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https://blog.csdn.net/liang674027206/category_12531414.html
极光优化算法（Aurora Optimization Algorithm, AOA）是一种基于自然现象的启发式优化算法，灵感源自极光现象的形成和传播。
该算法由Seyedali Mirjalili等人在2020年提出，旨在通过模拟极光的动态变化来解决复杂的优化问题，尤其适用于多峰和高维的目标函数优化。 AOA的核心思想是将极光视为一种群体智能的表现，其移动和变化反映了自然界中的信息传递与选择机制。在算法中，个体代表潜在解，极光的亮度和颜色变化象征着解的质量和适应度。算法通过模拟极光的波动，使个体在解空间中不断调整和优化，探索更优解的过程。这种动态更新机制能够有效地引导搜索过程，提高解的多样性和搜索效率。
极光优化算法的优点包括简单易于实现、参数设置较少以及良好的全局搜索能力。它在解决工程设计、数据挖掘和其他复杂优化任务中展现出优异的性能。随着对AOA的研究不断深入，相关的改进和变种也相继出现，使其在实际应用中的适应性和效果进一步增强。
部分代码展示：
Matlab
% 📜 After use of code, please users cite to the main paper on PLO:
% Polar Lights Optimizer: Algorithm and Applications in Image Segmentation and Feature Selection:
% Chong Yuan, Dong Zhao, Ali Asghar Heidari, Lei Liu, Yi Chen, Huiling Chen
% Neurocomputing - 2024
%----------------------------------------------------------------------------------------------------------------------------------------------------%
% 📊 You can use and compare with other optimization methods developed recently:
% - (PLO) 2024: 🔗 http://www.aliasgharheidari.com/PLO.html
% - (FATA) 2024: 🔗 http://www.aliasgharheidari.com/FATA.html
% - (ECO) 2024: 🔗 http://www.aliasgharheidari.com/ECO.html
% - (AO) 2024: 🔗 http://www.aliasgharheidari.com/AO.html
% - (PO) 2024: 🔗 http://www.aliasgharheidari.com/PO.html
% - (RIME) 2023: 🔗 http://www.aliasgharheidari.com/RIME.html
% - (INFO) 2022: 🔗 http://www.aliasgharheidari.com/INFO.html
% - (RUN) 2021: 🔗 http://www.aliasgharheidari.com/RUN.html
% - (HGS) 2021: 🔗 http://www.aliasgharheidari.com/HGS.html
% - (SMA) 2020: 🔗 http://www.aliasgharheidari.com/SMA.html
% - (HHO) 2019: 🔗 http://www.aliasgharheidari.com/HHO.html
%____________________________________________________________________________________________________________________________________________________%
function [Best_pos,Bestscore,Convergence_curve]=PLO(N,MaxFEs,lb,ub,dim,fobj)
tic
%% Initialization
FEs = 0;
it = 1;
fitness=inf*ones(N,1);
fitness_new=inf*ones(N,1);
X=initialization(N,dim,ub,lb);
V=ones(N,dim);
X_new=zeros(N,dim);
for i=1:N
fitness(i)=fobj(X(i,:));
FEs=FEs+1;
end
[fitness, SortOrder]=sort(fitness);
X=X(SortOrder,:);
Bestpos=X(1,:);
Bestscore=fitness(1);
Convergence_curve=[];
Convergence_curve(it)=Bestscore;
%% Main loop
while FEs <= MaxFEs
X_sum=sum(X,1);
X_mean=X_sum/N;
w1=tansig((FEs/MaxFEs)^4);
w2=exp(-(2*FEs/MaxFEs)^3);
for i=1:N
a=rand()/2+1;
V(i,:)=1*exp((1-a)/100*FEs);
LS=V(i,:);
GS=Levy(dim).*(X_mean-X(i,:)+(lb+rand(1,dim)*(ub-lb))/2);
X_new(i,:)=X(i,:)+(w1*LS+w2*GS).*rand(1,dim);
end
E =sqrt(FEs/MaxFEs);
A=randperm(N);
for i=1:N
for j=1:dim
if (rand<0.05) && (rand<E)
X_new(i,j)=X(i,j)+sin(rand*pi)*(X(i,j)-X(A(i),j));
end
end
Flag4ub=X_new(i,:)>ub;
Flag4lb=X_new(i,:)<lb;
X_new(i,:)=(X_new(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;
fitness_new(i)=fobj(X_new(i,:));
FEs=FEs+1;
if fitness_new(i)<fitness(i)
X(i,:)=X_new(i,:);
fitness(i)=fitness_new(i);
end
end
[fitness, SortOrder]=sort(fitness);
X=X(SortOrder,:);
if fitness(1)<Bestscore
Bestpos=X(1,:);
Bestscore=fitness(1);
end
it = it + 1;
Convergence_curve(it)=Bestscore;
Best_pos=Bestpos;
end
toc
end
function o=Levy(d)
beta=1.5;
sigma=(gamma(1+beta)*sin(pi*beta/2)/(gamma((1+beta)/2)*beta*2^((beta-1)/2)))^(1/beta);
u=randn(1,d)*sigma;v=randn(1,d);
step=u./abs(v).^(1/beta);
o=step;
end效果展示：
223号资源-源程序：(附带WORD文档)极光优化算法-本人博客有解读资源-CSDN文库https://download.csdn.net/download/LIANG674027206/89929084 👆👆👆👆👆👆👆👆👆👆👆👆👆👆👆👆👆👆👆👆👆👆👆👆👆下载资源链接👆👆👆👆👆👆👆👆👆👆👆👆👆👆👆👆👆👆👆👆👆👆👆👆👆👆
《《《《《《《《更多资源还请持续关注本专栏》》》》》》》
论文与完整源程序_电网论文源程序的博客-CSDN博客https://blog.csdn.net/liang674027206/category_12531414.html