Error in implementing particle swarm optimization in power system using MATLAB [closed]

I'm trying to get the solution of economic load despatch using particle swarm optimization. I've written the code but during updating the velocity and particle position in the second iteration, it does not generate the power value of generator which adds up to 850. Somehow the algorithm isn't converging. Please try to run it in MATLAB

 clear all
 clc
 %Initialization
 c1 = 2.0, c2 = 2.0
 wmax = 0.9, wmin = 0.4
 count = 0,countv=0
 p1min = 100,p1max = 600
 p2min = 50,p2max = 200
 p3min = 100,p3max = 400
 v1min = -50, v1max = 300
 v2min = -25, v2max = 100
 v3min = -50, v3max = 200
 pd = 850                     
 counter=0

 for i=1:10000
sum1=0;
if count<10
g(i-counter,1) = (p1max - p1min)*rand() + p1min
g(i-counter,2) = (p2max - p2min)*rand() + p2min
g(i-counter,3) = (p3max - p3min)*rand() + p3min
sum1=round(g(i-counter,1)+g(i-counter,2)+g(i-counter,3))
    if sum1==pd
        count=count+1;
    else
        g(i-counter,:)=[]
        counter=counter+1;
    end
 end
end




 for i=1:10                                                   %COST FUNCTION FOUND
    f(i,1) = 0.0016*(g(i,1))^2+7.92*g(i,1)+561 + 0.0048*(g(i,2))^2+ 7.92*g(i,2) + 78 +    0.0019*(g(i,3))^2 + 7.85*g(i,3) + 310
    end



total1 = [f g]


 for i=1:10                                                         %Intial VELOCITY OBTAINED
         v(i,1)= v1min + rand()*(v1max-v1min)
         v(i,2)= v2min + rand()*(v2max-v2min)
         v(i,3)= v3min + rand()*(v3max-v3min)
         countv = countv +1
         if countv == 10
             break
         end

     end

 ginti = 0
 for k=1:100

 w=wmax - ((wmax-wmin)/100)*k                   %K TO BE GET FROM FINAL LOOP

 gbest=min(total1(:,:))                                           %PBEST AND GBEST FOUND
 pbest=total1(:,:)
       counter = 0
        count = 0  


                                                                            %VELOCITY IS TO BE UPDATED
 for i=1:100000
     if count<10
     vnew(i-counter,1) = w*v(i-counter,1) + c1*rand()*(pbest(i-counter,2)-g(i-counter,1)) + c2*rand()*(gbest(1,2)-g(i-counter,1))
     vnew(i-counter,2) = w*v(i-counter,2) + c1*rand()*(pbest(i-counter,3)-g(i-counter,2)) + c2*rand()*(gbest(1,3)-g(i-counter,2))
     vnew(i-counter,3) = w*v(i-counter,3) + c1*rand()*(pbest(i-counter,4)-g(i-counter,3)) + c2*rand()*(gbest(1,4)-g(i-counter,3))

      gnew(i-counter,1) = g(i-counter,1) + vnew(i-counter,1)
      gnew(i-counter,2) = g(i-counter,2) + vnew(i-counter,2)
      gnew(i-counter,3) = g(i-counter,3) + vnew(i-counter,3)

     if gnew(i-counter,1)<=600 & gnew(i-counter,1)>=100 & gnew(i-counter,2)>=50 & gnew(i-counter,2)<=200& gnew(i-counter,3)>=100 & gnew(i-counter,3) <=400 & round(gnew(i-counter,1) + gnew(i-counter,2) + gnew(i-counter,3))==pd
        count = count + 1;
     else 
         vnew(i-counter,:) = []
         gnew(i-counter,:) = []
         counter = counter+1;
     end
     end 

  end


    for i=1:10                                                   % UPDATED COST FUNCTION FOUND
    fnew(i,1) = 0.00156*(gnew(i,1))^2+7.92*gnew(i,1)+561 + 0.00194*(gnew(i,2))^2+ 7.85*gnew(i,2) + 310 + 0.00482*(gnew(i,3))^2 + 7.97*gnew(i,3) + 78
    end

    total2 = [fnew gnew]

    % COMPARISON OF THE TWO TABLES
      for i=1:10
          if (total1(i,1)<=total2(i,1))
              total3(i,:)= total1(i,:)
          else
              total3(i,:)= total2(i,:)
          end
      end
      total1=total3
      g(:,1)=total3(:,1)
      g(:,2)=total3(:,2)
      g(:,3)=total3(:,3)
      ginti= ginti + 1
 end