pso doesnt go to main loop

i edit the code for optimize ,y cost function

%
% Copyright (c) 2015, Mostapha Kalami Heris & Yarpiz (www.yarpiz.com)
% All rights reserved. Please read the "LICENSE" file for license terms.
%
% Project Code: YPEA102
% Project Title: Implementation of Particle Swarm Optimization in MATLAB
% Publisher: Yarpiz (www.yarpiz.com)
% 
% Developer: Mostapha Kalami Heris (Member of Yarpiz Team)
% 
% Cite as:
% Mostapha Kalami Heris, Particle Swarm Optimization in MATLAB (URL: https://yarpiz.com/50/ypea102-particle-swarm-optimization), Yarpiz, 2015.
% 
% Contact Info: [e-mail], [e-mail]
%
%edit by MR.N
% function y = CostFunction(amp,teta)
clear;
close all;

% clc;
toc

%% Problem Definition

% CostFunction = @(x) Sphere(x);        % Cost Function

nVarAmr = 10;            % Number of Decision Variables
nVarTetar = 10;

VarSizeAmr   = [1 nVarAmr];   % Size of Decision Variables Matrix
VarSizeTetar = [1 nVarTetar];

VarMinAmr = 0;         % Lower Bound of Variables
VarMaxAmr = 1;         % Upper Bound of Variables
VarMinTetar = -90;         % Lower Bound of Variables
VarMaxTetar = 90;         % Upper Bound of Variables

%% PSO Parameters

MaxIt = 10000;      % Maximum Number of Iterations

nPop = 1000;        % Population Size (Swarm Size)

% PSO Parameters
w = 1;            % Inertia Weight
wdamp = 0.99;     % Inertia Weight Damping Ratio
c1 = 1.5;         % Personal Learning Coefficient
c2 = 2.0;         % Global Learning Coefficient

% If you would like to use Constriction Coefficients for PSO, 
% uncomment the following block and comment the above set of parameters.

% % Constriction Coefficients
% phi1 = 2.05;
% phi2 = 2.05;
% phi = phi1+phi2;
% chi = 2/(phi-2+sqrt(phi^2-4*phi));
% w = chi;          % Inertia Weight
% wdamp = 1;        % Inertia Weight Damping Ratio
% c1 = chi*phi1;    % Personal Learning Coefficient
% c2 = chi*phi2;    % Global Learning Coefficient

% Velocity Limits
VelMaxAmr  = 0.1*(VarMaxAmr-VarMinAmr);
VelMinAmr  = -VelMaxAmr;
VelMaxTetar = 0.1*(VarMaxTetar-VarMinTetar);
VelMinTetar = -VelMaxTetar;

%% Initialization

empty_particle.PositionAmr = [];
empty_particle.CostAmr = [];
empty_particle.VelocityAmr = [];
empty_particle.Best.PositionAmr = [];
empty_particle.Best.CostAmr = [];

empty_particle.PositionTetar = [];
empty_particle.CostTetar = [];
empty_particle.VelocityTetar = [];
empty_particle.Best.PositionTetar = [];
empty_particle.Best.CostTetar = [];

particleAmr = repmat(empty_particle.PositionAmr , nPop, 1);

particleTetar = repmat(empty_particle.PositionTetar, nPop, 1);
GlobalBest.Cost = inf;

for i = 1:nPop
    
    % Initialize Position
    particle(i).PositionAmr = unifrnd(VarMinAmr, VarMaxAmr,VarSizeAmr);
    particle(i).PositionTetar = unifrnd(VarMinTetar, VarMaxTetar, VarSizeTetar);
    particle(i).Position = [particle(i).PositionAmr,particle(i).PositionTetar];

    % Initialize Velocity
    particle(i).VelocityAmr = zeros(VarSizeAmr)+0.00001;
    particle(i).VelocityTetar = zeros(VarSizeTetar)+0.00001;
    
    % Evaluation
    particle(i).Cost = CostFunction(particle(i).PositionAmr,particle(i).PositionTetar);
    
    % Update Personal Best
    particle(i).Best.PositionAmr = particle(i).PositionAmr;
    particle(i).Best.PositionTetar = particle(i).PositionTetar;
    particle(i).Best.Cost = particle(i).Cost;
    
    % Update Global Best
    if particle(i).Best.Cost<GlobalBest.Cost
        
        GlobalBest = particle(i).Best;
        
    end
    
end

BestCost = zeros(MaxIt, 1);

%% PSO Main Loop

for it = 1:MaxIt
    
    for i = 1:nPop
        
        % Update Velocity
        particle(i).VelocityAmr = w*particle(i).VelocityAmr ...
            +c1*rand(VarSizeAmr).*(particle(i).Best.PositionAmr-particle(i).PositionAmr) ...
            +c2*rand(VarSizeAmr).*(GlobalBest.PositionAmr-particle(i).PositionAmr);

        particle(i).VelocityTetar = w*particle(i).VelocityTetar ...
            +c1*rand(VarSizeTetar).*(particle(i).Best.PositionTetar-particle(i).PositionTetar) ...
            +c2*rand(VarSizeTetar).*(GlobalBest.PositionTetar-particle(i).PositionTetar);
        
        
        % Apply Velocity Limits
        particle(i).VelocityAmr = max(particle(i).VelocityAmr, VelMinAmr);
        particle(i).VelocityAmr = min(particle(i).VelocityAmr, VelMaxAmr);

        particle(i).VelocityTetar = max(particle(i).VelocityTetar, VelMinTetar);
        particle(i).VelocityTetar = min(particle(i).VelocityTetar, VelMaxTetar);

        % Update Position
        particle(i).PositionAmr   = particle(i).PositionAmr + particle(i).VelocityAmr;

        particle(i).PositionTetar = particle(i).PositionTetar + particle(i).VelocityTetar;

        % Velocity Mirror Effect
        IsOutside = (particle(i).PositionAmr<VarMinAmr | particle(i).PositionAmr>VarMaxAmr);
        particle(i).VelocityAmr(IsOutside) = -particle(i).VelocityAmr(IsOutside);

        IsOutside = (particle(i).PositionTetar<VarMinTetar | particle(i).PositionTetar>VarMaxTetar);
        particle(i).VelocityTetar(IsOutside) = -particle(i).VelocityTetar(IsOutside);

        % Apply Position Limits
        particle(i).PositionAmr = max(particle(i).PositionAmr, VarMinAmr);
        particle(i).PositionAmr = min(particle(i).PositionAmr, VarMaxAmr);
        
        particle(i).PositionTetar = max(particle(i).PositionTetar, VarMinTetar);
        particle(i).PositionTetar = min(particle(i).PositionTetar, VarMaxTetar);

        % Evaluation
        % particle(i).Cost = CostFunction(particle(i).Position);
        % change above according to chatgpt
        particle(i).Cost = CostFunction(particle(i).PositionAmr, particle(i).PositionTetar);

        % Update Personal Best
        if particle(i).Cost<particle(i).Best.Cost
            
            particle(i).Best.Position = particle(i).Position;
            particle(i).Best.Cost = particle(i).Cost;
            
            % Update Global Best
            if particle(i).Best.Cost<GlobalBest.Cost
                
                GlobalBest = particle(i).Best;
                
            end
            
        end
        
    end
    % disp(['Particle ' num2str(i) ', Iteration ' num2str(it) ':']);
    %     disp(['  VelocityAmr = ' num2str(particle(i).VelocityAmr)]);
    %     disp(['  VelocityTetar = ' num2str(particle(i).VelocityTetar)]);
        
    % BestCost(it) = GlobalBest.Cost;
    % 
    % disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]);
    
    w = w*wdamp;
    
end






% function y = CostFunction (Amr,deltaTeta)
function y = CostFunction (PositionAmr,PositionTetar)
freq = 9*10^9;       %Freq
j = sqrt(-1);        %Define Imaginary
l =(3*10^8)/freq;    %Lambda
k = (2*pi)/l;        %Constant
d = 0.5*l;           %Distant of each element
elementNumb = 10;
step = 1;

teta1 = ((-80)  :step:  (-15));
teta2 = ((-15)  :step:  (0));
teta3 = ((0)    :step:  (15));
teta4 = ((25)   :step:  (40));
teta5 = ((40)   :step:  (60));

tetat = [teta1,teta2,teta3,teta4,teta5];
lenteta1 = length(teta1);
sumLenteta12   = length(teta1) + length(teta2);
sumLenteta123  = length(teta3) + sumLenteta12;
sumLenteta1234 = sumLenteta123 + length(teta4);

g = zeros(elementNumb,length(tetat));

    for h = 1:elementNumb

        %%teta1
        for aa = 1:length(teta1)

        g(h,aa) = g(h,aa)+(PositionAmr(h) * ( exp(j*(h-1) * (k*d*sind(teta1(aa)+PositionTetar(h))))));      %w W
        end

        %%teta2
        for bb=(lenteta1+1):(sumLenteta12)
      
            g(h,bb) =g(h,bb)+( PositionAmr(h) * exp(j*(h-1) * (k*d*sind(teta2(bb-lenteta1)+PositionTetar(h))))); %with W
        end
    
        %%teta3
        for cc = (sumLenteta12+1):(length(teta3)+sumLenteta12)


        g(h,cc) = (g(h,cc)+( PositionAmr(h) * exp(j*(h-1) * (k*d*sind(teta3(cc-sumLenteta12)...
            +PositionTetar(h))))));  %w W  
        end
        %%teta4

         for dd = (sumLenteta123+1):(sumLenteta123+length(teta4))
       

          g(h,dd) = g(h,dd)+( PositionAmr(h) * exp(j*(h-1) * (k*d*sind(teta4(dd-(sumLenteta123))...
                 +PositionTetar(h)))));

          end
        %%teta5

         for ee = (sumLenteta1234+1):(sumLenteta1234+length(teta5))
   

              g(h,ee) = g(h,ee)+( PositionAmr(h) * exp(j*(h-1) * (k*d*sind(teta5(ee-(sumLenteta1234))...
                 +PositionTetar(h)))));  %w W
         end
    end

y = abs(sum(g,1));

 end

it has 20 variable which those consist of amplitude (Amr) and angle (Tetar)

and separated my cost function to 5 section for my next goal ( i want to prioritize each section for example area two should be more minimize than others or i want to inverse my cost function

 1/( PositionAmr(h) * exp(j*(h-1) * (k*d*sind(teta(ee-(sumLenteta1234))...
                 +PositionTetar(h)))))

to maximize in some area

it has a loop which optimize my variable but after several time check i recognize it doesnt go to this loop and the first value always remain up to end

 % Update Personal Best
        if particle(i).Cost<particle(i).Best.Cost
            
            particle(i).Best.Position = particle(i).Position;
            particle(i).Best.Cost = particle(i).Cost;
            
            % Update Global Best
            if particle(i).Best.Cost<GlobalBest.Cost
                
                GlobalBest = particle(i).Best;
                
            end

should i put some condition for it ?