正余弦算法（SCA）求解单目标优化问题

Posted 2021-08-17 这是一个很随便的名字

【Matlab源码】

正弦余弦算法（SCA）是解决优化问题提供了新的优化技术。SCA 使用基于正弦和余弦函数的数学模型创建多个初始随机候选解决方案，并要求它们向外波动或朝着最佳解决方案波动。该算法还集成了几个随机变量和自适应变量，以强调在优化的不同里程碑中对搜索空间的探索和利用。

SCA 旨在解决单目标优化问题

代码：

% You can simply define your cost function in a seperate file and load its handle to fobj 
% The initial parameters that you need are:
%__________________________________________
% fobj = @YourCostFunction
% dim = number of your variables
% Max_iteration = maximum number of iterations
% SearchAgents_no = number of search agents
% lb=[lb1,lb2,...,lbn] where lbn is the lower bound of variable n
% ub=[ub1,ub2,...,ubn] where ubn is the upper bound of variable n
% If all the variables have equal lower bound you can just
% define lb and ub as two single numbers

% To run SCA: [Best_score,Best_pos,cg_curve]=SCA(SearchAgents_no,Max_iteration,lb,ub,dim,fobj)
%______________________________________________________________________________________________

clear all 
clc

SearchAgents_no=30; % Number of search agents

Function_name='F1'; % Name of the test function that can be from F1 to F23 (Table 1,2,3 in the paper)

Max_iteration=1000; % Maximum numbef of iterations

% Load details of the selected benchmark function
[lb,ub,dim,fobj]=Get_Functions_details(Function_name);

[Best_score,Best_pos,cg_curve]=SCA(SearchAgents_no,Max_iteration,lb,ub,dim,fobj);

figure('Position',[284   214   660   290])
%Draw search space
subplot(1,2,1);
func_plot(Function_name);
title('Test function')
xlabel('x_1');
ylabel('x_2');
zlabel([Function_name,'( x_1 , x_2 )'])
grid off

%Draw objective space
subplot(1,2,2);
semilogy(cg_curve,'Color','b')
title('Convergence curve')
xlabel('Iteration');
ylabel('Best flame (score) obtained so far');

axis tight
grid off
box on
legend('SCA')

display(['The best solution obtained by SCA is : ', num2str(Best_pos)]);
display(['The best optimal value of the objective funciton found by SCA is : ', num2str(Best_score)]);

        

运行结果：