% 2023
% 
% Project Title: Enhanced meta-heuristic optimization of resource 
% efficiency in multi-relay underground wireless sensor networks

% Developer: Mariem Ayedi
% 
% Contact Info: m.ayedi@psau.edu.sa
%



clear all
close all
clc
format short


teb=[];vv=[];
tebii=[];
tebconv=[];
teb_ee=[];teb_se=[];
teb_re=[];tebconv_ee=[];
tebconv_se=[];
tebconv_re=[];
teb_batEr=[];
teb_batEs=[];
teb_relay=[];

teb_Er=[];
teb_Es=[];


tabSNRdB=[ 15]; 
N=20;
iteratio=1000;
N0vect=[10^-12];
pu=0.0500 ;  % 0.1000    0.1500    0.2000    0.2500 ];
gamma=[ 0.125]; %0.015 0.02 0.025 0.03 0.035 0.04]% 0.1000    0.1500    0.2000    0.2500 ];
% 0.05 - 0.5
nbr=5;
bd=[300*10^6 ];

for d=1:length(pu)
d
som_rate=0;
som_teb=0;

som_rate_ee=0;
som_rate_Es=0;
som_rate_Er=0;

som_rate_se=0;
som_rate_re=0;

som_bat_Es=0;
som_bat_Er=0;

conv_ee=zeros(1,10);
conv_se=zeros(1,10);
conv_re=zeros(1,10);

ii=0;
som_BestCost=0;
for it=1:1:iteratio
    
EiniS=3;   
EiniR1=3;
EiniR2=3;
EiniR3=3;
EiniR4=3;
EiniR5=3;
while EiniS>0.005 && EiniR1 > 0.005 && EiniR2 > 0.005 && EiniR3 > 0.005 && EiniR4 > 0.005 && EiniR5 > 0.005
%for ii=1:2
    ii=ii+1;
    
N0=N0vect;

Lsr1_db=  36.3154;

Lr1d_db=  51.6983 ;

Lsr2_db=  34.7410;

Lr2d_db=  50.8535;

Lsr3_db=  30.3591 ;

Lr3d_db= 50.1112 ;

Lsr4_db= 28.7797;
 
Lr4d_db= 49.5239;

Lsr5_db =28.7797;

Lr5d_db = 49.1443;
 
Lsr1=10^(-Lsr1_db/10);

Lr1d=10^(-Lr1d_db/10);

Lsr2=10^(-Lsr2_db/10);

Lr2d=10^(-Lr2d_db/10);

Lsr3=10^(-Lsr3_db/10);

Lr3d=10^(-Lr3d_db/10);

Lsr4=10^(-Lsr4_db/10);

Lr4d=10^(-Lr4d_db/10);

Lsr5=10^(-Lsr5_db/10);

Lr5d=10^(-Lr5d_db/10);

hsr1= sqrt(Lsr1/2)*(randn(1)+1i*randn(1));

hsr2= sqrt(Lsr2/2)*(randn(1)+1i*randn(1));

hsr3= sqrt(Lsr3/2)*(randn(1)+1i*randn(1));

hsr4= sqrt(Lsr4/2)*(randn(1)+1i*randn(1));

hsr5= sqrt(Lsr5/2)*(randn(1)+1i*randn(1));


hr1d= sqrt(Lr1d/2)*(randn(1)+1i*randn(1)); 

hr2d= sqrt(Lr2d/2)*(randn(1)+1i*randn(1)); 

hr3d= sqrt(Lr3d/2)*(randn(1)+1i*randn(1)); 

hr4d= sqrt(Lr4d/2)*(randn(1)+1i*randn(1)); 

hr5d= sqrt(Lr5d/2)*(randn(1)+1i*randn(1));

B=bd;
emax=pu(d);

beta=2.1;
ub =[emax;emax;emax;emax;emax;emax];
lb=[0.005 ; 0.005; 0.005 ; 0.005; 0.005 ; 0.005];
dim=6;

Max_iter=10;


snr1= @(x) ((1/(x(1)*abs(hsr1)^2+N0))*x(1)*x(2)*abs(hsr1)^2*abs(hr1d)^2) / ( B*N0*((1/(x(1)*abs(hsr1)^2+N0))*x(2)*abs(hr1d)^2 +1) );
snr2= @(x) ((1/(x(1)*abs(hsr2)^2+N0))*x(1)*x(3)*abs(hsr2)^2*abs(hr2d)^2) / ( B*N0*((1/(x(1)*abs(hsr2)^2+N0))*x(3)*abs(hr2d)^2 +1) );
snr3= @(x) ((1/(x(1)*abs(hsr3)^2+N0))*x(1)*x(4)*abs(hsr3)^2*abs(hr3d)^2) / ( B*N0*((1/(x(1)*abs(hsr3)^2+N0))*x(4)*abs(hr3d)^2 +1) );
snr4= @(x) ((1/(x(1)*abs(hsr4)^2+N0))*x(1)*x(5)*abs(hsr4)^2*abs(hr4d)^2) / ( B*N0*((1/(x(1)*abs(hsr4)^2+N0))*x(5)*abs(hr4d)^2 +1) );
snr5= @(x) ((1/(x(1)*abs(hsr5)^2+N0))*x(1)*x(6)*abs(hsr5)^2*abs(hr5d)^2) / ( B*N0*((1/(x(1)*abs(hsr5)^2+N0))*x(6)*abs(hr5d)^2 +1) );

ee= @(x) B * log2( 1+ snr1(x) +snr2(x) + snr3(x)+ snr4(x)  + snr5(x) ) / (x(1)+ x(2)+ x(3)+ x(4)+ x(5)+ x(6));
se= @(x) log2( 1+  snr1(x) +snr2(x) + snr3(x)+ snr4(x)+ snr5(x) ) ;
re= @(x) ee(x)+  beta*(B/(6*emax))*se(x);


z=re;
HCSSC;
%SSAorig;
som_rate_re=som_rate_re+FoodFitness;  
conv_re=conv_re+Convergence_curve;

EiniS= EiniS- FoodPosition(1) ;   
EiniR1= EiniR1- FoodPosition(2);
EiniR2= EiniR2- FoodPosition(3);
EiniR3= EiniR3- FoodPosition(4);
EiniR4= EiniR4- FoodPosition(5);
EiniR5= EiniR5- FoodPosition(6);


ub =emax;
lb=0.005;
% WOA;
% som_rate_re=som_rate_re+Leader_score;
% EiniS= EiniS- Leader_pos(1) ;
% EiniR1= EiniR1- Leader_pos(2);
% EiniR2= EiniR2- Leader_pos(3);
% EiniR3= EiniR3- Leader_pos(4);
% EiniR4= EiniR4- Leader_pos(5);
% EiniR5= EiniR5- Leader_pos(6);

% abc
% som_rate_re=som_rate_re+BestSol.Cost;
% EiniS= EiniS- BestSol.Position(1) ;   
% EiniR1= EiniR1- BestSol.Position(2);
% EiniR2= EiniR2- BestSol.Position(3);
% EiniR3= EiniR3- BestSol.Position(4);
% EiniR4= EiniR4- BestSol.Position(5);
% EiniR5= EiniR5- BestSol.Position(6);


% DA;
% som_rate_re=som_rate_re+Best_score;
% EiniS= EiniS- Best_pos(1) ;   
% EiniR1= EiniR1- Best_pos(2);
% EiniR2= EiniR2- Best_pos(3);
% EiniR3= EiniR3- Best_pos(4);
% EiniR4= EiniR4- Best_pos(5);
% EiniR5= EiniR5- Best_pos(6);

% 
% pso;
% som_rate_re=som_rate_re+GlobalBest.Cost;  % maximum of ee
% %conv_re=conv_re+BestCost;
% EiniS= EiniS- GlobalBest.Position(1) ;   
% EiniR1= EiniR1- GlobalBest.Position(2);
% EiniR2= EiniR2- GlobalBest.Position(3);
% EiniR3= EiniR3- GlobalBest.Position(4);
% EiniR4= EiniR4- GlobalBest.Position(5);
% EiniR5= EiniR5- GlobalBest.Position(6);

%som_bat_Es= som_bat_Es+ EiniS;
%som_bat_Er= som_bat_Er+  EiniR1+ EiniR2+ EiniR3+ EiniR4+ EiniR5;

end

%v(it)=som_rate_re;
end
avg_conv_ee=conv_ee/iteratio;
avg_conv_se=conv_se/iteratio;
avg_conv_re=conv_re/iteratio;
avg_ii=ii/iteratio;
  


avg_rate_re=som_rate_re/iteratio;
teb_re=[teb_re,avg_rate_re]; 
tebii=[tebii,avg_ii]; 


tebconv_ee=[tebconv_ee,avg_conv_ee];
tebconv_se=[tebconv_se,avg_conv_se];
tebconv_re=[tebconv_re,avg_conv_re];



end 
tebconv_re
teb_re