genetic algorithm on solving hfsp

Here's my code of using ga to find the optimum schedule of the hybrid flow shop problem.
The question here is that I can't get the correct convergence line showed by LC1&LC2 at the end.
I don't see where go wrong. Could anyone maybe have a look at the code? Many thx!

function [Zp,Y1p,Y2p,Y3p,Xp,bp,LC1,LC2]=hfsp(M,N,Er,Pc,Pm,T,P)]
%1: variable initialization
[m,n]=size(T)
Xp=zeros(m,n);
bq=zeros(1,m);
LC1=zeros(1,M);
LC1=zeros(1,M);
%2: random generation of initial population
farm=cell(1,N);
farm_p=cell(1,N);
for k=1:N
    X=zeros(m,n);
    priority=randperm(m);
   for r=1:n
       for h=1:m
           X(h,r)=1+(P(r)-eps)*rand;
       end
   end
  farm{k}=X;
  farm_p{k}=priority;
end

fitness=zeros(1,N);
farm_sel=cell(1,N);

counter=0;
sum_fitness=0;
while counter<M



for i=1:N
    X=farm{i};
    priority=farm_p{i};
  
[Zp,Y1p,Y2p,Y3p]=MakeSpan1(X,T,priority);
      Z=Zp;
  fitness(i)=1./Z;
    end

%3: elitism
Elite_no=round(N*Er);
[max_val,indx]=sort(fitness,'descend');
for s=1:Elite_no
    farm_sel{s}=farm{indx(s)};
    fitness(s)=fitness(indx(s));
end
for s=Elite_no+1:N
    farm_sel{s}=farm{s};
    fitness(s)=fitness(s);
end
farm_elite=farm_sel;

%4: selection operator

  CumuProb=zeros(1,N);  
   for i=1:N
       CumuProb(i)=sum(fitness(1:i));
   end
   MaxProb=CumuProb(N);
for j=1:N
    pChosen=rand*MaxProb;
    Ind=find(CumuProb>pChosen,1);
  
          farm_sel{j}=farm{Ind};
          
      end
   
 
  newfarm=farm_sel;
  
  %4: crossover operator
  Ser=randperm(N);
  for i=1:2:(N-1)
      A=farm{Ser(i)};
      B=farm{Ser(i+1)};
      if rand<Pc
          cp=unidrnd(m-1);
          a=[A(1:cp,:);B((cp+1):m,:)];
          b=[B(1:cp,:);A((cp+1):m,:)];
              newfarm{i}=a;
              newfarm{i+1}=b;
      else
      newfarm{i}=A;
      newfarm{i+1}=B;
      end
      end
      farm_2=newfarm;
      
%5: mutation operator
for i=1:N
    if rand<Pm
        X=farm_2{i};
        I=unidrnd(m);
        J=unidrnd(n);
        X(I,J)=1+(P(J)-eps)*rand;
        farm_2{i}=X;
        farm_3{i}=farm_2{i};
    else
        farm_3{i}=farm_2{i};
    end
end
farm_3=farm_2;

for i=1:N
    X=farm_3{i};
    priority=farm_p{i};
    [Zp,Y1p,Y2p,Y3p]=MakeSpan1(X,T,priority);
  
    fitness(i)=1./Zp;
   
end
Cmin=min(Zp);

LC3(counter+1)=Cmin;

max_fitness=max(fitness);
mean_fitness=mean(fitness);
LC1(counter+1)=max_fitness;
LC2(counter+1)=mean_fitness;
pos=find(fitness==max_fitness);
Xp=farm_3{pos(1)};
X=Xp;
bp=farm_p{N};

[Zp,Y1p,Y2p,Y3p]=MakeSpan1(X,T,priority);
counter=counter+1;
for g=1:Elite_no
farm_3{g}=farm_elite{g};

farm=farm_3;
end
end
figure(2)
plot(LC3);


figure(1);
plot(LC1);
hold on
plot(LC2,'r');


end