多节优化器（Multi-Verse Optimizer (MVO) ）解决单目标优化问题

灰太狼 2023-01-24 04:21 15阅读 0赞

# 【Matlab源码】 #

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3VwZGF0ZTc_size_16_color_FFFFFF_t_70][]

**Multi-Verse Optimizer (MVO) 的** 灵感来源于 宇宙学中的三个概念：白洞、黑洞和虫洞。开发这三个概念的数学模型分别用于执行探索、开发和局部搜索。

MVO 旨在解决单目标优化问题。

代码：

% 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 MVO: [Best_score,Best_pos,cg_curve]=MVO(Universes_no,Max_iteration,lb,ub,dim,fobj)
    %__________________________________________
    
    clear all 
    clc
    
    Universes_no=60; %Number of search agents (universes)
    
    Function_name='F17'; %Name of the test function that can be from F1 to F23 (Table 1,2,3 in the paper)
    
    Max_iteration=500; %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]=MVO(Universes_no,Max_iteration,lb,ub,dim,fobj);
    
    figure('Position',[290   206   648   287])
    
    %Draw the 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
    shading interp;
    light;
    lighting phong;
    shading interp;
    
    %Draw the convergence curve
    subplot(1,2,2);
    semilogy(cg_curve,'Color','r')
    title('Convergence curve')
    xlabel('Iteration');
    ylabel('Best score obtained so far');
    
    axis tight
    grid off
    box on
    legend('MVO')
    
    display(['The best solution obtained by MVO is : ', num2str(Best_pos)]);
    display(['The best optimal value of the objective funciton found by MVO is : ', num2str(Best_score)]);

运行结果：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3VwZGF0ZTc_size_16_color_FFFFFF_t_70 1][]

获取**Multi-Verse Optimizer (MVO)完整源码：**[https://ai.52learn.online/code/22][https_ai.52learn.online_code_22]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3VwZGF0ZTc_size_16_color_FFFFFF_t_70]: /images/20221004/0b7901ef2d9b400abb95f4f2d8ac7393.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3VwZGF0ZTc_size_16_color_FFFFFF_t_70 1]: /images/20221004/9ee8b96f15984b55ad702f11a439072c.png
[https_ai.52learn.online_code_22]: https://ai.52learn.online/code/22