一、差异化创意搜索算法

差异化创意搜索（Differentiated Creative Search ，DCS）算法由Poomin Duankhan 等人于2024年提出，DCS将独特的知识获取过程与创造性的现实主义范式相结合，通过采用双重策略方法，提高算法效率。

参考文献：

 [1]Duankhan P, Sunat K, Chiewchanwattana S, et al. The Differentiated Creative search (DCS): Leveraging Differentiated knowledge-acquisition and Creative realism to address complex optimization problems[J]. Expert Systems with Applications, 2024: 123734. https://doi.org/10.1016/j.eswa.2024.123734

二、23个函数简介

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='F7';     % 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]=DCS(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('DCS')
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