how can i correct the error regarding pareto optimal front?

%Function to be minimized
 D=3;
 Nobj=2;
 objf=inline('[0.0076818+0.00017*x1+0.0009*x2 +0.00145*x3 -0.000405*x1^2+0.000295*x2^2+0.000495*x3^2+0.000187*x1*x2,5235.61+59.10*x1 +896.80*x2+833.10*x3-956.77*x2^2+595.73*x3^2-436.13*x2*x3]','x1','x2','x3');
 objf=vectorize(objf);
 %Initialization of DE parameters
 N=20; %population size (total function evaluations will be itmax*N), 
 itmax=30;
 c1=1.05; c2=1.05;
 wmax=1; wmin=0.3; %set to same value for constant w
 w=linspace(wmax,wmin,itmax); %linear variation of w
 %Problem and velocity bounds
 a(1:N,1)=-1.0; 
 b(1:N,1)=1.0; 
 %bounds on variable x1
 a(1:N,2)=-1.0; 
 b(1:N,2)=1.0; 
 %bounds on variable x2
 a(1:N,3)=-0.9; 
 b(1:N,3)=1.1; 
 %bounds on variable x3
 d=(b-a);
 m=a; n=b; 
 q=(n-m)/4; %initial velocities are 1/4 of parameter space size
 %Random initialization of positions and velocities
 x=a+d.*rand(N,D);
 v=q.*rand(N,D);
 %Evaluate objective for all particles
 f=objf(x(:,1),x(:,2),x(:,3));
 %Find gbest (random point on pareto front)
 idx=findpareto(f);
 igbest=idx(round(rand(1)*(size(idx,1)-1))+1);
 fgbest=f(igbest,:);
 gbest=x(igbest,:);
 %Find pbest (update only if new f is on front and dominates old)
 for i=1:N
     if (find(idx==i))
         if (domination(f(i,:),fpbest(i,:))==1)
             fpbest(i,:)=f(i,:);
             pbest(i,:)=x(i,:);
         end
     end
 end
 %Iterate
 for it=1:itmax;
 %Update velocities and positions
v(1:N,1:D)=w(it)*v(1:N,1:D)+c1*rand*(pbest(1:N,1:D)-x(1:N,1:D))+c2*rand *(repmat(gbest,N,1)-x(1:N,1:D))
 x(1:N,1:D)=x(1:N,1:D)+v(1:N,1:D)
 %Evaluate objectives
f=objf(x(:,1),x(:,2),x(:,3));
%Find gbest and pbest
 [minf,iminf]=min(f);
 if minf<= fgbest
 fgbest=minf; gbest=x(iminf,:);
 end
 inewpb=find(f<=fpbest);
 pbest(inewpb,:)=x(inewpb,:); fpbest(inewpb)=f(inewpb);
 end %end loop on iterations
[gbest,fgbest]
pso= gbest;