function [aaa,Ck,bestf]=gloa(func,par,ra,rb)
%this is the main gloa function. func:fucntion name; par:number of
%parameters; ra:lower bound for solution space; rb:upper bound for solution
%space
%for convenience, the output can be put in a file instead of disp.
% example call [aa,c,b]=gloa(@Rosenbrock,50,-100,100);
% tic
fnc=func;%str2func(func); %func to be optimized
iter=2000; %Max iteration

gr=10;%number of groups
ch=25;%Population in each group
pr=par;%number of parameters
r2=0.1;%the rates for the leader, itself and the random in mutation
r1=0.8;
r3=1-r1-r2;
q=par/2+1;%*0.5;%determines the transfer rate
a=ra; b=rb;%solution space
aaa=[0 0];%keeps the output
c=(b-a);
x=a+c*rand(ch,pr,gr); %initial populations
%initial evaluation of the population
for i=1:gr
    for j=1:ch
     F(j,1,i)=fnc(x(j,:,i));
    end
end
%leaders are assigned to each group
for i=1:gr;
         [~,I]=min(F(:,1,i));
         Father(:,:,i)=x(I,:,i);
end

%The main body of the algorithm
for it=1:iter;
  for i=1:gr;
   
      for j=1:ch; 
    %the mutation step
          A = (r1)*x(j,:,i)+r2*Father(:,:,i)+r3*(a+c*rand);
          NEW1=fnc(A);   
           if(NEW1<F(j,1,i))
               x(j,:,i)=A;
           end
           F(j,1,i)=fnc(x(j,:,i));  
%  For non-numerical problems example set up:        
%             A= x{i,j};
%            Leader_elements=ceil(gen*rand(r2,1));
%            A(leader_elements) = leader{i}(leader_elements);
%            random_member = random create
%            random_element=ceil(gen*rand(r3,1));%to generate indices
%            A(random_element)=random_member(random_element);
%            A_fitness=fitness(A,n);
%            if( A_fitness < F(i,j))
%                x{i,j}=A;
%                F(i,j)=A_fitness;
%            end
     end;
      
  end

  for i=1:gr;
         %the random transfer step
       crosay=ceil(rand*q);
       for cro=1:crosay
        crospr=ceil(pr*rand);
        crosgr=ceil(gr*rand);
        crospop=ceil(ch*rand);
        gco=x(crospop,:,i);
        Cros=x(crospop,crospr,crosgr);
        gco(crospr)=Cros;
	%%F(crospop,1,i)=fnc(x(crospop,:,i));
        NEW2=fnc(gco);       
        if(NEW2<F(crospop,1,i))
          x(crospop,:,i)=gco;
          F(crospop,1,i)=NEW2;
        end
       end
  end
  for i=1:gr;
         [~,I]=min(F(:,1,i));
         Father(:,:,i)=x(I,:,i);
  end
    [bestf,I]=min(min(F(:,1,:)));
    Ck=Father(:,:,I);
    
aaa=[aaa;it bestf];

disp([int2str(it) '.iteration:' mat2str(bestf)])


end
% s=toc