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The Antnet Routing Algorithm - A Modified Version. Firat Tekiner , Z. Ghassemlooy Optical Communications Research Group, The University of Northumbria, Newcastle upon Tyne S. Alkhayatt School of Computing Science, Sheffield Hallam University CSNDSP 20-22 July 2004. Contents.

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the antnet routing algorithm a modified version

The Antnet Routing Algorithm - A Modified Version

Firat Tekiner, Z. Ghassemlooy

Optical Communications Research Group, The University of Northumbria, Newcastle upon Tyne

S. Alkhayatt

School of Computing Science, Sheffield Hallam University

CSNDSP 20-22 July 2004

  • Background Information
  • Ant Colony Optimisation
  • Agent Based Routing Algorithms
  • Antnet routing algorithm
  • Improvements proposed
  • Simulation Environment and Results
  • Concluding Remarks
aims objectives
Aims & Objectives

Designing a routing algorithm:

  • Scalable
  • Distributed
  • Intellegent
  • Self - Organising
  • Fault Tolerant
  • Generic: Network and Machine Independent

“In internetworking, the process of moving a packet of data from source to destination.”

A routing algorithm is necessary to find the optimal path (or the shortest path) from source to destination.


  • Existing algorithms are mostly Table-Based (high cost)
  • Congestion and contention (requires traffic distribution)
  • Requires human intelligence
  • The routing algorithms that are in use are all static algorithms
  • Q-Learning
    • Q-routing (Boyan et al, 94) (Tekiner et al., 04)
    • Dual reinforcement Q-routing(Kumar et al., 97 & 01)
  • Ant (software agent) based Routing Algorithms
    • ABC routing (Schoonderwoerd et al., 96)
    • Regular and Uniform ant routing (Subramanian et al., 97)
    • Antnet (Dorigo et al., 98)
    • Antnet++ (Dorigo et al., 02)
    • Improved Antnet (Boyan et al., 02)
    • Antnet with evaporation(Tekiner et al. 2, 04)
  • Agent Distance Vector Routing (ADVR)(Amin et al., 01 & 02)
comparison of algorithms
Comparison of Algorithms
  • Antnet uses probabilistic routing tables whereas in Link State and Distance Vector routing table entries are deterministic
  • Ants use less resources on the nodes
  • Ants are dynamic and self organising whereas Distance Vector and Link State algorithms require human supervision
  • Q-Routing does not guarantee on finding the shortest path always. Moreover, they can only find a single path, they cannot explore multiple paths
  • In antnet stagnation is the main problem (routing table freezes due to selecting same path)
ants in nature unsophisticated and simple
Ants In Nature -“unsophisticated and simple”
  • Builds and protects their nests
  • Sorts brood and food items
  • Explore particular areas for food, and preferentially exploits the richest available food source
  • Cooperates in carrying large items
  • Migrates as colonies
  • Leaves pheromones on their way back
  • Stores information in the nature (uses world as a memory)
  • Make decision in a stochastic way
  • Always finds the shortest paths to their nests or food source
  • Are blind, can not foresee future, and has very limited memory
ants how do they find their way
Ants – How do they Find Their Way?
  • Ants don’t know where to go initially, and choose paths randomly
  • Ants taking the “shorter path” will reach the destinations before the those taking a long route. The path is marked with pheromone.
  • There after the number of ants using the shorter path will keep increasing, since more pheromone is laid on the path.
antnet in detail
Antnet in Detail

Positive reinforcement:

Negative reinforcement:

three improvements
Three Improvements

A. Deleting aged packets



B. Limiting the effect of r

if(NO_OF_NODES <= 5)

0.1 < r < (1 – 0.1 * NO_OF_NODES)

else/* if (NO_OF_NODES > 5) */

0.05 < r < (1– 0.05 * NO_OF_NODES)

C. Limiting the number of Ants in the system

simulation parameters
Simulation Parameters
  • Poisson traffic distribution, with three different system loads low, medium and high
  • 5000 packets created per node
  • Average of 8 simulation runs is used for accuracy
  • No packet loss due to node/link failures
  • All experiments are implemented for varying ant creation rates, since it has a significant effect on the performance of the algorithm
results 1
Results 1

Ant rate vs. avg. delay

results 2
Results 2

Ant rate vs. the throughput

concluding remarks
Concluding Remarks
  • Detecting and removing aged packets improved networks performance
  • Boundaries introduce reduces the effect of the traffic fluctuations on the solution
  • No mathematical formula only constant variables are used
  • There is a need for a second heruistic to optimise antnet’s parameters
  • Stagnation is a major problem but solution does exists
current and future work
Current and Future Work
  • Current Work:

Stagnation problem is currently being investigated in different traffic models and network configurations.

    • Evaporation: ~7% improvement in the performance of the algorithm [Tekiner et al. 2, SoftCOM04]
    • Multiple Ant Colonies
    • Aging, and Noise
  • Future Work:
    • Hybrid Algorithm: Distributed GA could be embedded in the proposed model [Tekiner et al., seminar 2]
    • Together with hybrid GA all constant variables used needs to be dynamic (currently static variables used).

Thanks to my sponsor

Northumbria University

Any Questions?