I have code for BFO of Rosenbrock function given in matlab. Now I want to change into clustering where I have to associate Bacteria with my given cluster centers. How could I do it.

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Jyoti Arora 2016-10-18

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https://ww2.mathworks.cn/matlabcentral/answers/307821-i-have-code-for-bfo-of-rosenbrock-function-given-in-matlab-now-i-want-to-change-into-clustering-whe

编辑： Walter Roberson 2016-10-18

%%Bacterial Foraging Optimization
% Rolando Gonzales
% Bayesian Institute for Research on Development
% (http://www.bayesgroup.org)
% November, 2015
% ------------------------------------------------------------------------
% Based on Section 2.2 of,
%   Chen, Hanning, Yunlong Zhu, and Kunyuan Hu. 
%   "Cooperative bacterial foraging optimization." 
%   Discrete Dynamics in Nature and Society 2009 (2009)
% and the Bacterial Foraging code of Wael Korani,
% http://www.mathworks.com/matlabcentral/fileexchange/20217-bacterial-foraging
% 
% Currently the BFO code is programmed to optimize the two-variable 
% Rosenbrock function,
%       f(x,y) = (a-x)^2 + b*(y-x^2)^2
% (rose_fungraph plots a countourplot of the Rosenbrock function)
% To optimize other functions it is necessary to change, 
%       fitnessBFO.m
% ------------------------------------------------------------------------
clear; clc
% (1) Initialization
n  =  2;           % Dimension of search space
S  = 60;           % Number of bacteria in the colony
Nc = 25;           % Number of chemotactic steps 
Ns =  4;           % Number of swim steps 
Nre=  4;           % Number of reproductive steps 
Ned=  2;           % Number of elimination and dispersal steps
Sr =S/2;           % The number of bacteria reproductions (splits) per generation 
Ped=0.5;          % The probability that each bacteria will be eliminated/dispersed 
c(:,1)=0.05*ones(S,1);   % the run length unit (the size of the step taken in each run or tumble)
% Initial positions
for m=1:S                    % the initital posistions 
    B(1,:,1,1,1)= 10*rand(S,1)';
    B(2,:,1,1,1)= 10*rand(S,1)';
end  
%%Loops
% (2) Elimination-dispersal loop
for l = 1:Ned
    % (3) Reproduction loop
    for k = 1:Nre    
        % (4) Chemotaxis (swim/tumble) loop
        for j=1:Nc
            % (4.1) Chemotatic step
            for i=1:S 
                % (4.2) Fitness function
                J(i,j,k,l) = fitnessBFO(B(:,i,j,k,l));
                % (4.3) Jlast
                Jlast=J(i,j,k,l);
                % (4.4) Tumble
                Delta(:,i) = unifrnd(-1,1,n,1); 
                % (4.5) Move
                B(:,i,j+1,k,l)=B(:,i,j,k,l)+c(i,k)*Delta(:,i)/sqrt(Delta(:,i)'*Delta(:,i));
                % (4.6) New fitness function
                J(i,j+1,k,l)=fitnessBFO(B(:,i,j+1,k,l));
                % (4.7) Swimming
                m=0; % counter for swim length
                while m < Ns 
                    m=m+1;
                     if J(i,j+1,k,l)<Jlast  
                        Jlast=J(i,j+1,k,l);    
                        B(:,i,j+1,k,l)=B(:,i,j+1,k,l)+c(i,k)*Delta(:,i)/sqrt(Delta(:,i)'*Delta(:,i)) ;  
                        J(i,j+1,k,l)=fitnessBFO(B(:,i,j+1,k,l));  
                     else       
                        m=Ns;     
                     end 
                end
                J(i,j,k,l)=Jlast; %???
            end % (4.8) Next bacterium
            x = B(1,:,j,k,l);
            y = B(2,:,j,k,l);
            clf % clears figure 
                run rose_fungraph.m
                plot(x,y,'*','markers',6) % plots figure
                axis([-1.5 1.5 -1 3]), axis square
                xlabel('x'); ylabel('y')
                title('Bacterial Foraging Optimization'); grid on
                legend('Rosenbrock function','Bacteria')
                pause(.01)
                hold on
        end % (5) if j < Nc, chemotaxis
        % (6) Reproduction
        % (6.1) Health
        Jhealth=sum(J(:,:,k,l),2);      % Set the health of each of the S bacteria
        [Jhealth,sortind]=sort(Jhealth);% Sorts bacteria in order of ascending values
        B(:,:,1,k+1,l)=B(:,sortind,Nc+1,k,l); 
        c(:,k+1)=c(sortind,k);          % Keeps the chemotaxis parameters with each bacterium at the next generation
        % (6.2) Split the bacteria
        for i=1:Sr % Sr??
                B(:,i+Sr,1,k+1,l)=B(:,i,1,k+1,l); % 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 % (7) Loop to go to the next reproductive step
    % (8) Elimination-dispersal
        for m=1:S 
            if  Ped>rand % % Generate random number 
                B(1,:,1,1,1)= 50*rand(S,1)';
                B(2,:,1,1,1)= .2*rand(S,1)';  
            else 
                B(:,m,1,1,l+1)=B(:,m,1,Nre+1,l); % Bacteria that are not dispersed
            end        
        end 
end
%%Results
           reproduction = J(:,1:Nc,Nre,Ned);
           [jlastreproduction,O] = min(reproduction,[],2);  % min cost function for each bacterial 
           [Y,I] = min(jlastreproduction);
           pbest = B(:,I,O(I,:),k,l);
           display('Best solution:')
           display(['x = ' mat2str(pbest(1),2)])
           display(['y = ' mat2str(pbest(2),2)])
           plot(pbest(1),pbest(2),'ro')
           hold off
           legend('Rosenbrock function','Bacteria','Best solution')