非洲秃鹫优化算法MATLAB实战

function [Best_vulture1_F,Best_vulture1_X,convergence_curve]=AVOA(pop_size,max_iter,lower_bound,upper_bound,variables_no,fobj)
    % initialize Best_vulture1, Best_vulture2    Best_vulture1_X=zeros(1,variables_no);    Best_vulture1_F=inf;    Best_vulture2_X=zeros(1,variables_no);    Best_vulture2_F=inf;
    %Initialize the first random population of vultures    X=initialization(pop_size,variables_no,upper_bound,lower_bound);
    %%  Controlling parameter    p1=0.6;    p2=0.4;    p3=0.6;    alpha=0.8;    betha=0.2;    gamma=2.5;
    %%Main loop    current_iter=0; % Loop counter
    while current_iter < max_iter        for i=1:size(X,1)            % Calculate the fitness of the population            current_vulture_X = X(i,:);            current_vulture_F=fobj(current_vulture_X);
            % Update the first best two vultures if needed            if current_vulture_F<Best_vulture1_F                Best_vulture1_F=current_vulture_F; % Update the first best bulture                Best_vulture1_X=current_vulture_X;            end            if current_vulture_F>Best_vulture1_F && current_vulture_F<Best_vulture2_F                Best_vulture2_F=current_vulture_F; % Update the second best bulture                Best_vulture2_X=current_vulture_X;            end        end
        a=unifrnd(-2,2,1,1)*((sin((pi/2)*(current_iter/max_iter))^gamma)+cos((pi/2)*(current_iter/max_iter))-1);        P1=(2*rand+1)*(1-(current_iter/max_iter))+a;
        % Update the location        for i=1:size(X,1)            current_vulture_X = X(i,:);  % pick the current vulture back to the population            F=P1*(2*rand()-1);  
            random_vulture_X=random_select(Best_vulture1_X,Best_vulture2_X,alpha,betha);                        if abs(F) >= 1 % Exploration:                current_vulture_X = exploration(current_vulture_X, random_vulture_X, F, p1, upper_bound, lower_bound);            elseif abs(F) < 1 % Exploitation:                current_vulture_X = exploitation(current_vulture_X, Best_vulture1_X, Best_vulture2_X, random_vulture_X, F, p2, p3, variables_no, upper_bound, lower_bound);            end
            X(i,:) = current_vulture_X; % place the current vulture back into the population        end
        current_iter=current_iter+1;        convergence_curve(current_iter)=Best_vulture1_F;
        X = boundaryCheck(X, lower_bound, upper_bound);
        fprintf("In Iteration %d, best estimation of the global optimum is %4.4f \n ", current_iter,Best_vulture1_F );    end
end

%African Vulture Optimization alghorithm
% Read the following publication first and cite if you use it
% @article{abdollahzadeh2021african,%   title={African Vultures Optimization Algorithm: A New Nature-Inspired Metaheuristic Algorithm for Global Optimization Problems},%   author={Abdollahzadeh, Benyamin and Gharehchopogh, Farhad Soleimanian and Mirjalili, Seyedali},%   journal={Computers \& Industrial Engineering},%   pages={107408},%   year={2021},%   publisher={Elsevier},%   url = {https://www.sciencedirect.com/science/article/pii/S0360835221003120}% }

clear all close allclc
% Population size and stoppoing condition pop_size=30;  max_iter=100;  
% Define your objective function's details herefobj = @ObjectiveFunction;variables_no=10;lower_bound=-100; % can be a vector tooupper_bound=100; % can be a vector too      [Best_vulture1_F,Best_vulture1_X,convergence_curve]=AVOA(pop_size,max_iter,lower_bound,upper_bound,variables_no,fobj);

figure 
% Best optimal values for the decision variables subplot(1,2,1)parallelcoords(Best_vulture1_X)xlabel('Decision variables')ylabel('Best estimated values ')box on
% Best convergence curvesubplot(1,2,2)plot(convergence_curve);title('Convergence curve of AVOA')xlabel('Current_iteration');ylabel('Objective value');box on