​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:. 

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部分理论引用网络文献，若有侵权联系博主删除。