【智能优化算法-正弦余弦算法】基于正弦余弦算法求解高维优化问题附matlab代码

1 内容介绍

ＳＣＡ 算法是基于正弦余弦数学模型进而提出的一种新型优化算法．一般来说，智能优化算法的初 始点往往随机选取一系列个体，虽然无法保证在一 次迭代过程中就能找到最优解，但如果有足够的个 体和迭代次数，并利用目标函数进行反复评价，ＳＣＡ 算法可大大增加获得最优解的概率．

2 仿真代码

%  Sine Cosine Algorithm (SCA)  
%                                                                                                     
%  Main paper:                                                                                        
%  S. Mirjalili, SCA: A Sine Cosine Algorithm for solving optimization problems
%  Knowledge-Based Systems, DOI: http://dx.doi.org/10.1016/j.knosys.2015.12.022
%_______________________________________________________________________________________________
% 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)]);

        

​​​​​​​3 运行结果

4 参考文献

[1]陈聪, 马良, 刘勇. 函数优化的量子正弦余弦算法[J]. 计算机应用研究, 2017, 34(11):5.

[2]崔东文. 正弦余弦算法-投影寻踪水污染物总量分配模型[J]. 水资源保护, 2016, 32(6):8.

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