Subscripted assignment dimension mismatch. Error in Untitled4 (line 80) ffmin(ite,run)=fmin; % storing best fitness

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Dear sir

i have this error

how can i solve it

((Subscripted assignment dimension mismatch))
Error in Untitled4 (line 80)
    ffmin(ite,run)=fmin;    % storing best fitness

the code is :-

%---------------------------------------------------------------------------------------------------------------------------------start 
tic
clc 
clear all
close all 
rng default  
LB=[0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05]; % lower bounds of variables
UB=[1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1]; % upper bounds of variables   
% pso parameters values 
m=14;          % number of variables 
n=800;          % population size 
wmax=0.9;      % inertia weight 
wmin=0.4;      % inertia weight
c1=1;        % acceleration factor 
c2=0.2;       % acceleration factor
% pso main program----------------------------------------------------start
maxite=10000;    % set maximum number of iteration
maxrun=10;      % set maximum number of runs need to be 
for run=1:maxrun    
    run   
    % pso initialization----------------------------------------------start     
    for i=1:n       
        for j=1:m            
            x0(i,j)=round(LB(j)+rand()*(UB(j)-LB(j)));         
        end
    end
    x=x0;       % initial population     
    v=0.1*x0;   % initial velocity 
    
    f0=cell(1,n);  %preallocation
    for i=1:n         
        f0{i}=ofun(x0(i,:));     
    end
    [fmin0,index0]=min(f0{i});
    pbest=x0;               % initial pbest    
    gbest=x0(index0,:);     % initial gbest    
    % pso initialization-----------------------------------------------end
    % pso algorithm---------------------------------------------------start    
    ite=1;         
    tolerance=1;     
    while ite<=maxite && tolerance>1e-12                
        w=wmax-(wmax-wmin)*ite/maxite; % update inertial weight          
        % pso velocity updates         
        for i=1:n            
            for j=1:m                 
                v(i,j)=w*v(i,j)+c1*rand()*(pbest(i,j)-x(i,j))...                        
                    +c2*rand()*(gbest(1,j)-x(i,j));             
            end
        end
        % pso position update
    for i=1:n             
        for j=1:m                
            x(i,j)=x(i,j)+v(i,j);             
        end
    end
     % handling boundary violations         
     for i=1:n             
         for j=1:m                 
             if x(i,j)<LB(j)                     
                 x(i,j)=LB(j);                
             elseif x(i,j)>UB(j)                     
                 x(i,j)=UB(j);                 
             end
         end
     end
     % evaluating fitness 
     f=cell(1,50);  %preallocation
     for i=1:n            
         f{i}=ofun(x(i,:));
    end
    % updating pbest and fitness         
    for i=1:n             
        if f{i}<f0{i}
             pbest(i,:)=x(i,:);                
             f0{i}=f{i};             
        end
    end
    [fmin,index]=min(f0{i});   
    % finding out the best particle         
    ffmin(ite,run)=fmin;    % storing best fitness         
    ffite(run)=ite;         % storing iteration count           
    % updating gbest and best fitness         
    if fmin<fmin0             
        gbest=pbest(index,:);             
        fmin0=fmin;         
    end
     % calculating tolerance 
     if ite>100;             
         tolerance=abs(ffmin(ite-100,run)-fmin0);         
     end
     % displaying iterative results         
     if ite==1             
         fprintf('Iteration    Best particle    Objective fun\n');         
     end
     fprintf('%8g  %8g          %8.4f\n',ite,index,fmin0);
     ite=ite+1;     
    end
      % pso algorithm---------------------------------------------------end     
  
      fvalue=2.633*x(1)+2.992*x(2)+3.134*x(3)+3.678*x(4)+3.620*x(5)+2.948*x(6)+1.607*x(7)+2.952*x(8)+3.348*x(9)+3.680*x(10)+3.774*x(11)+2.995*x(12)+3.237*x(13)+1.608*x(14);
    
      fff(run)=fvalue     
      rgbest(run,:)=gbest;     
      fprintf('--------------------------------------\n'); 
end 
% pso main program------------------------------------------------------end 
fprintf('\n\n'); 
fprintf('*********************************************************\n'); 
fprintf('Final Results-----------------------------\n'); 
[bestfun,bestrun]=min(fff) 
best_variables=rgbest(bestrun,:) 
fprintf('*********************************************************\n'); 
toc
  % PSO convergence characteristic 
  plot(ffmin(1:ffite(bestrun),bestrun),'-k');
  xlabel('Iteration'); 
  ylabel('Fitness function value'); 
  title('PSO convergence characteristic') 
  