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function [pop,pbest,gbest]=initializepso(num, bounds, evalFN,evalOps,options) 
% function [pop]=initializepso(populationSize, variableBounds,evalFN, 
%                           evalOps,options) 
%    initializega creates a matrix of random numbers with  
%    a number of rows equal to the populationSize and a number 
%    columns equal to the number of rows in bounds plus 1 for 
%    the f(x) value which is found by applying the evalFN. 
%    This is used by the ga to create the population if it 
%    is not supplied. 
% 
% pop            - the initial, evaluated, random population  
% populatoinSize - the size of the population, i.e. the number to create 
% variableBounds - a matrix which contains the bounds of each variable, i.e. 
%                  [var1_high var1_low; var2_high var2_low; ....] 
% evalFN         - the evaluation fn, usually the name of the .m file for  
%                  evaluation 
% evalOps        - any options to be passed to the eval function defaults [] 
% options        - options to the initialize function, ie.  
%                  [type prec] where eps is the epsilon value  
%                  and the second option is 1 for float and 0 for binary,  
%                  prec is the precision of the variables defaults [1e-6 1] 
 
%rand('seed',512341234) 
D=size(bounds,1); 
pop=zeros(num,2*D+1); 
diff=bounds(:,2)-bounds(:,1); 
for m=1:D 
    pop(:,m)=bounds(m,1)+rand(num,1)*diff(m); 
end 
for m=D+1:2*D 
    pop(:,m)=(rand(num,1)-0.5)*2*diff(m-D); 
end 
for m=1:num 
    eval(['[sol,val]=' evalFN '(pop(m,1:2),1);']); 
%    [sol,val]=fitness(pop(m,1:2),1); 
    pop(m,2*D+1)=val; 
end 
pbest(:,1:D)=pop(:,1:D); 
pbest(:,D+1)=pop(:,2*D+1); 
[y,n]=max(pop(:,2*D+1)); 
gbest(1,1:D)=pop(n,1:D); 
gbest(1,D+1)=pop(n,2*D+1);