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main.m
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% 📜 Moss Growth Optimization (MGO) source codes (version 1.0)
% 🌐 Website and codes of MGO: Moss Growth Optimization: Concepts and Performance:
% 🔗 https://aliasgharheidari.com/MGO.html
% 👥 Boli Zheng, Yi Chen, Chaofan Wang, Ali Asghar Heidari, Lei Liu, Huiling Chen
% 📅 Last update: 9 05 2024
% 📜 After use of code, please users cite the main paper on MGO:
% Moss Growth Optimization: Concepts and Performance
% Boli Zheng, Yi Chen, Chaofan Wang, Ali Asghar Heidari, Lei Liu, Huiling Chen
% Journal of Computational Design and Engineering, 2024
%----------------------------------------------------------------------------------------------------------------------------------------------------%
% 📊 You can use and compare with other optimization methods developed recently:
% - (MGO) 2024: 🔗 https://aliasgharheidari.com/MGO.html
% - (PLO) 2024: 🔗 https://aliasgharheidari.com/PLO.html
% - (FATA) 2024: 🔗 https://aliasgharheidari.com/FATA.html
% - (ECO) 2024: 🔗 https://aliasgharheidari.com/ECO.html
% - (AO) 2024: 🔗 https://aliasgharheidari.com/AO.html
% - (PO) 2024: 🔗 https://aliasgharheidari.com/PO.html
% - (RIME) 2023: 🔗 https://aliasgharheidari.com/RIME.html
% - (INFO) 2022: 🔗 https://aliasgharheidari.com/INFO.html
% - (RUN) 2021: 🔗 https://aliasgharheidari.com/RUN.html
% - (HGS) 2021: 🔗 https://aliasgharheidari.com/HGS.html
% - (SMA) 2020: 🔗 https://aliasgharheidari.com/SMA.html
% - (HHO) 2019: 🔗 https://aliasgharheidari.com/HHO.html
%____________________________________________________________________________________________________________________________________________________%
%%
% fobj = @YourCostFunction
% dim = number of your variables
% MaxFEs = Maximum numbef of fitness evaluations
% lb=[lb1,lb2,...,lbn] where lbn is the lower bound of variable n
% ub=[ub1,ub2,...,ubn] where ubn is the upper bound of variable n
% If all the variables have equal lower bound you can just
% define lb and ub as two single number numbers
%%
clear all
clc
rng('default')
Function_name='F21'; % Name of the test function that can be from F1 to F23
% Load details of the selected benchmark function
[lb,ub,dim,fobj]=Get_Functions_details(Function_name);
MaxFEs=1000*dim;
N = 50; %popultaion size
[Best_pos,Convergence_curve] = MGO(N,MaxFEs,lb,ub,dim,fobj);
figure('Position',[269 240 660 290])
%Draw search space
subplot(1,2,1);
func_plot(Function_name);
title('Parameter space')
xlabel('x_1');
ylabel('x_2');
zlabel([Function_name,'( x_1 , x_2 )'])
%Draw objective space
subplot(1,2,2);
plot(Convergence_curve,'Color','r')
title('Objective space')
xlabel('Iteration');
ylabel('Best score obtained so far');
axis tight
grid on
box on
legend('MGO')
display(['The best solution obtained by ,MGO is : ', num2str(Best_pos)]);