Particle swarm optimization implementation error

Question

Odesanmi Gbenga Abiodun 2019-11-1

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/488642-particle-swarm-optimization-implementation-error

评论： Walter Roberson 2019-11-9

@all please I need your suggestion and help on this code using pso

for num_acur= 1:1:num_aopt
    options=optimset('MaxIter',400);
    a_init=1*rand(4,1);
    [a_opt, Fval, exitFlag]=fminunc(@W,a_init,options); 
    a_result(num_acur,:)=[a_opt',Fval,a_init',exitFlag];
end

Rewriting this Using Pso

for num_acur = 1:1:num_aopt
    fun = @W;
    lb = [-30,-30,-30,-30];
    ub =[100,10,10,10];
   % options = optimoptions('particleswarm','SwarmSize',100);
   %options = optimoptions('particleswarm','SwarmSize',50,'HybridFcn',@fmincon);
   nvars = 4;
   [a_opt, Fval, exitFlag] = particleswarm(fun,nvars,lb,ub);%,options);
   a_result(num_acur,:)=[a_opt',Fval,lb',exitFlag];
end

Error

Error using horzcat

Dimensions of arrays being concatenated are not consistent.

Error in code ()

a_result(num_acur,:)=[a_opt',Fval,lb',exitFlag];

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Answer 1

Walter Roberson 2019-11-1

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/488642-particle-swarm-optimization-implementation-error#answer_399267

在 MATLAB Online 中打开

   [a_opt, Fval, exitFlag] = particleswarm(fun,nvars,lb,ub);%,options);
   a_result(num_acur,:)=[a_opt',Fval,lb',exitFlag];
   

The first output from particleswarm is a row vector with length equal to the number of variables -- so 1 x 4 in your case. The second output is the scalar function result.

You use conjugate transpose on the 1 x 4, which will give you a 4 x 1. You use it with [] with the scalar Fval, so you are trying to horizontally concatenate a vector with 4 rows and a vector with 1 row. That is not a permitted operation.

You need to either not transpose a_opt and lb, or else you need to use ; instead of , in the [] so that you construct a column.

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Odesanmi Gbenga Abiodun 2019-11-9

在 MATLAB Online 中打开

No, their is another function for the nested x

Tota_number =find(a_result(:,5)==min(a_result(:,5)));
a_prama_tra=a_result(Tota_number,1:4);
a_prama = a_prama_tra
%[opt_x_current, fun_negEI, exitFlag]=fmincon(@nested,opt_x_init,[-1,1.6;1,-1.6],[-0.2;1],[],[],[G_low,E_low],[G_high,E_high],[]); 
[opt_x_current, fun_negE, exitFlag]=particleswarm(@nested,2,lb,ub);
opt_x_nested=opt_x_current;
Bys_flag=0;
x=[x,opt_x_nested];
function  Kern_Cov_xixj=Aug(xi,xj)
        global a_prama       
        Kern_Cov_xixj=(a_prama(1)).^2*exp(-(1/2)*((xi-xj)'*inv(diag([(a_prama(2)).^2,( a_prama(3)).^2]))*(xi-xj)))+a_prama(4).^2*uita(xi,xj); 
        
end
function E_ne_o=nested(X_opt)
global  x y E_m E_v 
y_n=size(y,1); 
K=ones(y_n,y_n);
for i=1:1:y_n
    for j=1:1:y_n
        K(i,j)=Aug(x(:,i),x(:,j));     
    end
end
 k=ones(y_n,1);
 for i=1:1:y_n
     k(i,1)=Aug(X_opt,x(:,i)); 
 end
           
           E_m=k'*inv(K)*y;
           E_v=Aug(X_opt,X_opt)-k'*inv(K)*k;
           if(E_v>0)
               f_best=min(y);
               u_star=(f_best-E_m)/sqrt(E_v);
               E_o=(f_best-E_m).*normcdf(u_star)+sqrt(E_v).*normpdf(u_star);
           else
                E_o=0;
           end
            E_ne_o=-E_o;   
           
end

Walter Roberson 2019-11-9

There would need to be a function header at the top and an end at the bottom of that in order for that to make use of nested functions and shared variables.

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