💥💥💥💞💞💞歡迎來到本部落格❤️❤️❤️💥💥💥
🏆部落客優勢：🌞🌞🌞部落格内容盡量做到思維缜密，邏輯清晰，為了友善讀者。
⛳️座右銘：行百裡者，半于九十。
目錄
💥1 概述
📚2 運作結果
🎉3 參考文獻
🌈4 Matlab代碼實作
💥1 概述
多目标螞蟻獅子優化算法（MOALO）。首先使用存儲庫來存儲到目前為止獲得的非主導帕累托最優解。然後使用輪盤機制從該存儲庫中選擇解決方案，該機制基于解決方案作為蟻獅的覆寫範圍，以引導螞蟻進入多目标搜尋空間的有前途的區域。
📚2 運作結果
🎉3 參考文獻
[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
部分代碼：
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