi,1) = out(tempi,1); % update pbestvals
        pbest(tempi,:)    = pos(tempi,:); % update pbest positions

        sqrerr            = ((pbestval-egones).^2); % need to do this to reflect new pbests
        [temp,idx1]       = min(sqrerr);
        iterbestval       = pbestval(idx1);
        
        if (iterbestval-errgoal)^2 <= (gbestval-errgoal)^2
           gbestval = iterbestval;
           gbest    = pbest(idx1,:);
           % used with trainpso, for neural net training
            % assign gbest to net at each iteration, these interim assignments
            % are for plotting mostly
             if strcmp(functname,'pso_neteval')
                net=setx(net,gbest);
             end
        end
     end
    end
    
    
 %   % build a simple predictor 10th order, for gbest trajectory
 %   if i>500
 %    for dimcnt=1:D
 %      pred_coef  = polyfit(i-250:i,(bestpos(i-250:i,dimcnt))',20);
 %     % pred_coef  = polyfit(200:i,(bestpos(200:i,dimcnt))',20);       
 %      gbest_pred(i,dimcnt) = polyval(pred_coef,i+1);
 %    end
 %    else 
%       gbest_pred(i,:) = zeros(size(gbest));
%    end
  
   %gbest_pred(i,:)=gbest;    
   %assignin('base','gbest_pred',gbest_pred);

 %   % convert to non-inertial frame
 %    gbestoffset = gbest - gbest_pred(i,:);
 %    gbest = gbest - gbestoffset;
 %    pos   = pos + repmat(gbestoffset,ps,1);
 %    pbest = pbest + repmat(gbestoffset,ps,1);
 
     %PSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSO

      % get new velocities, positions (this is the heart of the PSO algorithm)     
      % each epoch get new set of random numbers
       rannum1 = rand([ps,D]); % for Trelea and Clerc types
       rannum2 = rand([ps,D]);       
       if     trelea == 2    
        % from Trelea's paper, parameter set 2
         vel = 0.729.*vel...                              % prev vel
               +1.494.*rannum1.*(pbest-pos)...            % independent
               +1.494.*rannum2.*(repmat(gbest,ps,1)-pos); % social  
       elseif trelea == 1
        % from Trelea's paper, parameter set 1                     
         vel = 0.600.*vel...                              % prev vel
               +1.700.*rannum1.*(pbest-pos)...            % independent
               +1.700.*rannum2.*(repmat(gbest,ps,1)-pos); % social 
       elseif trelea ==3
        % Clerc's Type 1" PSO
         vel = chi*(vel...                                % prev vel
               +ac1.*rannum1.*(pbest-pos)...              % independent
               +ac2.*rannum2.*(repmat(gbest,ps,1)-pos)) ; % social          
       else
        % common PSO algo with inertia wt 
        % get inertia weight, just a linear funct w.r.t. epoch parameter iwe
         if i<=iwe
            iwt(i) = ((iw2-iw1)/(iwe-1))*(i-1)+iw1;
         else
            iwt(i) = iw2;
         end
        % random number including acceleration constants
         ac11 = rannum1.*ac1;    % for common PSO w/inertia
         ac22 = rannum2.*ac2;
         
         vel = iwt(i).*vel...                             % prev vel
               +ac11.*(pbest-pos)...                      % independent
               +ac22.*(repmat(gbest,ps,1)-pos);           % social                  
       end
       
       % limit velocities here using masking
        vel = ( (vel <= velmaskmin).*velmaskmin ) + ( (vel > velmaskmin).*vel );
        vel = ( (vel >= velmaskmax).*velmaskmax ) + ( (vel < velmaskmax).*vel );     
        
       % update new position (PSO algo)    
        pos = pos + vel;
    
       % position masking, limits positions to desired search space
       % method: 0) no position limiting, 1) saturation at limit,
       %         2) wraparound at limit , 3) bounce off limit
        minposmask_throwaway = pos <= posmaskmin;  % these are psXD matrices
        minposmask_keep      = pos >  posmaskmin;     
        maxposmask_throwaway = pos >= posmaskmax;
        maxposmask_keep      = pos <  posmaskmax;
     
        if     posmaskmeth == 1
         % this is the saturation method
          pos = ( minposmask_throwaway.*posmaskmin ) + ( minposmask_keep.*pos );
          pos = ( maxposmask_throwaway.*posmaskmax ) + ( maxposmask_keep.*pos );      
        elseif posmaskmeth == 2
         % this is the wraparound method
          pos = ( minposmask_throwaway.*posmaskmax ) + ( minposmask_keep.*pos );
          pos = ( maxposmask_throwaway.*posmaskmin ) + ( maxposmask_keep.*pos );                
        elseif posmaskmeth == 3
         % this is the bounce method, particles bounce off the boundaries with -vel      
          pos = ( minposmask_throwaway.*posmaskmin ) + ( minposmask_keep.*pos );
          pos = ( maxposmask_throwaway.*posmaskmax ) + ( maxposmask_keep.*pos );

          vel = (vel.*minposmask_keep) + (-vel.*minposmask_throwaway);
          vel = (vel.*maxposmask_keep) + (-vel.*maxposmask_throwaway);
        else
         % no change, this is the original Eberhart, Kennedy method, 
         % it lets the particles grow beyond bounds if psoparams (P)
         % especially Vmax, aren't set correctly, see the literature
        end

     %PSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSO
% check for stopping criterion based on speed of convergence to desired 
   % error   
    tmp1 = abs(tr(i) - gbestval);
    if tmp1 > ergrd
       cnt2 = 0;
    elseif tmp1 <= ergrd
       cnt2 = cnt2+1;
       if cnt2 >= ergrdep
         if plotflg == 1
          fprintf(message,i,gbestval);           
          disp(' ');
          disp(['--> Solution likely, GBest hasn''t changed by at least ',...
              num2str(ergrd),' for ',...
                  num2str(cnt2),' epochs.']);  
          eval(plotfcn);
         end       
         break
       end
    end
    
   % this stops if using constrained optimization and goal is reached
    if ~isnan(errgoal)
     if ((gbestval<=errgoal) & (minmax==0)) | ((gbestval>=errgoal) & (minmax==1))  

         if plotflg == 1
             fprintf(message,i,gbestval);
             disp(' ');            
             disp(['--> Error Goal reached, successful termination!']);
             
             eval(plotfcn);
         end
         break
     end
     
    % this is stopping criterion for constrained from both sides    
     if minmax == 2
       if ((tr(i)<errgoal) & (gbestval>=errgoal)) | ((tr(i)>errgoal) ...
               & (gbestval <= errgoal))        
         if plotflg == 1
             fprintf(message,i,gbestval);
             disp(' ');            
             disp(['--> Error Goal reached, successful termination!']);            
             
             eval(plotfcn);
         end
         break              
       end
     end % end if minmax==2
    end  % end ~isnan if

 %    % convert back to inertial frame
 %     pos = pos - repmat(gbestoffset,ps,1);
 %     pbest = pbest - repmat(gbestoffset,ps,1);
 %     gbest = gbest + gbestoffset;
  

end  % end epoch loop

%% clear temp outputs
% evalin('base','clear temp_pso_out temp_te temp_tr;');

% output & return
 OUT=[gbest';gbestval];
 varargout{1}=[1:te];
 varargout{2}=[tr(find(~isnan(tr)))];
 
 return