clear
close all
Npar = 10;
VarLow=ones(1,10)*0.1;
VarHigh =ones(1,10)*33.5;
N=50;
MaxIter=100;
XBest = rand(1,Npar).* (VarHigh - VarLow) + VarLow;
[FBest]=fitnessFunc(XBest);
GB=FBest;
t = cputime;
X = zeros(N, Npar);
F = zeros(N, 1);
for ii = 1:N
X(ii,:) = rand(1,Npar).* (VarHigh - VarLow) + VarLow;
F(ii) = fitnessFunc(X(ii,:));
end
for it=1:MaxIter
num=zeros(1,Npar);
for ii=1:N
for jj=1:Npar
num(jj)=num(jj)+(X(ii,jj)/F(ii));
end
end
den=sum(1./F);
Xc=num/den;
for ii=1:N
for jj=1:Npar
New=X(ii,:);
New(jj)=Xc(jj)+((VarHigh(jj)*rand)/it^2);
end
NewP=limiter(New,VarHigh,VarLow);
newFit=fitnessFunc(New);
if newFit<F(ii)
X(ii,:)=New;
F(ii)=newFit;
if F(ii)<FBest
XBest=X(ii,:);
FBest=F(ii);
end
end
end
GB=[GB FBest];
end
t1=cputime;
fprintf('The time taken is %3.2f seconds \n',t1-t);
fprintf('The best value is :');
XBest
FBest
figure(1)
plot(0:MaxIter,GB, 'linewidth',1.2);
title('Convergence');
xlabel('Iterations');
ylabel('Objective Function (Cost)');
grid('on')
function newP=limiter(P,VarHigh,VarLow)
newP=P;
for i=1:length(P)
if newP(i)>VarHigh
newP(i)=VarHigh;
elseif newP(i)<VarLow
newP(i)=VarLow;
end
end
end
function [WE,FBest]=fitnessFunc(X)
Coord=360*[2 1;2 0;1 1;1 0;0 1;0 0];
Con=[5 3;1 3;6 4;4 2;3 4;1 2;6 3;5 4;4 1;3 2];
Re=[0 0;0 0;0 0;0 0;1 1;1 1 ];
Load=zeros(size(Coord));
Load(2,:)=[0 -1e5];
Load(4,:)=[0 -1e5];
E=ones(1,size(Con,1))*1e7;
A=ones(1,10);
TM=25000;
DM=2;
RO=0.1;
w=size(Re);
S=zeros(3*w(2));
U=1-Re;f=find(U);
WE=0;
for i=1:size(Con,2)
H=Con(:,i);
C=Coord(:,H(2))-Coord(:,H(1)); *********problem here
Le=norm(C);
T=C/Le;
s=T*T';
G=E(i)*A(i)/Le;
Tj(:,i)=G*T;
e=[3*H(1)-2:3*H(1),3*H(2)-2:3*H(2)];
S(e,e)=S(e,e)+G*[s -s;-s s];
WE=WE+Le*D.A(i)*D.RO;
end
U(f)=S(f,f)\Load(f);
F=sum(Tj.*(U(:,Con(2,:))-U(:,Con(1,:))));
R=reshape(S*U(:),w);
R(f)=0;
TS=(((abs(F'))./A)/TM)-1;
US=abs(U')/DM-1;
PS=sum(TS.*(TS>0));
PD=sum(sum(US.*(US>0)));
FBest=WE*(1+PS+PD)^2;
end
