NetLogo User Community Models
## WHAT IS IT?
Routing is a critical issue in the Delay Tolerant Network as finding the most optimized path, with minimum loss of data and in the shortest span of time is a challenge. Various
## HOW IT WORKS
Ant colony Optimization (ACO) has a chiefly studied drawback of converging into local optima and the proposed enhancement is intended at overcoming this prevailing disadvantage in ACO. Numerous research in the past has been devoted at achieving so . In the real scenario, ants travel vast areas without global view of the ground. No central processor exists to lead the ACO towards good paths. Convergence is certain, but time to converge is indeterminate. Taking primarily this shortcoming in account, we have tried to integrate particle swarm optimization characteristics to improve the traditional ACO. Previously global information has been employed in the form of global pheromone updating rule, to place or deposit extra pheromone to predispose the exploration procedure from a non-local viewpoint . Our modification to ACO is motivated by both study into diverse forms of ant colonies systems and their distinction with other meta-heuristic practices. In particle swarm optimization, each particle denotes a contestant solution and these particles are moved around in the search-space according to simple computed formulations over location and velocity of the particle. Each particle's movement is not subjective towards its local best known position but, also inclined towards the best discovered positions in the search-space, which are improved as better positions are located by other particles. This is expected to move the swarm toward the best solutions. Here we define a probability global-opt-factor, which correlates to the chance that a packet will follow best path discovered so far but only if the last best set path has not been deformed to change in nodes position and present node of the packet exists in the best-path.(further explained in the algorithm). In DTNs, the routing technique must adapt to topological modifications (e.g., link/node failures, link/node addition/removal). Therefore the convergence of the algorithm must be rapid and shortest routing paths may sustainably change over time. Our modification speeds up the convergence and accounts for global best as an update to each ant’s solution depending upon the global-opt-factor.
## HOW TO USE IT
The "num-of-nodes" slider determines the numberof the nodes in the network.
## THINGS TO NOTICE
When we set the congestion factor to a low value=0.5, we observe that
## THINGS TO TRY
## CREDITS AND REFERENCES
Bhatia, Abhishek, and Rahul Johari. "Genetically optimized ACO inspired PSO algorithm for DTNs." Reliability, Infocom Technologies and Optimization (ICRITO)(Trends and Future Directions), 2014 3rd International Conference on. IEEE, 2014.
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