bg_pso.m

一款埃及学生用Matlab编的的基于PSO的PID整定程序

%% Tunning of PID controller using Bacterial Foraging Orientec by Particle swarm optimization 
%
%
%My work has been accpepted in GECCO 2008 as Graduat Student workshop. I
%have used this techique in PID tunning and i got better result thatn BG
%and PSO
%
% Author: Wael Mansour ([e-mail])
%
% MSc Student, Electrical Enginering Dept, 
% Faculty of Engineering Cairo University, Egypt





%%
%Initialization
clear all   
clc
p=2;                         % dimension of search space 
s=10;                        % The number of bacteria 
Nc=10;                       % Number of chemotactic steps 
Ns=4;                        % Limits the length of a swim 
Nre=4;                       % The number of reproduction steps 
Ned=2;                       % The number of elimination-dispersal events 
Sr=s/2;                      % The number of bacteria reproductions (splits) per generation 
Ped=0.25;                    % The probabilty that each bacteria will be eliminated/dispersed 
c(:,1)=0.5*ones(s,1);       % the run length
for i = 1:s 
    Delta(:,i)=(2*round(rand(p,1))-1).*rand(p,1);  
end 
for m=1:s                    % the initital posistions 
    P(1,:,1,1,1)= 50*rand(s,1)';
    P(2,:,1,1,1)= 0.2*rand(s,1)';
   %P(3,:,1,1,1)= .2*rand(s,1)';
end                                                                  
c1 =1.2;                                        %  PSO parameter C1 1.2
c2 = 0.5;                                       %  PSO parameter C2 .5
R1 = rand(p,s);                                 %  PSO parameter
R2 = rand(p,s);                                 %  PSO parameter
Plocal_best_position=0*ones(p,s,Nc);            %  PSO                       
Pglobal_best_position=0*ones(p,s,Nc);           %  PSO
velocity = .3*randn(p,s);                       %  PSO                        
current_position=0*ones(p,s,Nc);                %  PSO

%%
%Main loop 
    

%Elimination and dispersal loop 
for ell=1:Ned
    

%Reprodution loop


    for K=1:Nre    

%  swim/tumble(chemotaxis)loop   

        for j=1:Nc
            
            for i=1:s        
                J(i,j,K,ell)=tracklsq(P(:,i,j,K,ell));         

% Tumble
                    
                Jlast=J(i,j,K,ell);
                Jlocal(i,j)=Jlast; 	             	
                P(:,i,j+1,K,ell)=P(:,i,j,K,ell)+c(i,K)*Delta(:,i); % This adds a unit vector in the random direction            
 
% Swim (for bacteria that seem to be headed in the right direction)     
                
                J(i,j+1,K,ell)=tracklsq(P(:,i,j+1,K,ell));  
                m=0;         % Initialize counter for swim length 
                    while m<Ns     
                          m=m+1;
                          if J(i,j+1,K,ell)<Jlast  
                             Jlast=J(i,j+1,K,ell);    
                             P(:,i,j+1,K,ell)=P(:,i,j+1,K,ell)+c(i,K)*Delta(:,i) ;  
                             J(i,j+1,K,ell)=tracklsq(P(:,i,j+1,K,ell));
                             current_position(:,i,j+1)= P(:,i,j+1,K,ell) ;          %   PSO
                             Jlocal(i,j+1) = J(i,j+1,K,ell) ;                       %   PSO
                          else
                             Jlocal(i,j+1) = J(i,j+1,K,ell) ;
                             current_position(:,i,j+1)= P(:,i,j+1,K,ell);
                             m=Ns ;     
                          end        
                    
                    end 
      
                sprintf('The value of interation i %3.0f ,j = %3.0f  , K= %3.0f, ell= %3.0f' , i, j, K ,ell );
                   
            end % Go to next bacterium
            
%For each chemotactic evaluate the local and global best position

%Local best position over all chemotactic that each bacteria move through it
            [Jmin_for_each_chemotactic,index]=min(Jlocal,[],2);
            for m=1:s 
                Plocal_best_position(:,m,j) = current_position(:,m,index(m,:));    
            end
%Global best position over all chemotactic and for each bacteria  
            [Y,I]=min(Jmin_for_each_chemotactic);
            global_best_position =current_position(:,I,index(I,:));
            for m =1:s  
                Pglobal_best_position(:,m,j)=global_best_position;
            end 
%Caluculate the new direction for each bacteria           
velocity =.9* velocity + c1*(R1.*(Plocal_best_position(:,:,j)-current_position(:,:,j+1))) + c2*(R2.*(Pglobal_best_position(:,:,j)-current_position(:,:,j+1)));
Delta    = velocity ;

        end  % Go to the next chemotactic    

                 
%Reprodution                                              
        Jhealth=sum(J(:,:,K,ell),2);              % Set the health of each of the S bacteria
        [Jhealth,sortind]=sort(Jhealth);          % Sorts the nutrient concentration in order of ascending 
        P(:,:,1,K+1,ell)=P(:,sortind,Nc+1,K,ell); 
        c(:,K+1)=c(sortind,K);                    % And keeps the chemotaxis parameters with each bacterium at the next generation
                                     

%Split the bacteria (reproduction)                             
            for i=1:Sr
                P(:,i+Sr,1,K+1,ell)=P(:,i,1,K+1,ell); % The least fit do not reproduce, the most fit ones split into two identical copies  
                c(i+Sr,K+1)=c(i,K+1);                 
            end   
        end %  Go to next reproduction    


%Eliminatoin and dispersal
        for m=1:s 
            if  Ped>rand % % Generate random number 
                P(1,:,1,1,1)= 0.2*rand(s,1)';
                P(2,:,1,1,1)= 0.2*rand(s,1)';
               %P(3,:,1,1,1)= .2*rand(s,1)';   
            else 
                P(:,m,1,1,ell+1)=P(:,m,1,Nre+1,ell); % Bacteria that are not dispersed
            end        
        end 
    end % Go to next elimination and disperstal 

%Report
           reproduction = J(:,1:Nc,Nre,Ned);
           [jlastreproduction,O] = min(reproduction,[],2);  % min cost function for each bacterial 
           [Y,I] = min(jlastreproduction);
           pbest=P(:,I,O(I,:),K,ell);
            Kp= pbest(1,:)
           Kd=pbest(2,:)