【特征选择】基于蜻蜓算法实现二进制特征选择问题附matlab代码(DragonflyAlgorithm，DA) 发资源.zip

%-------------------------------------------------------------------------% % Binary Dragonfly Algorithm (BDA) source codes demo version % % % % Programmer: Jingwei Too % % % % E-Mail: [email protected] % %-------------------------------------------------------------------------% function [sFeat,Sf,Nf,curve]=jBDA(feat,label,N,T,Dmax) %---Inputs----------------------------------------------------------------- % feat: features % label: labelling % N: Number of dragonflies % T: Maximum number of iterations % Dmax: Maximum velocity %---Outputs---------------------------------------------------------------- % sFeat: Selected features % Sf: Selected feature index % Nf: Number of selected features % curve: Convergence curve %-------------------------------------------------------------------------- fun=@jFitnessFunction; D=size(feat,2); X=zeros(N,D); DX=zeros(N,D); for i=1:N for d=1:D if rand() > 0.5 X(i,d)=1; end end end curve=inf;fit=zeros(1,N); fitF=inf; fitE=-inf; t=1; Xnew=zeros(N,D); figure(1); clf; axis([1 100 0 0.2]); xlabel('Number of iterations'); ylabel('Fitness Value'); title('Convergence Curve'); grid on; %---Iteration start------------------------------------------------------- while t <= T for i=1:N fit(i)=fun(feat,label,X(i,:)); if fit(i) < fitF fitF=fit(i); Xf=X(i,:); end if fit(i) > fitE fitE=fit(i); Xe=X(i,:); end end w=0.9-t*((0.9-0.4)/T); rate=0.1-t*((0.1-0)/(T/2)); if rate < 0, rate=0; end s=2*rand()*rate; a=2*rand()*rate; c=2*rand()*rate; f=2*rand(); e=rate; for i=1:N index=0; nNeighbor=0; Xn=zeros(1,D); DXn=zeros(1,D); for j=1:N if i~=j index=index+1; nNeighbor=nNeighbor+1; DXn(index,:)=DX(j,:); Xn(index,:)=X(j,:); end end S=repmat(X(i,:),nNeighbor,1)-Xn; S=-sum(S,1); A=sum(DXn,1)/nNeighbor; C=sum(Xn,1)/nNeighbor; C=C-X(i,:); F=Xf-X(i,:); E=Xe+X(i,:); for d=1:D dX=(s*S(d)+a*A(d)+c*C(d)+f*F(d)+e*E(d))+w*DX(i,d); dX(dX > Dmax)=Dmax; dX(dX < -Dmax)=-Dmax; DX(i,d)=dX; TF=abs(DX(i,d)/sqrt(((DX(i,d)^2)+1))); if rand() < TF Xnew(i,d)=1-X(i,d); else Xnew(i,d)=X(i,d); end end end X=Xnew; curve(t)=fitF; pause(1e-20); hold on; CG=plot(t,fitF,'Color','r','Marker','.'); set(CG,'MarkerSize',5); t=t+1; end Pos=1:D; Sf=Pos(Xf==1); Nf=length(Sf); sFeat=feat(:,Sf); end