1 簡介
為了提高極限學習機(ELM)資料分類的精度,提出了海洋捕食者算法(SOA)的ELM分類器參數優化方法(MPA-KELM),将CV訓練所得多個模型的平均精度作為MPA的适應度評價函數,為ELM的參數優化提供評價标準,用獲得MPA優化最優參數的ELM算法進行資料分類.利用UCI中資料集進行仿真.
2 部分代碼
%_________________________________________________________________________
% Marine Predators Algorithm source code (Developed in MATLAB R2015a)
%
function [Top_predator_fit,Top_predator_pos,Convergence_curve]=MPA(SearchAgents_no,Max_iter,lb,ub,dim,fobj)
Top_predator_pos=zeros(1,dim);
Top_predator_fit=inf;
Convergence_curve=zeros(1,Max_iter);
stepsize=zeros(SearchAgents_no,dim);
fitness=inf(SearchAgents_no,1);
Prey=initialization(SearchAgents_no,dim,ub,lb);
Xmin=repmat(ones(1,dim).*lb,SearchAgents_no,1);
Xmax=repmat(ones(1,dim).*ub,SearchAgents_no,1);
Iter=0;
FADs=0.2;
P=0.5;
while Iter<Max_iter
%------------------- Detecting top predator -----------------
for i=1:size(Prey,1)
Flag4ub=Prey(i,:)>ub;
Flag4lb=Prey(i,:)<lb;
Prey(i,:)=(Prey(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;
fitness(i,1)=fobj(Prey(i,:));
if fitness(i,1)<Top_predator_fit
Top_predator_fit=fitness(i,1);
Top_predator_pos=Prey(i,:);
end
end
%------------------- Marine Memory saving -------------------
if Iter==0
fit_old=fitness; Prey_old=Prey;
end
Inx=(fit_old<fitness);
Indx=repmat(Inx,1,dim);
Prey=Indx.*Prey_old+~Indx.*Prey;
fitness=Inx.*fit_old+~Inx.*fitness;
fit_old=fitness; Prey_old=Prey;
%------------------------------------------------------------
Elite=repmat(Top_predator_pos,SearchAgents_no,1); %(Eq. 10)
CF=(1-Iter/Max_iter)^(2*Iter/Max_iter);
RL=0.05*levy(SearchAgents_no,dim,1.5); %Levy random number vector
RB=randn(SearchAgents_no,dim); %Brownian random number vector
for i=1:size(Prey,1)
for j=1:size(Prey,2)
R=rand();
%------------------ Phase 1 (Eq.12) -------------------
if Iter<Max_iter/3
stepsize(i,j)=RB(i,j)*(Elite(i,j)-RB(i,j)*Prey(i,j));
Prey(i,j)=Prey(i,j)+P*R*stepsize(i,j);
%--------------- Phase 2 (Eqs. 13 & 14)----------------
elseif Iter>Max_iter/3 && Iter<2*Max_iter/3
if i>size(Prey,1)/2
stepsize(i,j)=RB(i,j)*(RB(i,j)*Elite(i,j)-Prey(i,j));
Prey(i,j)=Elite(i,j)+P*CF*stepsize(i,j);
else
stepsize(i,j)=RL(i,j)*(Elite(i,j)-RL(i,j)*Prey(i,j));
Prey(i,j)=Prey(i,j)+P*R*stepsize(i,j);
end
%----------------- Phase 3 (Eq. 15)-------------------
else
stepsize(i,j)=RL(i,j)*(RL(i,j)*Elite(i,j)-Prey(i,j));
Prey(i,j)=Elite(i,j)+P*CF*stepsize(i,j);
end
end
end
%------------------ Detecting top predator ------------------
for i=1:size(Prey,1)
Flag4ub=Prey(i,:)>ub;
Flag4lb=Prey(i,:)<lb;
Prey(i,:)=(Prey(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;
fitness(i,1)=fobj(Prey(i,:));
if fitness(i,1)<Top_predator_fit
Top_predator_fit=fitness(i,1);
Top_predator_pos=Prey(i,:);
end
end
%---------------------- Marine Memory saving ----------------
if Iter==0
fit_old=fitness; Prey_old=Prey;
end
Inx=(fit_old<fitness);
Indx=repmat(Inx,1,dim);
Prey=Indx.*Prey_old+~Indx.*Prey;
fitness=Inx.*fit_old+~Inx.*fitness;
fit_old=fitness; Prey_old=Prey;
%---------- Eddy formation and FADs? effect (Eq 16) -----------
if rand()<FADs
U=rand(SearchAgents_no,dim)<FADs;
Prey=Prey+CF*((Xmin+rand(SearchAgents_no,dim).*(Xmax-Xmin)).*U);
else
r=rand(); Rs=size(Prey,1);
stepsize=(FADs*(1-r)+r)*(Prey(randperm(Rs),:)-Prey(randperm(Rs),:));
Prey=Prey+stepsize;
end
Iter=Iter+1;
Convergence_curve(Iter)=Top_predator_fit;
end 3 仿真結果
4 參考文獻
[1]郁智博. 基于模糊神經網絡和ELM的分類算法的研究[D]. 東北大學, 2013.
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