【智能优化算法】非洲秃鹫优化算法:一种新的全局优化问题的自然启发的元启发式算法

非洲秃鹫优化算法（AVOA）发表在中科院一区Computers & Industrial Engineering期刊上的论文“African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems"

01.引言

元启发式算法在求解优化问题中起着至关重要的作用。大多数这样的算法都是受到集体智慧和自然界生物觅食的启发。本文以非洲秃鹫的生活方式为灵感，提出了一种新的元启发式方法。该算法被命名为非洲秃鹫优化算法(AVOA)，它模拟了非洲秃鹫的觅食和导航行为。为了评估AVOA的性能，首先在36个标准基准函数上进行了测试。然后进行了比较研究，证明了所提出的算法与几种现有算法相比的优越性。为了展示AVOA算法的适用性及其黑箱特性，本文利用AVOA算法对11个工程设计问题求最优解。

0.2.代码流程图

03.部分代码

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