Why do i receive the error:" unrecognized function or variable "Fun"?

% Particle Swarm Optimization
function [Y,X,cg_curve]=PSO(N,Max_iteration,lb,ub,dim,fhandle,fnonlin)
%PSO Infotmation

Vmax=6;
noP=N;
wMax=0.9;
wMin=0.2;
c1=2;
c2=2;

% Initializations
for k = 1 : N
    Swarm.Particles(k).X = (ub-lb) .* rand(1,dim) + lb;
    Swarm.Particles(k).V = zeros(1, dim);
    Swarm.Particles(k).PBEST.X = zeros(1,dim);
    Swarm.Particles(k).PBEST.O = inf;

    Swarm.GBEST.X = zeros(1,dim);
    Swarm.GBEST.O = inf;
end


cg_curve=zeros(1,Max_iteration);

%Main

for t = 1 : Max_iteration

    % Calcualte the objective value
    for k = 1 : N
        currentX = Swarm.Particles(k).X;
        Swarm.Particles(k).O = fun(fhandle,fnonlin,currentX);

        % Update the PBEST
        if Swarm.Particles(k).O < Swarm.Particles(k).PBEST.O
            Swarm.Particles(k).PBEST.X = currentX;
            Swarm.Particles(k).PBEST.O = Swarm.Particles(k).O;
        end

        % Update the GBEST
        if Swarm.Particles(k).O < Swarm.GBEST.O
            Swarm.GBEST.X = currentX;
            Swarm.GBEST.O = Swarm.Particles(k).O;
        end
    end

    % Update the X and V vectors
    w = wMax - t .* ((wMax - wMin) / Max_iteration);

    for k = 1 : N



        Swarm.Particles(k).V = w .* Swarm.Particles(k).V + c1 .* rand(1,dim) .* (Swarm.Particles(k).PBEST.X - Swarm.Particles(k).X) ...
                                                                                                     + c2 .* rand(1,dim) .* (Swarm.GBEST.X - Swarm.Particles(k).X);


        % Check velocities
        index1 = find(Swarm.Particles(k).V > Vmax);
        index2 = find(Swarm.Particles(k).V < -Vmax);

        Swarm.Particles(k).V(index1) = Vmax;
        Swarm.Particles(k).V(index2) = -Vmax;

        Swarm.Particles(k).X = Swarm.Particles(k).X + Swarm.Particles(k).V;

        % Check positions
        index1 = find(Swarm.Particles(k).X > ub);
        index2 = find(Swarm.Particles(k).X < lb);

        Swarm.Particles(k).X(index1) = ub(index1);
        Swarm.Particles(k).X(index2) = lb(index2);

    end



    cg_curve(t) = Swarm.GBEST.O;
end


X=Swarm.GBEST.X;
Y=Swarm.GBEST.O;
end

Reference: Grey Wold Optimizer (GWO) source codes version 1.0

The code was developed on R2011b(7.13), i tried to use it to learn more about PSO but came out as error:

unrecognized function or variable "Fun"

Error in PSO:

Swarm.Particles(k).O = fun(fhandle,fnonlin,currentX);

[ScottB]

Kevin,

There isn't a native ML function called "fun". Is it possible you mean "funm"? It evaluates general matrix function.

Answer 1

[Voss]

Maybe you mean feval instead of fun.

[Kevin]

After i switched it to

Swarm.Particles(k).O = feval(fhandle,fnonlin,currentX);

And input PSO(N,Max_iteration,lb,ub,dim,fhandle,fnonlin) as

PSO(40,5000,100,-100,20,2,[])

The result came back as

Error using feval Function to evaluate must be represented as a string scalar, character vector, or function_handle object.

Why does it happen and what should i do?

[Voss]

The PSO function is defined to take seven inputs, with input #6 being fhandle:

function [Y,X,cg_curve]=PSO(N,Max_iteration,lb,ub,dim,fhandle,fnonlin)

When you call it, your input #6 is the number 2:

PSO(40,5000,100,-100,20,2,[])

The inputs are not described in the code, so I have no idea what they represent, but based on the name "fhandle" it seems like fhandle should be a function handle, not a number.

Where did you download this PSO code from? I found several Grey Wolf Optimizer submissions on the File Exchange, but none of the ones I looked at had a PSO function. Are there examples of what inputs it expects shown at the place you got it? Feel free to post the link where I can download this PSO function, and I'll take a look myself.

[Kevin]

I got it from here:

Grey Wold Optimizer (GWO) source codes version 1.0

Developed in MATLAB R2011b(7.13)

Author and programmer: Seyedali Mirjalil

e-Mail: [e-mail]

[e-mail]

Homepage: http://www.alimirjalili.com

Main paper: S. Mirjalili, S. M. Mirjalili, A. Lewis Grey Wolf Optimizer, Advances in Engineering Software, in press, DOI: 10.1016/j.advengsoft.2013.12.007

P/s: I also think its a function handle and fnonlin is short for function nonlinear but i don't know what the function objective is so i just put 2 as a placeholder then if the feval code worked, it would show the input/ the content inside it was the problem. And i think the function handle should be in the following zip, since my work is to compare the result between PSO and EO, EO i have managed to but for PSO, i still stuck at the feval

[Voss]

Maybe you can use MATLAB's builtin particle swarm optimization function, particleswarm, to learn about PSO. It seems well-documented.

In any case, I got that GWO code. I'm not sure how it's related to the PSO function in your question. Here's what I found:

%___________________________________________________________________%
%  Grey Wolf Optimizer (GWO) source codes version 1.0               %
%                                                                   %
%  Developed in MATLAB R2011b(7.13)                                 %
%                                                                   %
%  Author and programmer: Seyedali Mirjalili                        %
%                                                                   %
%         e-Mail: [e-mail]                           %
%                 [e-mail]             %
%                                                                   %
%       Homepage: http://www.alimirjalili.com                       %
%                                                                   %
%   Main paper: S. Mirjalili, S. M. Mirjalili, A. Lewis             %
%               Grey Wolf Optimizer, Advances in Engineering        %
%               Software , in press,                                %
%               DOI: 10.1016/j.advengsoft.2013.12.007               %
%                                                                   %
%___________________________________________________________________%

% Grey Wolf Optimizer
function [Alpha_score,Alpha_pos,Convergence_curve]=GWO(SearchAgents_no,Max_iter,lb,ub,dim,fobj)

% initialize alpha, beta, and delta_pos
Alpha_pos=zeros(1,dim);
Alpha_score=inf; %change this to -inf for maximization problems

Beta_pos=zeros(1,dim);
Beta_score=inf; %change this to -inf for maximization problems

Delta_pos=zeros(1,dim);
Delta_score=inf; %change this to -inf for maximization problems

%Initialize the positions of search agents
Positions=initialization(SearchAgents_no,dim,ub,lb);

Convergence_curve=zeros(1,Max_iter);

l=0;% Loop counter

% Main loop
while l<Max_iter
    for i=1:size(Positions,1)  
        
       % Return back the search agents that go beyond the boundaries of the search space
        Flag4ub=Positions(i,:)>ub;
        Flag4lb=Positions(i,:)<lb;
        Positions(i,:)=(Positions(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;               
        
        % Calculate objective function for each search agent
        fitness=fobj(Positions(i,:));
        
        % Update Alpha, Beta, and Delta
        if fitness<Alpha_score 
            Alpha_score=fitness; % Update alpha
            Alpha_pos=Positions(i,:);
        end
        
        if fitness>Alpha_score && fitness<Beta_score 
            Beta_score=fitness; % Update beta
            Beta_pos=Positions(i,:);
        end
        
        if fitness>Alpha_score && fitness>Beta_score && fitness<Delta_score 
            Delta_score=fitness; % Update delta
            Delta_pos=Positions(i,:);
        end
    end
    
    
    a=2-l*((2)/Max_iter); % a decreases linearly fron 2 to 0
    
    % Update the Position of search agents including omegas
    for i=1:size(Positions,1)
        for j=1:size(Positions,2)     
                       
            r1=rand(); % r1 is a random number in [0,1]
            r2=rand(); % r2 is a random number in [0,1]
            
            A1=2*a*r1-a; % Equation (3.3)
            C1=2*r2; % Equation (3.4)
            
            D_alpha=abs(C1*Alpha_pos(j)-Positions(i,j)); % Equation (3.5)-part 1
            X1=Alpha_pos(j)-A1*D_alpha; % Equation (3.6)-part 1
                       
            r1=rand();
            r2=rand();
            
            A2=2*a*r1-a; % Equation (3.3)
            C2=2*r2; % Equation (3.4)
            
            D_beta=abs(C2*Beta_pos(j)-Positions(i,j)); % Equation (3.5)-part 2
            X2=Beta_pos(j)-A2*D_beta; % Equation (3.6)-part 2       
            
            r1=rand();
            r2=rand(); 
            
            A3=2*a*r1-a; % Equation (3.3)
            C3=2*r2; % Equation (3.4)
            
            D_delta=abs(C3*Delta_pos(j)-Positions(i,j)); % Equation (3.5)-part 3
            X3=Delta_pos(j)-A3*D_delta; % Equation (3.5)-part 3             
            
            Positions(i,j)=(X1+X2+X3)/3;% Equation (3.7)
            
        end
    end
    l=l+1;    
    Convergence_curve(l)=Alpha_score;
end

[Kevin]

Check my previous comment again, since i edited it after i found my zip folder, i have included the zip there too, and thank you for finding this

[Voss]

The zip contains "Get_Functions_details.m", which defines 23 objective functions (called F1 through F23), so maybe use one of those. You can copy the structure of "main.m" and replace the call to EO there with a call to your PSO function.

It may still be useful to know where the PSO function comes from.

[Kevin]

Thank you for your help. I had managed to make it work.