1 模型
2 部分代码
%______________________________________________________________________________________________% Moth-Flame Optimization Algorithm (MFO) % Main paper: % S. Mirjalili, Moth-Flame Optimization Algorithm: A Novel Nature-inspired Heuristic Paradigm, % Knowledge-Based Systems, DOI: http://dx.doi.org/10.1016/j.knosys.2015.07.006%_______________________________________________________________________________________________% 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 MFO: [Best_score,Best_pos,cg_curve]=MFO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj)%______________________________________________________________________________________________clear all clcSearchAgents_no=30; % Number of search agentsFunction_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]=MFO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj);figure('Position',[284 214 660 290])%Draw search spacesubplot(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 spacesubplot(1,2,2);semilogy(cg_curve,'Color','b')title('Convergence curve')xlabel('Iteration');ylabel('Best flame (score) obtained so far');axis tightgrid offbox onlegend('MFO')display(['The best solution obtained by MFO is : ', num2str(Best_pos)]);display(['The best optimal value of the objective funciton found by MFO is : ', num2str(Best_score)]);
%________________________________________________
% Moth-Flame Optimization Algorithm (MFO)
% Main paper:
% S. Mirjalili, Moth-Flame Optimization Algorithm: A Novel Nature-inspired Heuristic Paradigm,
% Knowledge-Based Systems, DOI: dx.doi.org/10.1016/j.k…
%_________________________________________________
% 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 MFO: [Best_score,Best_pos,cg_curve]=MFO(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]=MFO(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('MFO')
display(['The best solution obtained by MFO is : ', num2str(Best_pos)]);
display(['The best optimal value of the objective funciton found by MFO is : ', num2str(Best_score)]);
3 仿真结果
4 参考文献
[1]王万良等. "一种基于多目标飞蛾算法的小型水电站优化调度方法.".
