【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

本文主要是介绍【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，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

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_vulture2Best_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 vulturesX=initialization(pop_size,variables_no,upper_bound,lower_bound);%%  Controlling parameterp1=0.6;p2=0.4;p3=0.6;alpha=0.8;betha=0.2;gamma=2.5;%%Main loopcurrent_iter=0; % Loop counterwhile current_iter < max_iterfor i=1:size(X,1)% Calculate the fitness of the populationcurrent_vulture_X = X(i,:);current_vulture_F=fobj(current_vulture_X);% Update the first best two vultures if neededif current_vulture_F<Best_vulture1_FBest_vulture1_F=current_vulture_F; % Update the first best bultureBest_vulture1_X=current_vulture_X;endif current_vulture_F>Best_vulture1_F && current_vulture_F<Best_vulture2_FBest_vulture2_F=current_vulture_F; % Update the second best bultureBest_vulture2_X=current_vulture_X;endenda=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 locationfor i=1:size(X,1)current_vulture_X = X(i,:);  % pick the current vulture back to the populationF=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);endX(i,:) = current_vulture_X; % place the current vulture back into the populationendcurrent_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