【优化算法】多目标利希滕贝格算法（MOLA）【含Matlab源码 1434期】

一、获取代码方式

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二、部分源代码

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%              MULTI-OBJECTIVE LICHTENBERG ALGORITHM (MOLA)

%      

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

close all
clear all
clc
format long

set(0,'DefaultAxesFontName', 'Times New Roman')
set(0,'DefaultAxesFontSize', 14)
set(0,'DefaultTextFontname', 'Times New Roman')
set(0,'DefaultTextFontSize', 26)

% Search Space
LB = zeros(1,10);  %lower bounds
UB = ones(1,10);   %upper bounds

% Optimizator Parameters
pop = 100;         %Population
n_iter = 100;      %Max number os iterations/gerations
ref = 0.4;         %if more than zero, a second LF is created with refinement % the size of the other
IntCon = [];       %Avoid if there are no variables that must be integers. Ex.: IntCon = [1,2];   
Np = 100000;       %Number of Particles (If 3D, better more than 10000)
S_c = 1;           %Stick Probability: Percentage of particles that can don´t stuck in the
                   %cluster. Between 0 and 1. Near 0 there are more aggregate, the density of
                   %cluster is bigger and difusity is low. Near 1 is the opposite. 
Rc = 150;          %Creation Radius (if 3D, better be less than 80, untill 150)
M = 0;             %If M = 0, no lichtenberg figure is created (it is loaded a optimized figure); if 1, a single is created and used in all iterations; If 2, one is created for each iteration.(creating an LF figure takes about 2 min)
d = length(UB);    %problem dimension
ngrid = 30;        %Number of grids in each dimension
Nr = 100;          %Maximum number of solutions in PF

% MOLA
[x,fval] = LA_optimization(@objectives,d,pop,LB,UB,ref,n_iter,Np,Rc,S_c,M,ngrid,Nr,IntCon,@constraint);

% Figure
figure
plot(fval(:,1),fval(:,2),'ZDataSource','',...
    'MarkerFaceColor',[0 0 1],...
    'MarkerEdgeColor',[0 0 0],...
    'MarkerSize',8,...
    'Marker','o',...
    'LineWidth',1.1,...
    'LineStyle','none',...
    'Color',[0 0 0]);
hold on
box on
legend('PF');
title('Non-dominated Solutions','fontweight','bold');
xlabel('Y_1')
ylabel('Y_2')


  

 

  
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三、运行结果

四、matlab版本及参考文献

1 matlab版本
2014a

2 参考文献
[1] 包子阳,余继周,杨杉.智能优化算法及其MATLAB实例（第2版）[M].电子工业出版社，2016.
[2]张岩,吴水根.MATLAB优化算法源代码[M].清华大学出版社，2017.

文章来源: qq912100926.blog.csdn.net，作者：海神之光，版权归原作者所有，如需转载，请联系作者。

原文链接：qq912100926.blog.csdn.net/article/details/120917589