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function [x,endPop]=pso(bounds,w,c1,c2,termFns,termOps,evalFN,... 
evalOps,startPop,pbest,gbest); 
% PSO run a particle swarm algorithm 
% [x,endPop,bestPop,trace]=pso(bounds,w,c1,c2,termFns,termOps,evalFn,evalOps, 
%                               startPop,pbest,gbest); 
%                                 
% Output Arguments: 
%   x            - the best solution found during the course of the run 
%   endPop       - the final population  
%   bPop         - a trace of the best population 
%   traceInfo    - a matrix of best and means of the ga for each generation 
% 
% Input Arguments: 
%   bounds       - a matrix of upper and lower bounds on the variables 
%   w            - the inertia weight 
%   c1,c2        - the acceleration constants 
%   termFns      - name of the .m termination function (['maxGenTerm']) 
%   termOps      - options string to be passed to the termination function 
%                  ([100]). 
%   evalFn       - the name of the evaluation .m function 
%   evalOps      - options to pass to the evaluation function ([NULL]) 
%   startPop     - a matrix of solutions that can be initialized 
%                  from initialize.m 
%   pbest        - the initial best solution of each particle 
%   gbest        - the initial best solution of generation 1 
 
%  This program is written by Li Jun on Dec,15,2003.All rights reserved 
 
D    = size(bounds,1); 
num  = size(startPop,1); 
gen  = 1     ; 
done = 0     ; 
vnow = startPop(:,(D+1):2*D); 
xnow = startPop(:,1:D); 
xnew = xnow; 
pb   = pbest; 
gb   = gbest; 
while(~done) 
    %Caculate the fitness value of position xnow 
    xnow=xnew; 
    for m=1:num 
    eval(['[sol,val]=' evalFN '(xnow(m,1:2),1);']); 
    value(m,1)=val; 
    end 
    %replace the pbest 
    for m=1:num 
        if pb(m,D+1) < value(m,1) 
            pb(m,1:D)=xnow(m,1:D); 
            pb(m,D+1)=value(m,1); 
        end 
    end 
    %replace the gbest 
    [maxval,col]=max(value); 
    if gb(D+1) < maxval 
        gb=[xnow(col,:),maxval]; 
        bestg=gen; 
    end 
    %caculate the vnew and xnew 
    vnow = w*vnow+c1*rand*(pb(:,1:D)-xnow)+c2*rand*([gb(1)*ones(num,1),... 
           gb(2)*ones(num,1)]-xnow); 
    xnew = xnow+vnow; 
    if (100-gb(3)<1e-6) | (gen >= termOps) 
        done=1; 
    else 
        gen=gen+1; 
        w=w-0.01;    % w declines linearly 
        fprintf(1,'%d ',gen); 
    end 
end 
endPop=[xnow,value]; 
x=gb; 
fprintf(1,'\n w= %f',w); 
fprintf(1,'\n bestgen= %d',bestg);