# 【智能优化算法-麻雀搜索算法】基于萤火虫结合麻雀搜索算法求解单目标优化问题附matlab代码

## 2 部分代码

% 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)

%__________________________________________

clear all

clc

close all

rng('default');

SearchAgents_no=50; % Number of search agents 种群数量

Function_name='F2'; % 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=300;

% 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

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

figure('Position',[269   240   660   290])

%Draw search space

subplot(1,2,1);

func_plot(Function_name);

title('Parameter space')

xlabel('x_1');

ylabel('x_2');

zlabel([Function_name,'( x_1 , x_2 )'])

%Draw objective space

subplot(1,2,2);

plot(SSA_curve,'Color','r','linewidth',2)

hold on;

title('Objective space')

xlabel('迭代');

ylabel('Best score obtained so far');

axis tight

grid on

box on

legend('FASSA')

## 4 参考文献

[1]肖海飞, 曾国辉, 杜涛,等. 基于麻雀搜索算法的PMSM智能控制器设计[J]. 电力电子技术, 2022(056-001).​

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