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ACONETAkademisches Computer Netz
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Proposed algorithm ACONET Pseudo code of proposed algorithm ACONET 1: Initialize all vehicles' positions randomly on the highway 2: Initialize the speed/velocity of each vehicle 3: Randomly initialize each vehicle's direction 4: Create a mesh topology among nodes/vertices where each vertex represents the vehicle id 5: Initialize same pheromone values for each edge for the above mesh topology 6: Calculate distance of each vehicle with others, normalize and associate these distance values with the corresponding edges in the above mesh topology 7: WHILE (Iteration == Total Iterations OR Stall iteration ==20) 8: { FOR [Ant.sub.i] =1 to Swarm size 9: Anti.tour ==empty and cost==infinity 10: Vertices or Nodes - Available for clustering = {All Nodes} a.
The search space of ACONET is a mesh topology based graph as described in Table 1.
For ACONET, [f.sub.1] is the delta difference value of the clusters in t and [f.sub.2] is the summation of distance values of all the CHs from their cluster members.
Pheromone values on the edges are an important learning dynamic for the ACONET. The quality of the ant tours/ trails is used to make an efficient use of the pheromone values.
In this section, different criterions to stop the execution of ACONET algorithm are discussed.
k = Average number of CHs in a solution constructed by ant The computational complexity of ACONET can be calculated for individual steps and then these can be aggregated to represent the overall complexity
To decide about a CH to be added into a solution, in the worst case, O (n) time is required for ACONET. It may please be noted that for this decision, probability calculation is performed over pre-computed values of heuristic and pheromone.
ACONET takes O(k) time to increase the amount of pheromone on the links between the 'k' cluster heads related to the solution.