Index exceeds matrix dimensions.

Dear sirs

i have this error with PSO code below any one can help me

the error is

Index exceeds matrix dimensions.

Error in ofun (line 92)

f=alph1*(of)^2+alph2*(DT-beta(2)*(DT- abs(DT)))^2; % fitness function

Error in Untitled4 (line 32)

f0{i}=ofun(x0(i,:));

the code is

%Save the following codes in MATLAB script file (*.m) and save as ofun.m.

%----------------------------------------------------------------------------------------------------------------------------------start

function f=ofun(x) % objective function (minimization)

of =2.633*x(1)+2.992*x(2)+3.134*x(3)+3.678*x(4)+3.620*x(5)+2.948*x(6)+1.607*x(7)+2.952*x(8)+3.348*x(9)+3.680*x(10)+3.774*x(11)+2.995*x(12)+3.237*x(13)+1.608*x(14);

% if there is no constraints then comments all c0 lines below

c =[];

CTI =0.3;

A(1) =2.633*x(1); B(1) =4.112*x(6); %first row

A(2) =2.992*x(2); B(2) =4.870*x(1); %second row

A(3) =2.992*x(2); B(3) =2.178*x(7); %third row

A(4) =3.134*x(3); B(4) =3.857*x(2); %fourth row

A(5) =3.678*x(4); B(5) =3.989*x(3); %fifth row

A(6) =3.620*x(5); B(6) =4.979*x(4); %sixth row

A(7) =2.948*x(6); B(7) =5.756*x(5); %seventh row

A(8) =1.607*x(7); B(8) =5.826*x(5); %eighth row

A(9) =2.948*x(6); B(9) =2.150*x(14); %nighth row

A(10)=1.607*x(7); B(10)=6.920*x(13); %tenth row

A(11)=2.952*x(8); B(11)=2.144*x(7); %eleventh row

A(12)=2.952*x(8); B(12)=5.365*x(9); %tweleventh row

A(13)=3.348*x(9); B(13)=4.863*x(10); %thirteenth row

A(14)=3.680*x(10); B(14)=5.073*x(11); %fourteenth row

A(15)=3.774*x(11); B(15)=3.774*x(12); %fiveteenth row

A(16)=2.995*x(12); B(16)=6.920*x(13); %sixteenth row

A(17)=2.995*x(12); B(17)=2.151*x(14); %seventennth row

A(18)=3.237*x(13); B(18)=4.288*x(8); %eighteenth row

A(19)=1.608*x(14); B(19)=4.870*x(1); %nighnteenth row

A(20)=1.608*x(14); B(20)=5.365*x(9); % twenty row

for I = 1:length (A)

for J = 1:length (B)

DT(I,J) = B(J)-A(I)-CTI;

if DT(I,J)>=0

D(I,J)=0;

else

D(I,J)= B(J)-A(I)+CTI;

end

end

end

c0(1)= B(1) -A(1)-CTI;

c0(2)= B(2) -A(2)-CTI;

c0(3)= B(3) -A(3)-CTI;

c0(4)= B(4) -A(4)-CTI;

c0(5)= B(5) -A(5)-CTI;

c0(6)= B(6) -A(6)-CTI;

c0(7)= B(7) -A(7)-CTI;

c0(8)= B(8) -A(8)-CTI;

c0(9)= B(9) -A(9)-CTI;

c0(10)= B(10)-A(10)-CTI;

c0(11)= B(11)-A(11)-CTI;

c0(12)= B(12)-A(12)-CTI;

c0(13)= B(13)-A(13)-CTI;

c0(14)= B(14)-A(14)-CTI;

c0(15)= B(15)-A(15)-CTI;

c0(16)= B(16)-A(16)-CTI;

c0(17)= B(17)-A(17)-CTI;

c0(18)= B(18)-A(18)-CTI;

c0(19)= B(19)-A(19)-CTI;

c0(20)= B(20)-A(20)-CTI;

for d = 1: length(c0)

if c0(d)>=0

c(d)=0;

else

c(d)=1;

end

end

alph1=1;

alph2=2;

beta = 100;

f=alph1*(of)^2+alph2*(DT-beta(2)*(DT- abs(DT)))^2; % fitness function

%---------------------------------------------------------------------------------------------end

%---------------------------------------------------------------------------------------------------------------------------------start

tic

clc

clear all

close all

rng default

LB=[0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05]; % lower bounds of variables

UB=[1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1]; % upper bounds of variables

% pso parameters values

m=14; % number of variables

n=100; % population size

wmax=0.9; % inertia weight

wmin=0.4; % inertia weight

c1=2; % acceleration factor

c2=2; % acceleration factor

% pso main program----------------------------------------------------start

maxite=500; % set maximum number of iteration

maxrun=10; % set maximum number of runs need to be

for run=1:maxrun

run

% pso initialization----------------------------------------------start

for i=1:n

for j=1:m

x0(i,j)=round(LB(j)+rand()*(UB(j)-LB(j)));

end

end

x=x0; % initial population

v=0.1*x0; % initial velocity

f0=cell(1,n); %preallocation

for i=1:n

f0{i}=ofun(x0(i,:));

end

[fmin0,index0]=min(f0{i});

pbest=x0; % initial pbest

gbest=x0(index0,:); % initial gbest

% pso initialization-----------------------------------------------end

% pso algorithm---------------------------------------------------start

ite=1;

tolerance=1;

while ite<=maxite

tolerance>1e-12

w=wmax-(wmax-wmin)*ite/maxite; % update inertial weight

% pso velocity updates

for i=1:n

for j=1:m

v(i,j)=w*v(i,j)+c1*rand()*(pbest(i,j)-x(i,j))...

+c2*rand()*(gbest(1,j)-x(i,j));

end

end

% pso position update

for i=1:n

for j=1:m

x(i,j)=x(i,j)+v(i,j);

end

end

% handling boundary violations

for i=1:n

for j=1:m

if x(i,j)<LB(j)

x(i,j)=LB(j);

elseif x(i,j)>UB(j)

x(i,j)=UB(j);

end

end

end

% evaluating fitness

f=cell(1,50); %preallocation

for i=1:n

f{i}=ofun(x(i,:));

end

% updating pbest and fitness

for i=1:n

if f{i}<f0{i}

pbest(i,:)=x(i,:);

f0{i}=f{i};

end

end

[fmin,index]=min(f0{i});

% finding out the best particle

ffmin(ite,run)=fmin(1); % storing best fitness

ffite(run)=ite; % storing iteration count

% updating gbest and best fitness

if fmin<fmin0

gbest=pbest(index,:);

fmin0=fmin;

end

% calculating tolerance

if ite>100;

tolerance=abs(ffmin(ite-100,run)-fmin0);

end

% displaying iterative results

if ite==1

fprintf('Iteration Best particle Objective fun\n');

end

fprintf('%8g %8g %8.4f\n',ite,index,fmin0);

ite=ite+1;

end

% pso algorithm---------------------------------------------------end

gbest

fvalue=2.633*x(1)+2.992*x(2)+3.134*x(3)+3.678*x(4)+3.620*x(5)+2.948*x(6)+1.607*x(7)+2.952*x(8)+3.348*x(9)+3.680*x(10)+3.774*x(11)+2.995*x(12)+3.237*x(13)+1.608*x(14);

fff(run)=fvalue

rgbest(run,:)=gbest;

fprintf('--------------------------------------\n');

end

% pso main program------------------------------------------------------end

fprintf('\n\n');

fprintf('*********************************************************\n');

fprintf('Final Results-----------------------------\n');

[bestfun,bestrun]=min(fff)

best_variables=rgbest(bestrun,:)

fprintf('*********************************************************\n');

toc

% PSO convergence characteristic

plot(ffmin(1:ffite(bestrun),bestrun),'-k');

xlabel('Iteration');

ylabel('Fitness function value');

title('PSO convergence characteristic')

% checking for coordination time interval (CTI)

CTI1 = -2.633*best_variables(1) + 4.112*best_variables(6)

CTI2 = +4.870*best_variables(1) - 2.992*best_variables(2)

CTI3 = -2.992*best_variables(2) + 2.178*best_variables(7)

CTI4 = +3.857*best_variables(2) - 3.134*best_variables(3)

CTI5 = +3.989*best_variables(3) - 3.678*best_variables(4)

CTI6 = +4.979*best_variables(4) - 3.620*best_variables(5)

CTI7 = +5.756*best_variables(5) - 2.948*best_variables(6)

CTI8 = +5.826*best_variables(5) - 1.607*best_variables(7)

CTI9 = -2.948*best_variables(6) + 2.150*best_variables(14)

CTI10= -1.607*best_variables(7) + 6.920*best_variables(13)

CTI11= +2.144*best_variables(7) - 2.952*best_variables(8)

CTI12= -2.952*best_variables(8) + 5.365*best_variables(9)

CTI13= -3.348*best_variables(9) + 4.863*best_variables(10)

CTI14= -3.680*best_variables(10)+ 5.073*best_variables(11)

CTI15= -3.774*best_variables(11)+ 3.774*best_variables(12)

CTI16= -2.995*best_variables(12)+ 6.920*best_variables(13)

CTI17= -2.995*best_variables(12)+ 2.151*best_variables(14)

CTI18= +4.288*best_variables(8) - 3.237*best_variables(13)

CTI19= +4.870*best_variables(1) - 1.608*best_variables(14)

CTI20= +5.365*best_variables(9) - 1.608*best_variables(14)

%##########################################--------------------------end

Answer 1

[tahseen alshmary]

Any one know where is the problem? please