NN_GWO gives 0 values for weights

I have combined the Gre wolf optimizer with neural network to make a hybrid model to optimize Mean Square error for my research.

However my GWO code gives 0 values for all weights.

I have looked into code for hours but I am unable to find my mistake. If someone can spare some time it will be really helpful.

I am attaching the code below.

clear all 
clc

load ("slab.mat")

input_val = slab(:,1:6);
output_val = slab(:,7);

nm_input = [];

% Normalizing the data
for i=1:6
%the normalized values are correct, I have checked.
nm_input(:,i) = (input_val(:,i) - min(input_val(:,i))) / (max(input_val(:,i))  - min(input_val(:,i)));
end

X = nm_input;
nm_output = (output_val(:,1) - min(output_val(:,1))) / (max(output_val(:,1))  - min(output_val(:,1)));
Y = nm_output;

sz = (ceil(size(X,1))*0.75);
sz = round(sz);

% inputs for the neural net
inputs = X';
% targets for the neural net
targets = Y';

% number of neurons
n = 4;

% create a neural network
net_fnn = feedforwardnet(n);

% configure the neural network for this dataset
% net_nn = configure(net_nn, inputs, targets);
net_fnn = train(net_fnn, inputs, targets);

% get the normal NN weights and bias
getwb(net_fnn)

% error MSE normal NN
error_fnn = targets - net_fnn(inputs);
calc = mean(error_fnn.^2)/mean(var(targets',1));
output_fnn = net_fnn(inputs);

% defining the parameters
fobj = @(x) NMSE(x, net_fnn, inputs, targets);
dim = size(inputs,1)*n+n+n+1;
% Max_iteration = 
% SearchAgents_no = number of search agents
lb = 1;
ub = -1;

SearchAgents_no=3; % Number of search agents

%Function_name='F10'; % Name of the test function that can be from F1 to F23 (Table 1,2,3 in the paper)

Max_iteration=3; % Maximum numbef of iterations

% Load details of the selected benchmark function
%[lb,ub,dim,fobj]=Get_Functions_details(Function_name);

[Best_score,Best_pos,GWO_cg_curve]=GWO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj);
net_GWO = setwb(net_fnn, Best_pos');   %This lines contains the error

% get the GWO optimized NN weights and bias
getwb(net_GWO)

% error MSE GWO optimized NN
error_GWO = targets - net_GWO(inputs);
calc = mean(error_GWO.^2)/mean(var(targets',1));
output_GWO = net_GWO(inputs);

outputs = [targets; output_GWO; output_fnn]';
disp("error_GWO , error_nn ") 
errors = [error_GWO; error_fnn]'

predictions = [output_GWO', output_fnn', nm_output];

% biases and weights 
wb_GWO = getwb(net_GWO);
wb_fnn = getwb(net_fnn);
wb_total = [wb_GWO, wb_fnn];

%plots
figure;
plot(nm_output,'LineWidth',2, 'Marker','diamond', 'MarkerSize',8)
hold on;
plot(output_fnn, 'LineWidth',2, 'Marker','x', 'MarkerSize',8);
plot(output_GWO, 'LineWidth',2, 'Marker','pentagram', 'MarkerSize',8);
title('GWO Optimization based Feed-Forward Neural Network');
xlabel('Time Interval');
ylabel('Values');
legend('Actual Values', 'FNN Predictions', 'GWo-FNN Predictions');
hold off;

the code works without any error but the problem is that the weights are all zero so the bias value is returned as the ouput for prediction.