I Have a MATLAB CODE for Artificial Bee Colony , anyone can help me to plot the convergence graph?

clear all
close all
clc
warning off


objfun='tracklsqPID';
D=3;% dimension, number of parameters
R=40;% upper bound
R1=0;% Lower bound
NP=10; %number of population N*D
limit=30; %special function for scoutbee
maxCycle=15;%iteration

FoodNumber=NP/2; 

ub=ones(1,D)*R; %/*Upper bounds of the parameters. */
lb=ones(1,D)*(R1);%/*lower bound of the parameters.*/

runtime=1;%/*Algorithm can be run many times in order to see its robustness*/

ObjVal(:,:)=0*ones(1,FoodNumber);

GlobalMins=zeros(1,runtime);

for r=1:runtime
    
    
    % /*All food sources are initialized */
    %/*Variables are initialized in the range [lb,ub]. If each parameter has different range, use arrays lb[j], ub[j] instead of lb and ub */
    tic;
    Range = repmat((ub-lb),[FoodNumber 1]);
    Lower = repmat(lb, [FoodNumber 1]);
    Foods = rand(FoodNumber,D) .* Range + Lower;
    % i=1
    for i=1:FoodNumber
        
        ObjVal(1,i)=feval(objfun,Foods(i,:)); 
        
    end
    Fitness=calculateFitness(ObjVal);
    FirstFitness = Fitness;
    %reset trial counters
    trial=zeros(1,FoodNumber);
    
    %/*The best food source is memorized*/
    BestInd=find(ObjVal==min(ObjVal));
    BestInd=BestInd(end);
    GlobalMin=ObjVal(BestInd);
    GlobalParams=Foods(BestInd,:);
    
    
    
    iter=1;
    while ((iter <= maxCycle))
        
        
        %%%%%%%%% EMPLOYED BEE PHASE %%%%%%%%%%%%%%%%%%%%%%%%
        for i=1:(FoodNumber)
            
            %/*The parameter to be changed is determined randomly*/
            Param2Change=fix(rand*D)+1;
            
            %/*A randomly chosen solution is used in producing a mutant solution of the solution i*/
            neighbour=fix(rand*(FoodNumber))+1;
            %/*Randomly selected solution must be different from the solution i*/        
            while(neighbour==i)
                neighbour=fix(rand*(FoodNumber))+1;
            end;
            
            
            
            sol=Foods(i,:);
            %  /*v_{ij}=x_{ij}+\phi_{ij}*(x_{kj}-x_{ij}) */
            
            sol(Param2Change)=Foods(i,Param2Change)+(Foods(i,Param2Change)-Foods(neighbour,Param2Change))*(rand-0.5)*2;
            
            
            %  /*if generated parameter value is out of boundaries, it is shifted onto the boundaries*/
            ind=find(sol<lb);
            sol(ind)=lb(ind);
            ind=find(sol>ub);
            sol(ind)=ub(ind);
            
            %evaluate new solution
            ObjValSol=feval(objfun,sol);
            FitnessSol=calculateFitness(ObjValSol);
            
            
            % /*a greedy selection is applied between the current solution i and its mutant*/
            if (FitnessSol>Fitness(i)) %/*If the mutant solution is better than the current solution i, replace the solution with the mutant and reset the trial counter of solution i*/
                Foods(i,:)=sol;
                Fitness(i)=FitnessSol;
                ObjVal(i)=ObjValSol;
                trial(i)=0;
            else
                trial(i)=trial(i)+1; %/*if the solution i can not be improved, increase its trial counter*/
            end;
            
            
        end;
        
        %%%%%%%%%%%%%%%%%%%%%%%% CalculateProbabilities %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
        %/* A food source is chosen with the probability which is proportioal to its quality*/
        %/*Different schemes can be used to calculate the probability values*/
        %/*For example prob(i)=fitness(i)/sum(fitness)*/
        %/*or in a way used in the metot below prob(i)=a*fitness(i)/max(fitness)+b*/
        %/*probability values are calculated by using fitness values and normalized by dividing maximum fitness value*/
        
        prob=(0.9*Fitness./max(Fitness))+0.1;
        
        %%%%%%%%%%%%%%%%%%%%%%%% ONLOOKER BEE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
        
        i=1;
        t=0;
        while(t<FoodNumber)
            if(rand<prob(i))
                t=t+1;
                %/*The parameter to be changed is determined randomly*/
                Param2Change=fix(rand*D)+1;
                
                
                %/*A randomly chosen solution is used in producing a mutant solution of the solution i*/
                neighbour=fix(rand*(FoodNumber))+1;
                
                %/*Randomly selected solution must be different from the solution i*/        
                while(neighbour==i)
                    neighbour=fix(rand*(FoodNumber))+1;
                end;
                
                sol=Foods(i,:);
                %  /*v_{ij}=x_{ij}+\phi_{ij}*(x_{kj}-x_{ij}) */
                sol(Param2Change)=Foods(i,Param2Change)+(Foods(i,Param2Change)-Foods(neighbour,Param2Change))*(rand-0.5)*2;
                
                %  /*if generated parameter value is out of boundaries, it is shifted onto the boundaries*/
                ind=find(sol<lb);
                sol(ind)=lb(ind);
                ind=find(sol>ub);
                sol(ind)=ub(ind);
                
                %evaluate new solution
                ObjValSol=feval(objfun,sol);
                FitnessSol=calculateFitness(ObjValSol);
                
                % /*a greedy selection is applied between the current solution i and its mutant*/
                if (FitnessSol>Fitness(i)) %/*If the mutant solution is better than the current solution i, replace the solution with the mutant and reset the trial counter of solution i*/
                    Foods(i,:)=sol;
                    Fitness(i)=FitnessSol;
                    ObjVal(i)=ObjValSol;
                    trial(i)=0;
                else
                    trial(i)=trial(i)+1; %/*if the solution i can not be improved, increase its trial counter*/
                end;
            end;
            
            i=i+1;
            if (i==(FoodNumber)+1) 
                i=1;
            end;   
        end; 
        
        GlobalParams;
        %/*The best food source is memorized*/
        ind=find(ObjVal==min(ObjVal));
        ind=ind(end);
        if (ObjVal(ind)<GlobalMin)
            GlobalMin=ObjVal(ind);
            GlobalParams=Foods(ind,:);
        end;
        
        best(:,iter)=GlobalParams;
        %/*The best food source is memorized*/
        
        
        %%%%%%%%%%%% SCOUT BEE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
        
        %/*determine the food sources whose trial counter exceeds the "limit" value. 
        %In Basic ABC, only one scout is allowed to occur in each cycle*/
        
        ind=find(trial==max(trial));
        ind=ind(end);
        if (trial(ind)>limit)
            trial(ind)=0;
            
            sol=(ub-lb).*rand(1,D)+lb;
            ObjValSol=feval(objfun,sol);
            FitnessSol=calculateFitness(ObjValSol);
            Foods(ind,:)=sol;
            Fitness(ind)=FitnessSol;
            ObjVal(ind)=ObjValSol;
        end;
        fprintf('GloabalParams = \n\n  kp=%f ki=%f kd=%f \n\n ',GlobalParams);
        fprintf(' Ýter=%d ObjVal=%g  D=%g\n\n ',iter,GlobalMin,D);
        GlobalParams;
        k(:,iter)=GlobalMin;
        kl(:,iter)=GlobalParams;
        iter=iter+1;
        
    end % End of ABC
    
    
    GlobalMins(r)=GlobalMin;
    toc
    kH=toc;
    
    
end; %end of runs

Error using feval
Unrecognized function or variable 'tracklsqPID'.

save all

[Sam Chak]

Hi @AZRUL AZIM

Since you have established the lower and upper bounds to range between 0 and 40 in order to ensure the discovery of a solution within this specific range, could you evaluate the ABC algorithm using the provided simple convex function tracklsqPID(x)? If it operates as expected, it should yield the solution at .

objfun = @tracklsqPID;
[xsol, fval, exitflag, output] = ga(objfun, 1, [], [], [], [], 0, 40)

Optimization terminated: average change in the fitness value less than options.FunctionTolerance.

xsol = 20.0000

fval = 9.5035e-10

exitflag = 1

output = struct with fields:
      problemtype: 'boundconstraints'
         rngstate: [1×1 struct]
      generations: 62
        funccount: 2967
          message: 'Optimization terminated: average change in the fitness value less than options.FunctionTolerance.'
    maxconstraint: 0
       hybridflag: []

function J = tracklsqPID(x)
    J = (x - 20).^2;
end