ant colony optimisation for 01 MKP

Question

I'm trying to implement an ACO for 01MKP. My input values are from the OR-Library mknap1.txt. According to my algorithm, first I choose an item randomly. then i calculate the probabilities for all other items on the construction graph. the probability equation depends on pheremon level and the heuristic information.

p[i]=(tau[i]*n[i]/Σ(tau[i]*n[i]). 

my pheremon matrix's cells have a constant value at initial (0.2). for this reason when i try to find the next item to go, pheremon matrix is becomes ineffective because of 0.2. so, my probability function determines the next item to go, checking the heuristic information. As you know, the heuristic information equation is

n[i]=profit[i]/Ravg. 

(Ravg is the average of the resource constraints). for this reason my prob. functions chooses the item which has biggest profit value. (Lets say at first iteration my algorithm selected an item randomly which has 600 profit. then at the second iteration, chooses the 2400 profit value. But, in OR-Library, the item which has 2400 profit value causes the resource violation. Whatever I do, the second chosen is being the item which has 2400 profit.

is there anything wrong my algorithm? I hope ppl who know somethings about ACO, should help me. Thanks in advance.

Input values:

6 10 3800//no of items (n) / no of resources (m) // the optimal value
 100 600 1200 2400 500 2000//profits of items (6)
 8 12 13 64 22 41//resource constraints matrix (m*n)
 8 12 13 75 22 41
 3 6  4  18 6  4
 5 10 8  32 6  12
 5 13 8  42 6  20
 5 13 8  48 6  20
 0 0  0  0  8  0
 3 0  4  0  8  0
 3 2  4  0  8  4
 3 2  4  8  8  4
 80 96 20 36 44 48 10 18 22 24//resource capacities.

My algorithm:

for i=0 to max_ant
 for j=0; to item_number
 if j==0
 {
  item=rand()%n
  ant[i].value+=profit[item]
  ant[i].visited[j]=item
 }
 else
 {
  calculate probabilities for all the other items in P[0..n]
  find the biggest P value.
  item=biggest P's item.
  check if it is in visited list
  check if it causes resource constraint.
  if everthing is ok:
   ant[i].value+=profit[item]
   ant[i].visited[j]=item
  }//end of else
 }//next j
 update pheremon matrix => tau[a][b]=rou*tau[a][b]+deltaTou
}//next i