多目标蚂蚁狮子优化算法（MOALO）（Matlab代码实现）

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目录

​​💥1 概述​​

​​📚2 运行结果​​

​​🎉3 参考文献​​

​​🌈4 Matlab代码实现​​

💥1 概述

多目标蚂蚁狮子优化算法（MOALO）。首先使用存储库来存储到目前为止获得的非主导帕累托最优解。然后使用轮盘机制从该存储库中选择解决方案，该机制基于解决方案作为蚁狮的覆盖范围，以引导蚂蚁进入多目标搜索空间的有前途的区域。

📚2 运行结果

🎉3 参考文献

[1]Mirjalili, Seyedali, Pradeep Jangir, and Shahrzad Saremi.  Multi-objective ant lion optimizer: a multi-objective optimization   algorithm for solving engineering problems." Applied Intelligence      

(2016): 1-17, DOI: http://dx.doi.org/10.1007/s10489-016-0825-8         

部分代码：

function [RWs]=Random_walk_around_antlion(Dim,max_iter,lb, ub,antlion,current_iter)
if size(lb,1) ==1 && size(lb,2)==1 %Check if the bounds are scalar
    lb=ones(1,Dim)*lb;
    ub=ones(1,Dim)*ub;
end

if size(lb,1) > size(lb,2) %Check if boundary vectors are horizontal or vertical
    lb=lb';
    ub=ub';
end

I=1; % I is the ratio in Equations (2.10) and (2.11)

if current_iter>max_iter/10
    I=1+100*(current_iter/max_iter);
end

if current_iter>max_iter/2
    I=1+1000*(current_iter/max_iter);
end

if current_iter>max_iter*(3/4)
    I=1+10000*(current_iter/max_iter);
end

if current_iter>max_iter*(0.9)
    I=1+100000*(current_iter/max_iter);
end

if current_iter>max_iter*(0.95)
    I=1+1000000*(current_iter/max_iter);
end

% Dicrease boundaries to converge towards antlion
lb=lb/(I); % Equation (2.10) in the paper
ub=ub/(I); % Equation (2.11) in the paper

% Move the interval of [lb ub] around the antlion [lb+anlion ub+antlion]
if rand<0.5
    lb=lb+antlion; % Equation (2.8) in the paper
else
    lb=-lb+antlion;
end

if rand>=0.5
    ub=ub+antlion; % Equation (2.9) in the paper
else
    ub=-ub+antlion;
end

% This function creates n random walks and normalize accroding to lb and ub
% vectors 
for i=1:Dim
    X = [0 cumsum(2*(rand(max_iter,1)>0.5)-1)']; % Equation (2.1) in the paper
    %[a b]--->[c d]
    a=min(X);
    b=max(X);
    c=lb(i);
    d=ub(i);      
    X_norm=((X-a).*(d-c))./(b-a)+c; % Equation (2.7) in the paper
    RWs(:,i)=X_norm;
end

 

​​🌈​​4 Matlab代码实现