2024最新群智能优化算法：黑翅鸢算法（Black-winged kite algorithm，BKA）求解23个函数，提供MATLAB代码_群智能算法最新matlab

一、黑翅鸢算法

黑翅鸢算法（Black-winged kite algorithm，BKA）由Wang Jun等人于2024年提出，该算法受黑翅鸢的迁徙和掠食行为启发而得。BKA集成了柯西突变策略和领导者策略，增强了算法的全局搜索能力，提高了算法的收敛速度。

参考文献：

[1]Wang J, Wang W, Hu X, et al. Black-winged kite algorithm: a nature-inspired meta-heuristic for solving benchmark functions and engineering problems[J]. Artificial Intelligence Review, 2024, 57(4): 1-53.

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、部分代码

close all ;
clear
clc
Npop=30;                
Function_name='F8';     % Name of the test function that can be from F1 to F23 ( 
Tmax=300;              
[lb,ub,dim,fobj]=Get_Functions_details(Function_name);
[Best_fit,Best_pos,Convergence_curve]=BKA(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('BKA')
saveas(gca,[Function_name '.jpg']);
display(['The best solution is ', num2str(Best_pos)]);
display(['The best fitness value is ', num2str(Best_fit)]);

四、部分结果

五、完整MATLAB代码