RIME-VMD【23年新算法】霜冰优化算法优化VMD变分模态分解 可直接运行 Matlab
1.利用霜冰优化算法优化VMD中的参数k、a,适应度函数为样本熵。分解效果好,包含分解效果图、频率图、边际图、收敛曲线等图,可完全满足您的需求~
2.霜冰优化算法RIME是23年提出的新算法,还没人用过,适合作为创新点,包含VMD超参数优化迭代过程图,凸显每次迭代过程的变化。~
3.附赠测试数据 直接运行main即可一键出图~
4.直接替换Excel文件即可使用 适合新手小白 注释清晰~
function [Best_rime_rate,Best_rime,Convergence_curve,result]=RIME(N,Max_iter,lb,ub,dim,fobj)
% initialize position
Best_rime=zeros(1,dim);
Best_rime_rate=inf;%change this to -inf for maximization problems
Rimepop=initialization(N,dim,ub,lb);%Initialize the set of random solutions
Lb=lb.*ones(1,dim);% lower boundary
Ub=ub.*ones(1,dim);% upper boundary
it=1;%Number of iterations
Convergence_curve=zeros(1,Max_iter);
Rime_rates=zeros(1,N);%Initialize the fitness value
newRime_rates=zeros(1,N);
W = 5;%Soft-rime parameters, discussed in subsection 4.3.1 of the paper
%Calculate the fitness value of the initial position
for i=1:N
Rime_rates(1,i)=fobj(Rimepop(i,:));%Calculate the fitness value for each search agent
%Make greedy selections
if Rime_rates(1,i)<Best_rime_rate
Best_rime_rate=Rime_rates(1,i);
Best_rime=Rimepop(i,:);
end
end
%GBestF = Rime_rates(1);%全局最优适应度值
% Main loop
while it <= Max_iter
it
RimeFactor = (rand-0.5)*2*cos((pi*it/(Max_iter/10)))*(1-round(it*W/Max_iter)/W);%Parameters of Eq.(3),(4),(5)
E =(it/Max_iter)^0.5;%Eq.(6)
newRimepop = Rimepop;%Recording new populations
normalized_rime_rates=normr(Rime_rates);%Parameters of Eq.(7)
for i=1:N
%Boundary absorption
Flag4ub=newRimepop(i,:)>ub;
Flag4lb=newRimepop(i,:)<lb;
newRimepop(i,:)=(newRimepop(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;
newRime_rates(1,i)=fobj(newRimepop(i,:));
%Positive greedy selection mechanism
if newRime_rates(1,i)<Rime_rates(1,i)
Rime_rates(1,i) = newRime_rates(1,i);
Rimepop(i,:) = newRimepop(i,:);
if newRime_rates(1,i)< Best_rime_rate
Best_rime_rate=Rime_rates(1,i);
Best_rime=Rimepop(i,:);
end
end
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原文链接:https://blog.csdn.net/kjm13182345320/article/details/119920826
[1] https://blog.csdn.net/kjm13182345320/article/details/129215161
[2] https://blog.csdn.net/kjm13182345320/article/details/128105718