【优化求解】基于麻雀搜索算法SSA求解最优目标matlab源码

1 简介

2 部分代码

%_________________________________________________________________________________
% Salp Swarm Algorithm (SSA) source codes version 1.0
%

% You can simply define your cost 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 generations
% 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 number numbers

% To run SSA: [Best_score,Best_pos,SSA_cg_curve]=SSA(SearchAgents_no,Max_iteration,lb,ub,dim,fobj)
%__________________________________________

clear all 
clc
SearchAgents_no=30; % Number of search agents
Function_name='F5'; % Name of the test function that can be from F1 to F23 ( 
Max_iteration=100; % Maximum numbef of iterations
% Load details of the selected benchmark function
[lb,ub,dim,fobj]=Get_Functions_details(Function_name);
[Best_score,Best_pos,SSA_cg_curve]=SSA(SearchAgents_no,Max_iteration,lb,ub,dim,fobj);
figure('Position',[500 500 660 290])
% %Draw search space
subplot(1,2,1);
func_plot(Function_name);
title('Parameter space')
xlabel('x_1');
ylabel('x_2');
zlabel(['( x_1 , x_2 )'])
%Draw objective space
subplot(1,2,2);
semilogy(SSA_cg_curve,'Color','r')
title('Objective space')
xlabel('Iteration');
ylabel('Best score obtained so far');
axis tight
grid on
box on
legend('SSA')
display(['The best solution obtained by SSA is \m ', num2str(Best_pos)]);
display(['The best optimal value of the objective funciton found by SSA is \n ', num2str(Best_score)]);
img =gcf;  %获取当前画图的句柄
print(img, '-dpng', '-r600', './img.png')         %即可得到对应格式和期望dpi的图像
       

3 仿真结果

4 参考文献

[1]田慕玲, 杨宇博, 许春雨,等. 一种基于混沌麻雀搜索算法的煤岩分界图像增强方法:. ​