Why does this code give me an error?

Question

Sadiq Akbar 2024-2-24

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2086293-why-does-this-code-give-me-an-error

评论： Rena Berman 2024-3-15

I want to run the Wild Horse Optimizer (WHO) with my fitness function. It will give me an estimated solution "gBest". Then I want to supply that estimated solution to the local builtin optimizer "sqp" while providing an initial guess as a starting point. My code is below:

clear;clc
u=[-33 33];% desired vector
dim=length(u);
lb=-90*ones(1,dim);
ub= 90*ones(1,dim);
Noise=0;
% Define the objective function
objectiveFunctionForMetaheuristic = @(x) MIMOfunNoise(x,u,Noise) + penaltyTerm(x, u);
objectiveFunctionForLocal = @(x) MIMOfunNoise(x, u, Noise) + penaltyTerm(x, u);
% Initial guess 
initialGuess = u;
% Set options for fmincon (SQP solver)
options = optimoptions('fmincon', 'Algorithm', 'sqp', 'Display', 'off');
Runs=30;
% Pre-allocation for WHO algorithm
one=zeros(Runs,1);
time=zeros(Runs,1);
temp=zeros(Runs,dim);
two=zeros(Runs,dim);
% Pre-allocation for SQP algorithm
oneSQP=zeros(Runs,1);
timeSQP=zeros(Runs,1);
tempSQP=zeros(Runs,dim);
twoSQP=zeros(Runs,dim);
nn=0;
for n=1:Runs %------------(2)
    nn=nn+1;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Call WHO 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%[time,gBest,gBestScore]=WHO(N,Max_iter,lb,ub,dim,objectiveFunction);
[time,gBest,gBestScore]=WHO(30,500,lb,ub,dim,objectiveFunctionForMetaheuristic);
one(nn)=gBestScore;
temp(nn,:)=gBest;
time1(nn)=time;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%- Swapping
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[~, ix] = sort(u); 
[~, ix1(ix)] = sort(temp(nn,:));  
two(nn,:) = temp(nn,ix1); 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Call the SQP
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
tic;
%[bestX, fmin] = fmincon(objectiveFunctionForLocal, gBest, [], [], [], [], lb, ub, [], options);
objectiveFunctionForLocal(gBest)
[c,ceq] = nonlinear_constraint(gBest)
[bestX, fmin] = fmincon(objectiveFunctionForLocal, gBest, [], [], [], [], lb, ub, @(x) nonlinear_constraint(x), options, initialGuess);
timeSQP(nn)=toc;
oneSQP(nn)=fmin;
tempSQP(nn,:)=bestX;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%- Swapping
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[~, ix] = sort(u); % u is my desired vector
[~, ix1(ix)] = sort(tempSQP(nn,:));  
twoSQP(nn,:) = tempSQP(nn,ix1); 
end
ans = 1.2321
Unrecognized function or variable 'nonlinear_constraint'.
%%%%%%%%%%%%%%%%%%%
% Choose best
%%%%%%%%%%%%%%%%%%%
[one1 ind]=sort(one,'descend');
[fitness,ind1]=min(one1);
two1=two(ind1,:);
[one1SQP indSQP]=sort(oneSQP','descend');
[fitnessSQP,ind1SQP]=min(one1SQP);
two1SQP=twoSQP(ind1SQP,:);
% Display results
format compact
fprintf('Global_Sol  : %s\n', num2str(round(two1, 4),'%.4f '));
fprintf('Local_Sol   : %s\n', num2str(round(two1SQP,4),'%.4f '));
fprintf('Desired     : %s\n', num2str(u,'%.4f '));
fprintf('Global_fmin : %f\n', fitness);
fprintf('Local_fmin  : %f\n', fitnessSQP);
%%%%%%%%%%%%%%%%%%
% Save workspace data
%%%%%%%%%%%%%%%%%
%save 2sn0dB
function e=MIMOfunNoise(b,u,Noise)
M=6; % const1
N=6; % const2
K=length(u);%const3
d = 0.5; % const4
vec = @(MAT) MAT(:); 
vecH = @(MAT) MAT(:).'; 
steerVecT = @(ang) exp(1j*2*pi*d*(0:M-1).'*sin(vecH(ang))); 
steerVecR = @(ang) exp(1j*2*pi*d*(0:N-1).'*sin(vecH(ang))); 
%%%%%%%%%%%%%%%%%%%%
% Swapping vector b
%%%%%%%%%%%%%%%%%%%%
[~, ix] = sort(u); 
[~, ix1(ix)] = sort(b);  
b = b(ix1);
A = ones(K, 1);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Calculation of yo and ye
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
yo = yMatTR(deg2rad(u), steerVecT, steerVecR); 
yo=awgn(yo,Noise);
ye = yMatTR(deg2rad(b), steerVecT, steerVecR); 
%%%%%%%%%%%%%%%%%%
% MSE
%%%%%%%%%%%%%%%%%%
e=norm(yo-ye).^2/(M*N);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% User defined function called within MIMOfunNoise
%%%%%%%%%%%%%%%%%%%%%%%%5%%%
function y = yMatTR(targetAngle, steerVecT, steerVecR)
   steerA = steerVecT(targetAngle); 
   steerB = steerVecR(targetAngle); 
   y=sum( steerA.*permute(steerB,[3,2,1]) ,2);
   y=y(:);   
end
function penalty = penaltyTerm(x, desiredResult)
    % Define a penalty term based on the deviation from the desired result
    penalty = 100 * sum((x - desiredResult).^2);
end
function [X]=initialization(N,dim,up,down)
if size(up,1)==1
    X=rand(N,dim).*(up-down)+down;
end
if size(up,1)>1
    for i=1:dim
        high=up(i);low=down(i);
        X(:,i)=rand(1,N).*(high-low)+low;
    end
end
end
function Stallion=exchange(Stallion)
nStallion=length(Stallion);
for i=1:nStallion 
   [value,index]=min([Stallion(i).group.cost]);
  
   if value<Stallion(i).cost
       
       bestgroup=Stallion(i).group(index);
       
       Stallion(i).group(index).pos=Stallion(i).pos;
       Stallion(i).group(index).cost=Stallion(i).cost;
       
       
       Stallion(i).pos=bestgroup.pos;
       Stallion(i).cost=bestgroup.cost;
   end   
end
end
% Developed in MATLAB R2017b
% Source codes demo version 1.0
% _____________________________________________________
%  Author, inventor and programmer: Iraj Naruei and Farshid Keynia,
%  e-Mail: irajnaruei@iauk.ac.ir , irajnaruei@yahoo.com
% _____________________________________________________
%  Co-author and Advisor: Farshid Keynia
%
%         e-Mail: fkeynia@gmail.com
% _____________________________________________________
%  Co-authors: Amir Sabbagh Molahoseyni
%        
%         e-Mail: sabbagh@iauk.ac.ir
% _____________________________________________________
% You can find the Wild Horse Optimizer code at 
% _____________________________________________________
%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Max_iter: maximum iterations, N: populatoin size, Convergence_curve: Convergence curve
%function [Convergence_curve,gBest,gBestScore]=WHO(N,Max_iter,lb,ub,dim,fobj)
function [time,gBest,gBestScore]=WHO(N,Max_iter,lb,ub,dim,fobj)
tic; % By Me
if size(ub,1)==1
    ub=ones(1,dim).*ub;
    lb=ones(1,dim).*lb;
end
 PS=0.2;     % Stallions Percentage 
 PC=0.13;    % Crossover Percentage
 NStallion=ceil(PS*N); % number Stallion
 Nfoal=N-NStallion;
Convergence_curve = zeros(1,Max_iter);
gBest=zeros(1,dim);
 gBestScore=inf;
 
 %create initial population
 empty.pos=[];
empty.cost=[];
group=repmat(empty,Nfoal,1);
for i=1:Nfoal 
   group(i).pos=lb+rand(1,dim).*(ub-lb);
   group(i).cost=fobj(group(i).pos);
end
Stallion=repmat(empty,NStallion,1);
for i=1:NStallion 
   Stallion(i).pos=lb+rand(1,dim).*(ub-lb);
   Stallion(i).cost=fobj(Stallion(i).pos);
 
end
  ngroup=length(group);
  a=randperm(ngroup);
  group=group(a);
i=0;
k=1;
for j=1:ngroup
i=i+1;    
  Stallion(i).group(k)=group(j);  
if i==NStallion
    i=0;
    k=k+1;
end
end
Stallion=exchange(Stallion);
[value,index]=min([Stallion.cost]);
WH=Stallion(index); % global
   gBest=WH.pos;
   gBestScore=WH.cost;
Convergence_curve(1)=WH.cost;
l=2; % Loop counter
while l<Max_iter+1
TDR=1-l*((1)/Max_iter);
for i=1:NStallion
    
   ngroup=length(Stallion(i).group);
    [~,index]=sort([Stallion(i).group.cost]);
    Stallion(i).group=Stallion(i).group(index);
   
   for j=1:ngroup
    
    if rand>PC
              z=rand(1,dim)<TDR;
            r1=rand;
            r2=rand(1,dim);
            idx=(z==0);
            r3=r1.*idx+r2.*~idx;
           rr=-2+4*r3;
           Stallion(i).group(j).pos= 2*r3.*cos(2*pi*rr).*(Stallion(i).pos-Stallion(i).group(j).pos)+(Stallion(i).pos);
    else
    A=randperm(NStallion);
    A(A==i)=[];
    a=A(1);
    c=A(2);
%     B=randperm(ngroup);
%     BB=randperm(ngroup);
%     b1=B(1);b2=BB(1);
    x1=Stallion(c).group(end).pos;
    x2=Stallion(a).group(end).pos;
       y1=(x1+x2)/2;   % Crossover
    Stallion(i).group(j).pos=y1;
    end
   
    Stallion(i).group(j).pos=min(Stallion(i).group(j).pos,ub);
    Stallion(i).group(j).pos=max(Stallion(i).group(j).pos,lb);
    
    Stallion(i).group(j).cost=fobj(Stallion(i).group(j).pos);
    
    
   end
    
% end
% 
% for i=1:NStallion
    R=rand;
%     z=rand(1,dim)<TDR;
%             r1=rand;
%             r2=rand(1,dim);
%             idx=(z==0);
%             r3=r1.*idx+r2.*~idx;
%             rr=-2+4*r3;
       if R<0.5
        k= 2*r3.*cos(2*pi*rr).*(WH.pos-(Stallion(i).pos))+WH.pos;
        else
        k= 2*r3.*cos(2*pi*rr).*(WH.pos-(Stallion(i).pos))-WH.pos;
       end
    k=min(k,ub);
    k=max(k,lb);
    fk=fobj(k);
    if fk<Stallion(i).cost
      Stallion(i).pos  =k;
      Stallion(i).cost=fk;
    end
end
    Stallion=exchange(Stallion);
     [value,index]=min([Stallion.cost]);
   if value<WH.cost
       WH=Stallion(index);
   end
   gBest=WH.pos;
   gBestScore=WH.cost;
    Convergence_curve(l)=WH.cost;
    l = l + 1;
    time=toc; % By Me
end
end

The WHO and its supported files are in the attachment. But when I run the above code, it gives me an error like this:

Error using abcd>@(x)MIMOfunNoise(x,u,Noise)+penaltyTerm(x,u)

Too many input arguments.

Error in fmincon (line 563)

initVals.f = feval(funfcn{3},X,varargin{:});

Error in abcd (line 61)

[bestX, fmin] = fmincon(objectiveFunctionForLocal, gBest, [], [], [], [], lb, ub, @(x) nonlinear_constraint(x), options, initialGuess);

Caused by:

Failure in initial objective function evaluation. FMINCON

cannot continue.

>>

8 个评论
显示 6更早的评论隐藏 6更早的评论

Sam Chak 2024-2-24

编辑：Sam Chak 2024-2-24

Hi @Sadiq Akbar,

It appears that the code was originally authored by Iraj Naruei and Farshid Keynia. Could you please clarify which part of the code you have modified to suit your application? It is important to establish your understanding of the entire code and the extent of your modifications. Without this clarity, it may be challenging to effectively communicate and address programming issues.

Based on logical deduction, I suspect that you may have modified or supplied your own Cost Function, MIMOfunNoise(), but without including constraints in your optimization problem. In the authors' demo, the original fmincon() optimization problem likely required the nonlinear_constraint(x) function. If you run the code without addressing this incompatibility issue, an error is likely to occur.

It is unlikely that any designer who created the Cost Function would not understand whether or not there are constraints placed on the design. Could you please write out the optimization problem in mathematical form? By expressing the problem mathematically, you can later translate it into MATLAB code for implementation.

Sam Chak 2024-2-24

在 MATLAB Online 中打开

@Sadiq Akbar, you should check and compare the fmincon() function in your code with the proper syntax of fmincon as shown below:

[bestX, fmin] = fmincon(objectiveFunctionForLocal, ...  % cost function
    gBest, ...                                          % initial point
    [], ...                                             % A
    [], ...                                             % b
    [], ...                                             % Aeq
    [], ...                                             % beq
    lb, ...                                             % lower bound
    ub, ...                                             % upper bound
    @(x) nonlinear_constraint(x), ...                   % nonlinear inequalities
    options, ...                                        % optimoptions()
    initialGuess);                                      % ???

Rena Berman 2024-3-15

(Answers Dev) Restored edit

请先登录，再进行评论。

请先登录，再回答此问题。

Answer 1

Sam Chak 2024-2-24

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2086293-why-does-this-code-give-me-an-error#answer_1415683

在 MATLAB Online 中打开

Hi @Sadiq Akbar

If nonlinear inequality constraints are not available, you can disable them in the code. It appears that the updated code now returns some results. Please verify and check.

% [c,ceq] = nonlinear_constraint(gBest)
[bestX, fmin] = fmincon(objectiveFunctionForLocal, gBest, [], [], [], [], lb, ub, [], options)
bestX = 1×2
  -32.9928   32.9909
fmin = 0.4869
bestX = 1×2
  -32.9921   33.0007
fmin = 0.7671
bestX = 1×2
  -32.9935   33.0065
fmin = 0.5644
bestX = 1×2
  -32.9949   32.9937
fmin = 0.6559
bestX = 1×2
  -33.0059   33.0040
fmin = 0.7550
bestX = 1×2
  -32.9730   32.9957
fmin = 0.5571
bestX = 1×2
  -32.9869   33.0073
fmin = 0.5580
bestX = 1×2
  -33.0045   33.0003
fmin = 0.6424
bestX = 1×2
  -32.9803   33.0095
fmin = 0.5441
bestX = 1×2
  -33.0141   33.0049
fmin = 0.5860
bestX = 1×2
  -33.0051   33.0009
fmin = 0.5768
bestX = 1×2
  -33.0024   33.0039
fmin = 0.4507
bestX = 1×2
  -33.0093   33.0032
fmin = 0.5387
bestX = 1×2
  -32.9842   32.9942
fmin = 0.6914
bestX = 1×2
  -32.9965   32.9991
fmin = 0.5265
bestX = 1×2
  -32.9984   32.9997
fmin = 0.6837
bestX = 1×2
  -32.9989   33.0143
fmin = 0.4914
bestX = 1×2
  -32.9944   32.9951
fmin = 0.5838
bestX = 1×2
  -33.0080   33.0096
fmin = 0.6222
bestX = 1×2
  -32.9865   33.0004
fmin = 0.6634
bestX = 1×2
  -33.0073   33.0058
fmin = 0.7632
bestX = 1×2
  -33.0026   33.0117
fmin = 0.5873
bestX = 1×2
  -32.9913   32.9825
fmin = 0.7897
bestX = 1×2
  -33.0031   32.9977
fmin = 0.5744
bestX = 1×2
  -33.0043   32.9873
fmin = 0.7019
bestX = 1×2
  -33.0017   33.0097
fmin = 0.5797
bestX = 1×2
  -33.0032   32.9998
fmin = 0.6127
bestX = 1×2
  -33.0016   32.9977
fmin = 0.5354
bestX = 1×2
  -33.0086   33.0055
fmin = 0.5288
bestX = 1×2
  -32.9955   32.9920
fmin = 0.6432
%%%%%%%%%%%%%%%%%%%
% Choose best
%%%%%%%%%%%%%%%%%%%
[one1 ind]=sort(one,'descend');
[fitness,ind1]=min(one1);
two1=two(ind1,:);
[one1SQP indSQP]=sort(oneSQP','descend');
[fitnessSQP,ind1SQP]=min(one1SQP);
two1SQP=twoSQP(ind1SQP,:);
% Display results
format compact
fprintf('Global_Sol  : %s\n', num2str(round(two1, 4),'%.4f '));
Global_Sol  : -32.9952  32.9930
fprintf('Local_Sol   : %s\n', num2str(round(two1SQP,4),'%.4f '));
Local_Sol   : -32.9955  32.9920
fprintf('Desired     : %s\n', num2str(u,'%.4f '));
Desired     : -33.0000  33.0000
fprintf('Global_fmin : %f\n', fitness);
Global_fmin : 0.269830
fprintf('Local_fmin  : %f\n', fitnessSQP);
Local_fmin  : 0.450739

2 个评论
显示无隐藏无

Sadiq Akbar 2024-2-24

@Sam Chak

Thanks a lot for your guiadance. Yes there is no nonlinear constraint in my problem. But now again there is a problem. In earlier times, when I used the GA tool, and I used to run my code with GA, SQP and then GA_SQP. At that time the estimated solution by GA wa less accurate, likewise the SQP estimation was more accurate but the estimation by GA_SQP was the best. I mean when I gave the estimated solution of Ga to SQP as an input, then the SQP would fine tune that estimated solution further and therefore the estimated solution by the hybrid GA_SQP would be the best at that time but here it is not so. Why?

Sam Chak 2024-2-24

You're welcome, @Sadiq Akbar. I simply fixed the fmincon syntax in the optimization code to get it running. As for why the GA-SQP hybrid is expected to perform better in your problem, I cannot provide a definitive answer at the moment.

请先登录，再进行评论。

Answer 2

Walter Roberson 2024-2-24

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2086293-why-does-this-code-give-me-an-error#answer_1415778

在 MATLAB Online 中打开

[bestX, fmin] = fmincon(objectiveFunctionForLocal, gBest, [], [], [], [], lb, ub, @(x) nonlinear_constraint(x), options, initialGuess);

fmincon() does not expect an initialGuess parameter. It sees the initialGuess as an extra parameter. What it does with that extra parameter is no longer documented: it passes that extra parameter as an extra parameter to objectiveFunctionForLocal and as an extra parameter to @(x) nonlinear_constraint(x) too. But

objectiveFunctionForLocal = @(x) MIMOfunNoise(x, u, Noise) + penaltyTerm(x, u);

expects only a single parameter, and so gives an error when the extra parameter intialGuess is passed to it.

1 个评论
显示 -1更早的评论隐藏 -1更早的评论

Sadiq Akbar 2024-2-25

@Walter Roberson

Thanks a lot for your kind guiadance. Yes, I removed the nonlinear constraint function and it works but the problem is that when I run my fitness function MIMOfunNoise() with GA, it gives me a best estimated vector, then I give that best estimated vetor to the SQP so that SQP further fine tune it( as I used to do before) but it is not so now i.e., I mean the SQP gives even a worse solution and fitness than GA. I don't know why and where am I doing the mistake?

请先登录，再进行评论。

Why does this code give me an error?

8 个评论
显示 6更早的评论隐藏 6更早的评论

回答（2 个）

2 个评论
显示无隐藏无

1 个评论
显示 -1更早的评论隐藏 -1更早的评论

另请参阅

类别

标签

Community Treasure Hunt

Why does this code give me an error?

8 个评论 显示 6更早的评论隐藏 6更早的评论

回答（2 个）

2 个评论 显示 无隐藏 无

1 个评论 显示 -1更早的评论隐藏 -1更早的评论

另请参阅

类别

标签

Community Treasure Hunt

8 个评论
显示 6更早的评论隐藏 6更早的评论

2 个评论
显示无隐藏无

1 个评论
显示 -1更早的评论隐藏 -1更早的评论