JAYA Optimization based Feed-Forward Network Matlab Code

Version 1.0.0 (3 KB) by SANA
JAYA Optimization based Feed-Forward Neural Network's Matlab Code, created by Sana Mujeeb
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Updated 18 Aug 2022

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JAYA Optimization based Feed-Forward Neural Network's Matlab Code, created by Sana Mujeeb. 14th August, 2022.
Researchgate: https://www.researchgate.net/profile/Sana-Mujeeb
Proposed by: Wang S, Rao RV, Chen P, Zhang Y, Liu A, Wei L. Abnormal breast detection in mammogram images by feed-forward neural network trained by Jaya algorithm. Fundamenta Informaticae. 2017 Jan 1;151(1-4):191-211.
Any errors and suggestions of improvements in his code are welcome. If you have any questions regarding this code, then please ask me.
Below is the complete code other than the attached file. Just copy it and paste on a m file and run the code, it has one file.
Enjoy!!!
% *************************************************************************************************************
% Source Code of JAYA Optimization based Feed-Forward
% Neural Network By Sana Mujeeb, 14h August, 2022
% Cite: Wang S, Rao RV, Chen P, Zhang Y, Liu A, Wei L. Abnormal breast detection in
% mammogram images by feed-forward neural network trained by Jaya algorithm.
% Fundamenta Informaticae. 2017 Jan 1;151(1-4):191-211.
% *************************************************************************************************************
% Enjoy JAYA-ANN!
clc;
% Generating random correlated data
mu = 50;
sigma = 5;
M = mu + sigma * randn(300, 2);
R = [1, 0.75; 0.75, 1];
L = chol(R);
M = M*L;
x = M(:,1); % Example Inputs, Replace by your data inputs for your own experiments
y = M(:,2); % Example labels, Replace by your data labels for your own experiments
%% JAYA algorithms
%% Problem Definition
pop = 30; % Population size
% Min-max normalization of data
m = max(x); mn = min(x); mm = m-mn;
X = ((x-mn)/mm); Y = ((x-mn)/mm);
% 90%:10% splitting of data for training and testing
sz = (ceil(size(X,1))*0.9);
inputs = (X(1:sz))';
targets = (Y(1:sz))';
XTest = (X(sz+1:end))';
YTest = Y(sz+1:end)';
% number of neurons
n = 4;
tic;
% create a neural network
net = feedforwardnet(n);
% configure the neural network for this dataset
net = configure(net, inputs, targets);
% Denormalizaion and Prediction by FNN
FNN_Pred = ((net(XTest))' * mm) + mn;
sz = n^2 + n + n + 1; % Number of design variables i.e., no. of weights in FNN
maxGen = 30; % Maximum number of iterations
mini = repmat(-1,1,sz); % Lower Bound of Variables
maxi = ones(1,sz); % Upper Bound of Variables
objective = @(x) NMSE(x, net, inputs, targets); % Cost Function
%% initialize
[row,var] = size(mini);
x = zeros(pop,var);
fnew = zeros(pop,1);
f = zeros(pop,1);
fopt= zeros(pop,1);
xopt=zeros(1,var);
%% Generation and Initialize the positions
for i=1:var
x(:,i) = mini(i)+(maxi(i)-mini(i))*rand(pop,1);
end
for i=1:pop
f(i) = objective(x(i,:));
end
%% Main Loop
gen=1;
fprintf('Best Cost per Iteration of JAYA Opimization Algorithm \n');
while(gen <= maxGen)
[row,col]=size(x);
[t,tindex]=min(f);
Best=x(tindex,:);
[w,windex]=max(f);
worst=x(windex,:);
xnew=zeros(row,col);
for i=1:row
for j=1:col
xnew(i,j)=(x(i,j))+rand*(Best(j)-abs(x(i,j))) - (worst(j)-abs(x(i,j))); %
end
end
for i=1:row
xnew(i,:) = max(min(xnew(i,:),maxi),mini);
fnew(i,:) = objective(xnew(i,:));
end
for i=1:pop
if(fnew(i)<f(i))
x(i,:) = xnew(i,:);
f(i) = fnew(i);
end
end
fnew = []; xnew = [];
[fopt(gen),ind] = min(f);
xopt(gen,:)= x(ind,:);
gen = gen+1;
disp(['Iteration No. = ',num2str(gen-1), ', Best Cost = ',num2str(min(f))])
end
%%
[val,ind] = min(fopt);
Fes = pop*ind;
disp(['Optimum value = ',num2str(val,10)])
figure;
plot(fopt,'LineWidth', 2);
xlabel('Itteration');
ylabel('Best Cost');
legend('JAYA');
disp(' ' );
% Setting optimized weights and bias in network
net = setwb(net, Best');
% Denormalizaion and Prediction by JAYA_FNN
JAYA_FNN_Pred = ((net(XTest))' * mm) + mn;
YTest = (YTest * mm) + mn;
JAYA_FNN_Execution_Time_Seconds = toc
% Plotting prediction results
figure;
plot(YTest,'LineWidth',2, 'Marker','diamond', 'MarkerSize',8);
hold on;
plot(FNN_Pred, 'LineWidth',2, 'Marker','x', 'MarkerSize',8);
plot(JAYA_FNN_Pred, 'LineWidth',2, 'Marker','pentagram', 'MarkerSize',8);
title('JAYA Optimization based Feed-Forward Neural Network');
xlabel('Time Interval');
ylabel('Values');
legend('Actual Values', 'FNN Predictions', 'JAYA-FNN Predictions');
hold off;
% Performance Evaluaion of FNN and JAYA-FNN
fprintf('Performance Evaluaion of FNN and JAYA-FNN using Normalized Root Mean Square Error \n');
NRMSE_FNN = (abs( sqrt( mean(mean((FNN_Pred - YTest).^2) )) )) / (max(YTest)-min(YTest))
NRMSE_JAYA_FNN = (abs( sqrt( mean(mean((JAYA_FNN_Pred - YTest).^2) ) ) )) / (max(YTest)-min(YTest))
% Objective Function for minimizing normalized mean square error of FNN by
% updation of nework's weights and biases
function [f] = NMSE(wb, net, input, target)
% wb is the weights and biases row vector obtained from the genetic algorithm.
% It must be transposed when transferring the weights and biases to the network net.
net = setwb(net, wb');
% The net output matrix is given by net(input). The corresponding error matrix is given by
error = target - net(input);
% The mean squared error normalized by the mean target variance is
f = (mean(error.^2)/mean(var(target',1)));
% It is independent of the scale of the target components and related to the Rsquare statistic via
% Rsquare = 1 - NMSEcalc ( see Wikipedia)
end

Cite As

SANA (2024). JAYA Optimization based Feed-Forward Network Matlab Code (https://www.mathworks.com/matlabcentral/fileexchange/116455-jaya-optimization-based-feed-forward-network-matlab-code), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2018a
Compatible with any release
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Version Published Release Notes
1.0.0