Effacer les filtres
Effacer les filtres

Why does this code give me an error?

5 vues (au cours des 30 derniers jours)
Sadiq Akbar
Sadiq Akbar le 24 Fév 2024
Commenté : Rena Berman le 15 Mar 2024
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 commentaires
Sam Chak
Sam Chak le 24 Fév 2024
@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
Rena Berman le 15 Mar 2024
(Answers Dev) Restored edit

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Réponses (2)

Sam Chak
Sam Chak le 24 Fév 2024
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 commentaires
Sadiq Akbar
Sadiq Akbar le 24 Fév 2024
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
Sam Chak le 24 Fév 2024
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.

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Walter Roberson
Walter Roberson le 24 Fév 2024
[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 commentaire
Sadiq Akbar
Sadiq Akbar le 25 Fév 2024
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?

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