一、巨型犰狳优化算法
巨型犰狳优化算法（Giant Armadillo Optimization，GAO）由Omar Alsayyed等人于2023年提出，该算法模仿了巨型犰狳在野外的自然行为。GAO设计的基本灵感来自巨型犰狳向猎物位置移动和挖掘白蚁丘的狩猎策略。GAO理论在两个阶段进行表达和数学建模：（i）基于模拟巨型犰狳向白蚁丘的运动的探索，以及（ii）基于模拟巨型犰狳的挖掘技能以捕食和撕裂白蚁丘的开发。
参考文献：
1\]Alsayyed O, Hamadneh T, Al-Tarawneh H, Alqudah M, Gochhait S, Leonova I, Malik OP, Dehghani M. Giant Armadillo Optimization: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems. *Biomimetics* . 2023; 8(8):619. [Biomimetics \| Free Full-Text \| Giant Armadillo Optimization: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems](https://doi.org/10.3390/biomimetics8080619 "Biomimetics | Free Full-Text | Giant Armadillo Optimization: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems") ### 二、23个函数介绍  参考文献： \[1\] Yao X, Liu Y, Lin G M. Evolutionary programming made faster\[J\]. IEEE transactions on evolutionary computation, 1999, 3(2):82-102. ### 三、GAO求解23个函数 #### 3.1部分代码 ``` close all ; clear clc Npop=30; Function_name='F1'; % Name of the test function that can be from F1 to F23 ( Tmax=500; [lb,ub,dim,fobj]=Get_Functions_details(Function_name); [Best_fit,Best_pos,Convergence_curve]=GAO(Npop,Tmax,lb,ub,dim,fobj); figure('Position',[100 100 660 290]) %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); semilogy(Convergence_curve,'Color','r','linewidth',3) title('Search space') xlabel('Iteration'); ylabel('Best score obtained so far'); axis tight grid on box on legend('GAO') saveas(gca,[Function_name '.jpg']); display(['The best solution is ', num2str(Best_pos)]); display(['The best fitness value is ', num2str(Best_fit)]); ``` #### 3.2部分结果              ### 四、完整MATLAB代码  