this code of BAT algorithm is not working..function i wrote i last three lines..plz guide me

function [best,fmin,N_iter]=bat_algorithm(para)
% Display help
 help bat_algorithm.m

% Default parameters
if nargin<1,  para=[20 1000 0.5 0.5];  end
n=para(1);      % Population size, typically 10 to 40
N_gen=para(2);  % Number of generations
A=para(3);      % Loudness  (constant or decreasing)
r=para(4);      % Pulse rate (constant or decreasing)
% This frequency range determines the scalings
% You should change these values if necessary
Qmin=0;         % Frequency minimum
Qmax=2;         % Frequency maximum
% Iteration parameters
N_iter=0;       % Total number of function evaluations
% Dimension of the search variables
d=5;           % Number of dimensions 
% Lower limit/bounds/ a vector
Lb=-3*ones(1,d);
% Upper limit/bounds/ a vector
Ub=6*ones(1,d);
% Initializing arrays
Q=zeros(n,1);   % Frequency
v=zeros(n,d);   % Velocities
% Initialize the population/solutions
for i=1:n
  Sol(i,:)=Lb+(Ub-Lb).*rand(1,d);
  Fitness(i)=Fun(Sol(i,:));
end
% Find the initial best solution
[fmin,I]=min(Fitness);
best=Sol(I,:);

for t=1:N_gen, 
% Loop over all bats/solutions
        for i=1:n,
          Q(i)=Qmin+(Qmin-Qmax)*rand;
          v(i,:)=v(i,:)+(Sol(i,:)-best)*Q(i);
          S(i,:)=Sol(i,:)+v(i,:);
          % Apply simple bounds/limits
          Sol(i,:)=simplebounds(Sol(i,:),Lb,Ub);
          % Pulse rate
          if rand>r
          % The factor 0.001 limits the step sizes of random walks 
              S(i,:)=best+0.001*randn(1,d);
          end

       % Evaluate new solutions
             Fnew=Fun(S(i,:));
       % Update if the solution improves, or not too loud
             if (Fnew<=Fitness(i)) & (rand<A) ,
                  Sol(i,:)=S(i,:);
                  Fitness(i)=Fnew;
             end

            % Update the current best solution
            if Fnew<=fmin,
                  best=S(i,:);
                  fmin=Fnew;
            end
          end
          N_iter=N_iter+n;
  end
  % Output/display
  disp(['Number of evaluations: ',num2str(N_iter)]);
  disp(['Best =',num2str(best),' fmin=',num2str(fmin)]);

% Application of simple limits/bounds
function s=simplebounds(s,Lb,Ub)
  % Apply the lower bound vector
  ns_tmp=s;
  I=ns_tmp<Lb;
  ns_tmp(I)=Lb(I);

    % Apply the upper bound vector 
    J=ns_tmp>Ub;
    ns_tmp(J)=Ub(J);
    % Update this new move 
    s=ns_tmp;
  xdata =[ 10.^(-3) 10.^(-2) 10.^(-1) 10.^(0) 10.^(1) 10.^(2)]  ; 
  ydata=(0.5012./(1.9*xdata.^2+1.12*xdata+2));
   function fun=@(x)sum(x(1)./((x(2).*xdata.^1.1+x(3).*xdata.^0.1+1)- ydata).^2);

[Image Analyst]

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[ash]

function [best,fmin,N_iter]=bat_algorithm(para)

% Default parameters
if nargin<1,  para=[20 1000 0.5 0.5];  end
n=para(1);      % Population size, typically 10 to 40
N_gen=para(2);  % Number of generations
A=para(3);      % Loudness  (constant or decreasing)
r=para(4);      % Pulse rate (constant or decreasing)

% This frequency range determines the scalings

Qmin=0;         % Frequency minimum
Qmax=2;         % Frequency maximum

% Iteration parameters
N_iter=0;       % Total number of function evaluations

% Dimension of the search variables
d=5;           % Number of dimensions

% Lower limit/bounds/ a vector
Lb=-3*ones(1,d);

% Upper limit/bounds/ a vector
Ub=6*ones(1,d);

% Initializing arrays
Q=zeros(n,1);   % Frequency
v=zeros(n,d);   % Velocities

% Initialize the population/solutions
for i=1:n
  Sol(i,:)=Lb+(Ub-Lb).*rand(1,d);
  Fitness(i)=fun(Sol(i,:));
end

% Find the initial best solution
[fmin,I]=min(Fitness);
best=Sol(I,:);

for t=1:N_gen, 

% Loop over all bats/solutions
        for i=1:n,
          Q(i)=Qmin+(Qmin-Qmax)*rand;
          v(i,:)=v(i,:)+(Sol(i,:)-best)*Q(i);
          S(i,:)=Sol(i,:)+v(i,:);

            % Apply simple bounds/limits
            Sol(i,:)=simplebounds(Sol(i,:),Lb,Ub);

            % Pulse rate
            if rand>r
            % The factor 0.001 limits the step sizes of random walks 
                S(i,:)=best+0.001*randn(1,d);
            end

       % Evaluate new solutions
             Fnew=fun(S(i,:));

       % Update if the solution improves, or not too loud
             if (Fnew<=Fitness(i)) & (rand<A) ,
                  Sol(i,:)=S(i,:);
                  Fitness(i)=Fnew;
             end

            % Update the current best solution
            if Fnew<=fmin,
                  best=S(i,:);
                  fmin=Fnew;
            end
          end
          N_iter=N_iter+n;
  end

% Output/display
disp(['Number of evaluations: ',num2str(N_iter)]);
disp(['Best =',num2str(best),' fmin=',num2str(fmin)]);

% Application of simple limits/bounds
function s=simplebounds(s,Lb,Ub)

    % Apply the lower bound vector
    ns_tmp=s;
    I=ns_tmp<Lb;
    ns_tmp(I)=Lb(I);

    % Apply the upper bound vector 
    J=ns_tmp>Ub;
    ns_tmp(J)=Ub(J);

    % Update this new move 
    s=ns_tmp;

function z=fun(x,xdata,i)
xdata =[10.^(-5) 10.^(-4) 10.^(-3) 10.^(-2) 10.^(-1) 1];

ydata=(0.5012./(xdata.^1.9+2*xdata.^0.9+1 ));
fun = @(x) sum((x(1)./((x(2).*(xdata.^(1+0.9)))+x(3).*(xdata.^(0.9))+1) - ydata).^2);
%%%%%============ end ====================================