clear all;
clc;
%common parameter setting
N=3;
M=50;
F=0.5;
C=0.9;
I_max=200;
Run=1;
X_max=[10,10,10];
X_min=[0,0,0];
Func=@optim_pid;
%2.The whole test loop
for r=1:Run
iter=0;
for m=1:M
for n=1:N
X(m,n)=X_min(n)+rand()*(X_max(n)-X_min(n));
end
end
for i=1:I_max %stop when the iteration lrge than the max
iter=iter+1;
for m=1:M %for each individual mutattion
[V]=rand1(X,M,F,m);
% check the element in the V matrix beyond the boundary
for n=1:N
if V(1,n)>X_max(1,n)
V(1,n)=X_max(1,n);
end
if V(1,n)<X_min(1,n)
V(1,n)=X_min(1,n);
end
end
% crossover put the result in the u matrix
jrand=floor(rand()*N+1);
for n=1:N
R1=rand();
if (R1<C || n==jrand)
U(1,n)=V(1,n);
else
U(1,n)=X(m,n);
end
end
%selection
if Func(U(1,:))>Func(X(m,:))
Tr=U(1,:);
else
Tr=X(m,:);
end
X(m,:)=Tr;%use the selection result to replace the m row
Y(m,1)=Func(X(m,:));%evaluate each individuals fitness value and put the result
end %
%select the lowest fitness value
[y,ind1]=sort(Y,1);
Y_min=y(1,1);
[Ymin,ind]=min(Y);
%plot the picture of iteration
figure(2);
plot(iter,Ymin,'r.');
xlabel('iteration');
ylabel('Fitness');
title(sprintf('iteration=%d,Fitness=%9.9f',i,Ymin));
grid on;
hold on;
end %Finish I_max times iteration
hold off;
Plot(R);
hold on;
scatter3(X(ind,1),X(ind,1),Ymin,'fill','ro');
end %run 30 times
