Matlab实现SO-ELM蛇算法优化极限学习机多输入单输出回归预测（Matlab完整源码和数据)

function [Best_pos, Best_score,curve] = SO(pop,Max_iter,lb,ub,dim,fobj) %initial vec_flag=[1,-1]; Threshold=0.25; Thresold2= 0.6; C1=0.5; C2=.05; C3=2; % if(max(size(ub)) == 1) % ub = ub.*ones(1,dim); % lb = lb.*ones(1,dim); % end X=initialization(pop,dim,ub,lb) %X=lb+rand(pop,dim)*(ub-lb); for i=1:pop fitness(i)=feval(fobj,X(i,:)); end [GYbest, gbest] = min(fitness); Best_pos = X(gbest,:); %Diving the swarm into two equal groups males and females Nm=round(pop/2);%eq.(2&3) Nf=pop-Nm; Xm=X(1:Nm,:); Xf=X(Nm+1:pop,:); fitness_m=fitness(1:Nm); fitness_f=fitness(Nm+1:pop); [fitnessBest_m, gbest1] = min(fitness_m); Xbest_m = Xm(gbest1,:); [fitnessBest_f, gbest2] = min(fitness_f); Xbest_f = Xf(gbest2,:); for t = 1:Max_iter Temp=exp(-((t)/Max_iter)); %eq.(4) Q=C1*exp(((t-Max_iter)/(Max_iter)));%eq.(5) if Q>1 Q=1; end % Exploration Phase (no Food) if Q<Threshold for i=1:Nm for j=1:1:dim rand_leader_index = floor(Nm*rand()+1); X_randm = Xm(rand_leader_index, :); flag_index = floor(2*rand()+1); Flag=vec_flag(flag_index); Am=exp(-fitness_m(rand_leader_index)/(fitness_m(i)+eps));%eq.(7) Xnewm(i,:)=X_randm(j)+Flag*C2*Am*((ub-lb)*rand+lb);%eq.(6) end end for i=1:Nf for j=1:1:dim rand_leader_index = floor(Nf*rand()+1); X_randf = Xf(rand_leader_index, :); flag_index = floor(2*rand()+1); Flag=vec_flag(flag_index); Af=exp(-fitness_f(rand_leader_index)/(fitness_f(i)+eps));%eq.(9) Xnewf(i,:)=X_randf(j)+Flag*C2*Af*((ub-lb)*rand+lb);%eq.(8) end end else %Exploitation Phase (Food Exists) if Temp>Thresold2 %hot for i=1:Nm flag_index = floor(2*rand()+1); Flag=vec_flag(flag_index); for j=1:1:dim Xnewm(i,:)=Best_pos(j)+C3*Flag*Temp*rand*(Best_pos(j)-Xm(i,j));%eq.(10) end end for i=1:Nf flag_index = floor(2*rand()+1); Flag=vec_flag(flag_index); for j=1:1:dim Xnewf(i,:)=Best_pos(j)+Flag*C3*Temp*rand*(Best_pos(j)-Xf(i,j));%eq.(10) end end else %cold if rand>0.6 %fight for i=1:Nm for j=1:1:dim FM=exp(-(fitnessBest_f)/(fitness_m(i)+eps));%eq.(13) Xnewm(i,:)=Xm(i,j) +C3*FM*rand*(Q*Xbest_f(j)-Xm(i,j));%eq.(11) end end for i=1:Nf for j=1:1:dim FF=exp(-(fitnessBest_m)/(fitness_f(i)+eps));%eq.(14) Xnewf(i,:)=Xf(i,j)+C3*FF*rand*(Q*Xbest_m(j)-Xf(i,j));%eq.(12) end end else%mating for i=1:Nm for j=1:1:dim Mm=exp(-fitness_f(i)/(fitness_m(i)+eps));%eq.(17) Xnewm(i,:)=Xm(i,j) +C3*rand*Mm*(Q*Xf(i,j)-Xm(i,j));%eq.(15 end end for i=1:Nf for j=1:1:dim Mf=exp(-fitness_m(i)/(fitness_f(i)+eps));%eq.(18) Xnewf(i,:)=Xf(i,j) +C3*rand*Mf*(Q*Xm(i,j)-Xf(i,j));%eq.(16) end end flag_index = floor(2*rand()+1); egg=vec_flag(flag_index); if egg==1; [GYworst, gworst] = max(fitness_m); Xnewm(gworst,:)=lb+rand*(ub-lb);%eq.(19) [GYworst, gworst] = max(fitness_f); Xnewf(gworst,:)=lb+rand*(ub-lb);%eq.(20) end end end end for j=1:Nm Flag4ub=Xnewm(j,:)>ub; Flag4lb=Xnewm(j,:)<lb; Xnewm(j,:)=(Xnewm(j,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb; y = feval(fobj,Xnewm(j,:)); if y<fitness_m(j) fitness_m(j)=y; Xm(j,:)= Xnewm(j,:); end end [Ybest1,gbest1] = min(fitness_m); for j=1:Nf Flag4ub=Xnewf(j,:)>ub; Flag4lb=Xnewf(j,:)<lb; Xnewf(j,:)=(Xnewf(j,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb; y = feval(fobj,Xnewf(j,:)); if y<fitness_f(j) fitness_f(j)=y; Xf(j,:)= Xnewf(j,:); end end [Ybest2,gbest2] = min(fitness_f); if Ybest1<fitnessBest_m Xbest_m = Xm(gbest1,:); fitnessBest_m=Ybest1; end if Ybest2<fitnessBest_f Xbest_f = Xf(gbest2,:); fitnessBest_f=Ybest2; end if Ybest1<Ybest2 curve(t)=min(Ybest1); else curve(t)=min(Ybest2); end if fitnessBest_m<fitnessBest_f GYbest=fitnessBest_m; Best_pos=Xbest_m; else GYbest=fitnessBest_f; Best_pos=Xbest_f; end end Best_score = GYbest; end