蛇群算法(SO)优化长短期记忆神经网络的数据分类预测，SO-LSTM分类预测，多输入单输出模型 多特征输入单输出的二分类及多

%___________________________________________________________________% % Snake Optimizer (SO) source codes version 1.0 % % % % Developed in MATLAB R2021b % % % % Author and programmer: Fatma Hashim & Abdelazim G. Hussien % % % % e-Mail: fatma_hashim@h-eng.helwan.edu.eg % % abdelazim.hussien@liu.se % % aga08@fayoum.edu.eg % % % % % % Main paper: Fatma Hashim & Abdelazim G. Hussien % % Knowledge-based Systems % % in press, % % DOI: 10.1016/j.knosys.2022.108320 % % % %___________________________________________________________________% function [fval,Xfood,gbest_t] = SO(N,T,lb,ub,dim,fobj) %initial vec_flag=[1,-1]; Threshold=0.25; Thresold2= 0.6; C1=0.5; C2=.05; C3=2; X=initialization(N,dim,ub,lb); for i=1:N fitness(i)=feval(fobj,X(i,:)); end [GYbest, gbest] = min(fitness); Xfood = X(gbest,:); %Diving the swarm into two equal groups males and females Nm=round(N/2);%eq.(2&3) Nf=N-Nm; Xm=X(1:Nm,:); Xf=X(Nm+1:N,:); fitness_m=fitness(1:Nm); fitness_f=fitness(Nm+1:N); [fitnessBest_m, gbest1] = min(fitness_m); Xbest_m = Xm(gbest1,:); [fitnessBest_f, gbest2] = min(fitness_f); Xbest_f = Xf(gbest2,:); for t = 1:T disp(['第',num2str(t),'次迭代']) Temp=exp(-((t)/T)); %eq.(4) Q=C1*exp(((t-T)/(T)));%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,j)=X_randm(j)+Flag*C2*Am*((ub(j)-lb(j))*rand+lb(j));%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,j)=X_randf(j)+Flag*C2*Af*((ub(j)-lb(j))*rand+lb(j));%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,j)=Xfood(j)+C3*Flag*Temp*rand*(Xfood(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,j)=Xfood(j)+Flag*C3*Temp*rand*(Xfood(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,j)=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,j)=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,j)=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,j)=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 gbest_t(t)=min(Ybest1); else gbest_t(t)=min(Ybest2); end if fitnessBest_m<fitnessBest_f GYbest=fitnessBest_m; Xfood=Xbest_m; else GYbest=fitnessBest_f; Xfood=Xbest_f; end end fval = GYbest; Xfood(2)=round(Xfood(2)); end