matlab code

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% Main programs starts here
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=10;           % Number of dimensions 
% Lower limit/bounds/ a vector
Lb=-2*ones(1,d);
% Upper limit/bounds/ a vector
Ub=2*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,:);

% ======================================================  %
% Start the iterations -- Bat Algorithm (essential part)  %
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;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Sphere function with fmin=0 at (0,0,...,0)
z=sum(u.^2);
%%%%% ============ end ====================================

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