%___________________________________________________________________%
% 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 [Xfood, fval,gbest_t] = SO(N,T,fobj, dim,lb,ub)
%initial
vec_flag=[1,-1];
Threshold=0.25;
Thresold2= 0.6;
C1=0.5;
C2=.05;
C3=2;
X=lb+rand(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
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-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,j)=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,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;
end
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