MATLAB Answers

how can i make it to loop of an iteration?

i have this coding, i want to have make a loop of for 10 iteration.. the curent coding didnt give me 10 iteration..

i want to have 10 possible solution however its only show 1 solution. below is the coding. what can i do to make it generate 10 solution

%read data
clc
clear
%global f d;
%flow
f=[  0	5	2	4	1
5	0	3	0	2
2	3	0	0	0
4	0	0	0	5
1	2	0	5	0];    
%distance
d=[ 0	1	2	3	1
1	0	1	2	2
2	1	0	1	3
3	2	1	0	4
1	2	3	4	0];
[r,c]=size(f); 
max_i= r;
max_j =r;
max_k= r;
max_q= r;
A=zeros(r,r);
ID=randperm(r);
MaxIt=10;
x=zeros(r,r); 
n=length(ID);
x1=ID;
for i=1:n
  for j=1:r
    x(j,x1(j))=1;
    B(i).mat=x;  %store permutationof binary value, 0,1  
  end
  x=zeros(r,r);  
end
for i=MaxIt
  for no=1:length(B)
    z=0;
    xa=B(no).mat;
    for i=1:max_i
      for j=1:max_j
        for k=1:max_k           
          for q=1:max_q               
            z= z+ f(i,k).*d(j,q).*xa(i,j).*xa(k,q);
          end           
        end
      end
    end
  end
  no;
  F(no,:)=[no z];
end
zmin=min(F(:,2));
ii=find(F(:,2)==zmin) ;
x_initial = B(ii).mat;
xbin= B(ii).mat
z_minimum = zmin
%disp(B(ii).mat)
%fprintf (B(no).mat);
%disp(z)

  10 Comments

please dont be sorry, im feel very grateful for ur reply, actually my intention to solve the Quadratic Assignment Problem using GA, this is the 1st step which is to fine the initial population, which is the set of solution.. however the coding didnt show set of solution, it only show 1 solution.. are my coding itself is wrong?

Quadratic Assignment Problem using GA

I'm not familiar with the topic above, do you have pseudocode for it? or any reference for me to see? Where do you find reference to write your code?

Perhaps they will help me to understand more.

i have the step

begin
   create initial population;
   for every generation, repeat 
      randomly select individuals into the mating pool;
      apply genetic operators to generate offspring;
      (maybe) apply post-crossover heuristic on offspring;
       if not in i×Tc generations
         compare every offspring with its similar parent and
          remove the worse one;
        else
        compare every offspring with the worst member in
        the current population and remove the worse one
    until some stopping criterion is met
 end;

http://iopscience.iop.org/article/10.1088/1757-899X/300/1/012002 this is the paper that i write for conference.. i want to solve the problem using genetic algorithm

the reference is from one f my lecturer in my university... i find one coding that solve the same problem using the same method, however i need to change here and there, and its become difficult..that is why i want to build my own..however i find a difficulties also..

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