optimization problem using algorithms(GA, ALO)

hi, Iam begginer on matlab, I have an optimization problem and i need to find the global minimum value, but i did all the procedures by making matrices for every thing and finally i get the minimum value using min and mink function. but in fact i need to learn how to use the algortihms such as GA or ALO to solve my problem. how can i build my functions and formulate my problem to be compatible with these algorithm and how can i use my parameters and constraints. Thanks in advance

Answer 1

[Rupesh Gosavi]

The best way to learn about Genetic Algorithm (GA) is to refer to MATLAB documentation. The plenty of videos, documentations, examples and webinars will surely help you to solve your problem. Take a look at this here.

[Mohammad Al ja'idi]

thanks so much, i will follow the link to see how it can help me

Answer 2

[bahar vojdani]

fobj = @ReadGrasshopperFile;
dim = 3;
Max_iteration = 100;
SearchAgents_no = 300;
lb=[1,1,1]; 
ub=[10,10,10];
[Best_score,Best_pos,cg_curve]=ALO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj);

function [Elite_antlion_fitness,Elite_antlion_position,Convergence_curve]=ALO(N,Max_iter,lb,ub,dim,fobj,handles)

% Initialize the positions of antlions and ants
antlion_position=initialization(N,dim,ub,lb);
ant_position=initialization(N,dim,ub,lb);

% Initialize variables to save the position of elite, sorted antlions, 
% convergence curve, antlions fitness, and ants fitness
Sorted_antlions=zeros(N,dim);
Elite_antlion_position=zeros(1,dim);
Elite_antlion_fitness=inf;
Convergence_curve=zeros(1,Max_iter);
antlions_fitness=zeros(1,N);
ants_fitness=zeros(1,N);

% Calculate the fitness of initial antlions and sort them
for i=1:size(antlion_position,1)
    antlions_fitness(1,i)=fobj(antlion_position(i,:)); 
end

[sorted_antlion_fitness,sorted_indexes]=sort(antlions_fitness);

for newindex=1:N
    Sorted_antlions(newindex,:)=antlion_position(sorted_indexes(newindex),:);
end

Elite_antlion_position=Sorted_antlions(1,:);
Elite_antlion_fitness=sorted_antlion_fitness(1);

% Main loop start from the second iteration since the first iteration 
% was dedicated to calculating the fitness of antlions
Current_iter=2; 
while Current_iter<Max_iter+1
    
    % This for loop simulate random walks
    for i=1:size(ant_position,1)
        % Select ant lions based on their fitness (the better anlion the higher chance of catching ant)
        Rolette_index=RouletteWheelSelection(1./sorted_antlion_fitness);
        if Rolette_index==-1  
            Rolette_index=1;
        end
        
        % RA is the random walk around the selected antlion by rolette wheel
        RA=Random_walk_around_antlion(dim,Max_iter,lb,ub, Sorted_antlions(Rolette_index,:),Current_iter);
        
        % RA is the random walk around the elite (best antlion so far)
        [RE]=Random_walk_around_antlion(dim,Max_iter,lb,ub, Elite_antlion_position(1,:),Current_iter);
        
        ant_position(i,:)= (RA(Current_iter,:)+RE(Current_iter,:))/2; % Equation (2.13) in the paper          
    end
    
    for i=1:size(ant_position,1)  
        
        % Boundar checking (bring back the antlions of ants inside search
        % space if they go beyoud the boundaries
        Flag4ub=ant_position(i,:)>ub;
        Flag4lb=ant_position(i,:)<lb;
        ant_position(i,:)=(ant_position(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;  
        
        ants_fitness(1,i)=fobj(ant_position(i,:));    
        All_fitness(1,i)=ants_fitness(1,i);
        
    end
    
    % Update antlion positions and fitnesses based of the ants (if an ant 
    % becomes fitter than an antlion we assume it was cought by the antlion  
    % and the antlion update goes to its position to build the trap)
    double_population=[Sorted_antlions;ant_position];
    double_fitness=[sorted_antlion_fitness ants_fitness];
    
    [double_fitness_sorted, I]=sort(double_fitness);
    double_sorted_population=double_population(I,:);
    
    antlions_fitness=double_fitness_sorted(1:N);
    Sorted_antlions=double_sorted_population(1:N,:);
    
    % Update the position of elite if any antlinons becomes fitter than it
    if antlions_fitness(1)<Elite_antlion_fitness 
        Elite_antlion_position=Sorted_antlions(1,:);
        Elite_antlion_fitness=antlions_fitness(1);
    end
    
    % Keep the elite in the population
    Sorted_antlions(1,:)=Elite_antlion_position;
    antlions_fitness(1)=Elite_antlion_fitness;
    
    % Update the convergence curve
    Convergence_curve(Current_iter)=Elite_antlion_fitness;
    
    
    
    if Current_iter>2
        line([Current_iter-1 Current_iter], [Convergence_curve(Current_iter-1) Convergence_curve(Current_iter)],'Color',[0    0.4470    0.7410])
        xlabel('Iteration');
        ylabel('Best score obtained so far');        
        drawnow
    end
    
    results = get(handles.uitable1,'data');
    results{2,1}=Current_iter;
    results{2,2}=Elite_antlion_fitness;
    set(handles.uitable1,'data',results);
    
    Current_iter=Current_iter+1; 
end
end

@Seyedali Mirjalili

Hello,

I used your valuable optimization (ALO) source codes; This is amazing. Unfortunately, when I run the code, it gives me some error " unable to define local function ALO."

I really appreciate any help you can provide.