海马优化算法SHO-蜣螂优化算法DBO-爬虫优化算法RSA-卷尾猴优化算法CSA-土拨鼠优化算法PDO【单目标优化算法】在23个测试函数上对比（Matlab代码实现）

% 使用方法

%__________________________________________

% fobj = @YourCostFunction 设定适应度函数

% dim = number of your variables 设定维度

% Max_iteration = maximum number of generations 设定最大迭代次数

% SearchAgents_no = number of search agents 种群数量

% lb=[lb1,lb2,...,lbn] where lbn is the lower bound of variable n 变量下边界

% ub=[ub1,ub2,...,ubn] where ubn is the upper bound of variable n 变量上边界

% If all the variables have equal lower bound you can just

% define lb and ub as two single number numbers

% To run SSA: [Best_pos,Best_score,curve]=SSA(pop,Max_iter,lb,ub,dim,fobj)

%__________________________________________

clc

close all

clear all

rng('default');

SearchAgents_no=30; % Number of search agents 种群数量

Function_name='F6'; % Name of the test function that can be from F1 to F23 (Table 1,2,3 in the paper) 设定适应度函数

Max_iteration=500; % Maximum numbef of iterations 设定最大迭代次数

% Load details of the selected benchmark function

[lb,ub,dim,fobj]=Get_Functions_details(Function_name); %设定边界以及优化函数

%% 海马算法

[fMin , bestX, SHOcurve ] = SHO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj); %开始优化

%% 蜣螂算法

[Alpha_score,Alpha_pos,DBOcurve]=DBO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj); %开始优化

%% 爬虫算法

[gBestScore,gBest,RSAcurve]=RSA(SearchAgents_no,Max_iteration,lb,ub,dim,fobj); %开始优化

%% 卷尾猴算法

[Leader_score,Best, CSAcurve]=CSA(SearchAgents_no,Max_iteration,lb,ub,dim,fobj); %开始优化

%% 土拨鼠算法

[Best_score,Best_pos,PDOcurve]=PDO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj); %开始优化