【特征选择】基于时变正弦和V形传递函数的二元海洋捕食者算法附matlab代码.zip

function [Convergence_curve,Top_predator_fit,Error,Top_predator_pos]=BMPA_TVSinV(SearchAgents_no,Max_iter,dim,fobj) rand('state',sum(100*clock)) %#ok 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,1,0)>0.5; %% Initilization SIGMAi=8; SIGMAf=0.05; GAMMAi=0.3; GAMMAf=0; PHIi=0.3; PHIf=4; Iter=1; FADs=0.2; P=0.5; %% while Iter<=Max_iter % Updating PHI, SIGMA and GAMMA PHI=PHIi+Iter*(PHIf-PHIi)/Max_iter; % Eq. 14 SIGMA=SIGMAi+Iter*(SIGMAf-SIGMAi)/Max_iter; % Eq. 17 GAMMA=GAMMAi+Iter*(GAMMAf-GAMMAi)/Max_iter; % Eq. 18 %------------------- Detecting top predator ----------------- for i=1:size(Prey,1) [fitness(i),Acc]=feval(fobj,Prey(i,:)); if fitness(i)<Top_predator_fit Top_predator_fit=fitness(i); Top_predator_pos=Prey(i,:); Error=1-Acc; end end %------------------- Marine Memory saving ------------------- if Iter==1 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 ------------------- 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 ---------------- 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 ------------------- 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 %% Time-varying Sinus transfer function Prey=max(min (Prey,6),-6); % Eq. 11 S=abs(sin(Prey/PHI)); % Eq. 12 Prey=rand(SearchAgents_no,dim)<S; % Eq. 13 %% %------------------ Detecting top predator ------------------ for i=1:size(Prey,1) [fitness(i),Acc]=feval(fobj,Prey(i,:)); if fitness(i)<Top_predator_fit Top_predator_fit=fitness(i); Top_predator_pos=Prey(i,:); Error=1-Acc; end end %---------------------- Marine Memory saving ---------------- if Iter==1 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; tmpPrey=Prey; %---------- Eddy formation and FADs� effect (Eq 16) ----------- if rand()<FADs U=rand(SearchAgents_no,dim)<FADs; Prey=Prey+CF*((0+rand(SearchAgents_no,dim).*(1)).*U); else r=rand(); Rs=size(Prey,1); stepsize=(FADs*(1-r)+r)*(Prey(randperm(Rs),:)-Prey(randperm(Rs),:)); Prey=Prey+stepsize; end %% Time-varying V-Shaped transfer function Prey=max(min (Prey,6),-6); % Eq. 11 S=abs(2/pi*atan(Prey/SIGMA*pi/2))+GAMMA; % Eq. 15 temp=rand(SearchAgents_no,dim)<S; % Eq. 16 moving=find(temp==1); tmpPrey(moving)=~tmpPrey(moving); Prey=tmpPrey; %% Convergence_curve(Iter)=Top_predator_fit; fprintf('Iteration:%3d\tAccuracy:%7.4f\tFitness:%7.4f\t#Selected Features:%d\n',Iter,((1-Error)*100),Top_predator_fit,(sum(Top_predator_pos,2))); Iter=Iter+1; end